#sometimes its less becoming better. and more being horrible has some uses you can funnel in more fun and less destructive ways
Text
Hey look its the worst character ever
#*shakes her like a dog toy*#ocs#my ocs#oc art#original character#art#character design#i dont even mean 'shes the worst' in a cutesy way#'called someone a slur' kind of worst#heehee i hope you all get to hate her someday#sometimes its less becoming better. and more being horrible has some uses you can funnel in more fun and less destructive ways#not compelling like having motivations and reasons but compelling like watching maggots in a corpse is compelling#surprisingly the eye thing is plot relevant#but lets be real i drew it cuz it was cool like 5 years ago and then realised the delicious implications#i put an image description in i hope its good cuz i didnt trust my handwriting#tumblr can handle a little treasur trail yes?
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Gender Thoughts Pt 1 and 2
The first time I put a binder on, a little under a week ago, I felt euphoric. Ever since I hit puberty very early on, I felt uncomfortable with my breasts. They never felt right on me, and even though I’ve come to love them sometimes, they still don’t always feel like they match up. I hated how people always looked at them, pointed out how much they showed in low cut shirts when I never even noticed they were--or even wanted them to. They were just there. I liked the way low cut shirts feel and look on me, I just can’t help these giant sacks of flesh that sit on my chest.
Except...now I can! I ran my hands over my smooth chest, feeling bright. I looked into the mirror, and felt something warm wash over me. I put on my new masculine clothes, letting my partner clip on my new suspenders. I realized that I was shaking as I looked at myself again… I looked like a boy. I felt like a boy. Like a man. And I liked it. I wanted it. Admitting that to myself was like coming home.
I remember being in sixth grade, walking around the track for my civil air patrol class. I had been slotted in with the rest of the girls, the boys walking ahead of us. I remember feeling uncomfortable being shoved in with only girls, and looking at the gaggle of boys ahead. The exact thought that whispered in my brain was “I wish I was a boy. I want to be like them, with them.” I never forgot that moment, and how strange it made me feel. How it was easier to shake that thought away, and dismiss those feelings. Except they never really left, did they?
I remember sitting on my bed, crying with my best friend kneeling in front of me. I remember telling her how I didn’t like feeling like a woman all the time. That I wished I could be a black shadow, monstrous, androdynous. Specifically like Venom. She took my hand, did my makeup all in black and helped me pick out the perfect black outfit to achieve that dark, gothic look. I was so incredibly happy and validated. But I still felt like something was missing.
I remember going into an Adam and Eve for laugh, not expecting much since I am an asexual with a low libido. I remember seeing packers and feeling my chest tighten. I never liked my genitalia--I had wished for a cloaca or something akin to that, but since that was biologically impossible for a human… I sometimes wished I had the opposite of a vagina. I frequently imagined what it would be like to have a penis. I frequently lamented the fact that I didn’t have one. I took the box up to the counter to ask some questions, my dress swishing as I went. The cashier told me it was for trans people only, and a girl like me couldn’t have it. She didn’t know what asexuality was, and had tried polyamory once but decided it was bad when her girlfriend kissed her boyfriend. I was upset, disheartened, and left the store empty handed feeling frustrated and lost.
I remember finally cutting the long, curly locks that had frustrated and imprisoned me for so long. Seeing all of my hair fall to the floor, staring into the mirror as the barber buzzed the back of my head… It made me want to cry tears of joy. It was the first time in my entire life that I had looked at my hair and was happy. The first time I could look in the mirror and feel like myself. Then I remember wanting to go shorter, and my barber encouraging me to keep it a little longer so I didn’t look manly, so I could still be soft and feminine. The way my stomach dropped and the sick feeling in my chest only increased when he began to make fun of the gay men who came down the street near his favorite restaurant. I never saw that barber again. I instead found a nice local place down the road from my apartment, where the kind lady cut it all off without question, other than “Why?” and accepted my warm “It makes me happy. It makes me feel beautiful.”
But wearing that binder for the first time? It was as if a beam of light had funneled its way directly into my heart. I felt like a handsome man, with just a little bit of striking man boob, and it felt so right. My partner called me a dashing boy and my heart began to race. I still feel his hand tracing my jawline as he called me handsome, and the butterflies it sent up through my belly, even after more than eleven years.
I love my partner--he identifies as agender and primarily masculine, and has been on the lookout for a good pair of size thirteen shoes to wear with a dress. They also wear joggers and flip flops and graphic tees and can’t seem to stop talking about the ocean and outer space. They’re probably one of my biggest inspirations for finding myself, and being authentically me.
I’m not super sure who or what I am right now. I’m still figuring that out, but I’m pretty sure I’m somewhere between agender and genderfluid. I feel like me more than anything else, but all pronouns make me feel good. I feel like all of them and none of them at once, but I swing between wanting to be feminine and masculine pretty strongly, though I enjoy being masculine most of all--even when I’m wearing dresses and pink. I feel like a beautiful person in a dress or a button down, no matter what gender I feel like today or tomorrow.
I am me. And I am one dashing boy, and one beautiful girl.
4 July 2021
XXX
Since first writing this little essay, I’ve been doing a lot more examination of my gender. I have come to the conclusion that I am transmasc and nonbinary, and am shaky on the title of genderfluid. I am feeling less and less like a woman--if anything, occasionally adjacent to a woman rather than actually being one. I love feeling like and presenting as a man. I have my first appointment with a gender services doctor at my local community clinic for consultation on starting hrt testosterone. I am planning to start with low dose first, and see how I feel.
I am still unsure of my exact identity, but I have found great euphoria with being and presenting as a man. I love being a man and everything that entails. I have loved myself like never before. Being with my partner is amazing, and he has been endlessly supportive--even recounting little things they had noticed throughout the years. One of the funniest being that I only ever referred to my body parts--my belly, hands, hair, genitalia--with masculine pronouns. I always seemed to see my body as male even if I had a certain sort of dissonance from it.
Coming out has been difficult. I have had both positive and negative experiences from it. I have been told going on testosterone would be self harm, and that I can’t be something I’m not. I’ve had coworkers I trusted out me without my permission. But I have also had positive affirmation, polite questions, and discussions. I am terrified to tell my mother and her boyfriend--I have no idea how they will react and am terrified that I will be disrespected and disowned.
But I am prepared to do whatever it takes to be my happiest and most authentic self.
I have been binding a lot more often, wearing sports bras for long shifts at work, and occasionally going without either when I feel like letting my man boobs hang free. I’ve had the delightful experience of going to a men’s big and tall store and finally wearing pants. I grew up as a fat girl and felt as if I had to perform high femininity to be taken seriously and be treated well--and had been told by someone I trusted that I was too fat to wear pants, which I heavily internalized. So I had completely cast them away in favor of dresses and skirts, bows and gaudy jewelry. Realizing that I could wear pants was...totally wild. That I could be comfortable and look good in pants and shorts, and that it didn’t matter what people did or thought of me was life changing. Maybe I’ll feel like being feminine again someday, but right now this masculinity and masculine clothing, with perhaps the added spice of funky earrings, feels like home.
I also grew up autistic and with PCOS, both which I think have affected my gender identity. Being autistic, I truly struggled to connect to others socially, and especially to understand societal norms. Being a proper woman felt like I was making up for everything else I was lacking--I may have been awkward, semi-verbal and weird with no friends, but at least I was cute and girlish. I never connected to womanhood though, and always felt out of place no matter how hard I tried. With PCOS, I had heightened testosterone, which meant wider breasts and shoulders, a lack of periods, and excessive body hair. I recall the endocrinologist asking high school age me if I had excessive body hair around my stomach, breasts, etc. and my mother jumping to say no I didn’t...even though I did. I remember suddenly feeling very self aware and ashamed of something completely natural, and even something I started to enjoy. I started shaving my entire body then.
I even remember being in middle school, and thinking nothing of my hairy legs. In fact, I loved my body hair and how it felt. A rude girl began making fun of me though, tutting her tongue as she cooed, “Aw, does your mommy not let you shave?” Among other things, all throughout many years of severe bullying and abuse. I remember feeling ashamed, but not knowing why, and immediately shaving my legs, covering them in nicks from my shaky and unsteady hands, that same night.
So many things set me back in my gender expression. So many things contributed to me willful ignorance and denial. I remember wanting to be butch, and everyone in my life laughing at me and saying I was too soft for that. That sweet, sharp ache in my chest. I remember going to a salad bar with my mother, wearing a button up and telling her I wanted to wear some more boyish clothes around that same time--I had already told her that I was bi sometime earlier. I remember her lip curling, looking uncomfortable, and telling me that I better not become one of those boy girls. My late father was very vocal in denouncing homosexuality and specifically men loving men--something which always sat horribly wrong with me on a deeper level.
I think I might ending up being a trans man. I am still unsure and figuring myself out, but I struggle greatly with the autistic need for sameness vs. the trans need for change. My sapphic love of women has always been very important to me, and fully becoming a man rather than genderfluid is scary for that very reason. I am still navigating my identity and what it means to me and my reality--but no matter what, being a man, being masculine is integral to who I am.
I was called a “sir” at a job interview for the first time the other day, and nearly began to bawl from sheer joy. The gender euphoria from that and so many moments is worth so much more to me than the years of suffering and ignorance and my ongoing struggles with dysphoria. I finally got a packer and have had help from my partner in learning to position it properly--I am thinking of cutting my hair even shorter. I have almost perfected a pretty basic tie tying skill. Okay, not really, but I’m getting there. I feel deep inside that even though my father loved me, he would not like who and what I am. Still, I wear the last watch he ever wore, and hope to be a good man like him--and to learn from the toxic parts of him to be an even better man.
I am very excited to start hrt. I am terrified of hair loss and vaginal atrophy, but I look forward to so much more. I cannot wait for bottom growth and body hair, for the voice drop that will hopefully get me misgendered less. I have always felt disconnected from my voice and look forward to getting to know it better as it changes with me. I look forward to meeting with new facial hair. Working out and growing muscle. I just look forward to my second puberty and becoming more like myself. I look forward to navigating and exploring my gender even further, both with loved ones, support groups, and myself.
More than anything, I am just happy to be me.
25 August 2021
#transmasc#ftm#ftx#gender#genderqueer#transgender#trans#lgbt#lgbtq#trans man#nonbinary#genderfluid#poets of tumblr#spilled thoughts#gender expression#low dose t#hrt
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5 Lies You Tell Yourself About Your Analytics (And How to Fix It)
Consulting data is good.
But being a slave to data, is not.
There is such a thing as being too data-obsessed. Confirmation bias pops up. And you miss the good, albeit, intangible stuff that comes along with your efforts.
The solution is to uncover those biases and misunderstandings that lead you astray.
It’s not easy. Or even intuitive. But it’s the only way to avoid these five analytics blinders.
Here’s how it strikes when you least expect it.
Here’s why you fall for it.
And here’s how to avoid it by bringing in other types of feedback and analysis.
Lie #1. Your “Conversions” Are Flawless
You’ve got three AdWords campaigns.
The first brings in zero leads on $78 bucks spent.
The second brings in one at a cost of $135.31.
The third brings in two at $143.28 per lead.
Nine times out of ten, the campaign with more “conversions” is declared the winner.
But what do you really, truly, know about this scenario?
Which campaign is actually performing the best? Which is putting the most money back into your pocket?
There’s simply no way to tell at this point.
First and foremost, these “conversions” are leads — not closed customers.
Second, they might be for different products or services. So different average order values or LTVs come into play.
Third, this is nowhere close to statistical significance. For example, the third campaign has the most leads because you’ve spent the most money on it.
Not because it’s “better.”
What if you simply spend the same amount on the first two? What if you let them both get to around the same ~$150/per mark?
See what I mean?
Too many “what ifs” for my taste.
Yet this is exactly what happens inside any marketing department. The same end result pops up after each client or superior meeting.
Everyone points to the third campaign. It gets the adulation. It gets the increased budget. It gets the additional staff and resources.
So it becomes a self-fulfilling prophecy.
One solution to figure all this out is closed loop analysis.
Ideally, your goal is to match up the customer’s information (name, email, phone, credit card) to the lead data you’re seeing inside Google Analytics.
Haha — just kidding.
That would mean you were gathering Personally Identifiable Information, which is a big no-no in Google Analytics.
Do it and they’ll delete your account right away.
The simplest alternative is to just use a tool that gives you this power, without jeopardizing your data. Hint, hint.
Lie #2. Your “Top” Traffic Sources
What are your top sources of traffic?
A quick glance inside Google Analytics usually tells you (1) organic search and (2) direct. Maybe a little (3) referrals thrown in for good measure if you got some press last month.
Here’s the problem.
Two of those three are legit. The other is not.
The problem is that your direct traffic isn’t, in reality, all that “direct.”
Technically, this should be the number of people typing in your website URL to the address bar and hitting “Enter.”
Instead, it’s a healthy mix of email, social media, and good ol’ organic search.
The bigger the site, the bigger this problem usually is.
For example, The Atlantic couldn’t account for or explain how 25% of their visitors came to their site.
One of the biggest publishers in the world. One of the most respected. Who gets paid based on the number of visitors and page views they get. Has no idea how a quarter of their traffic is getting to their site.
That ain’t good.
But how can you really tell where people are coming from, if most analytics programs can’t tell you with any degree of accuracy?
For instance, let’s say your new, fantastic-looking email campaign is about to go out.
It’s been given the green light. “Legal” gives you the A-OK.
But wait! You didn’t tag the promo links correctly.
Now, you’ve spent all that time on a campaign that won’t have anything to show for it, because the traffic you get will now end up in the dumpster pile officially known as “Direct traffic.”
This isn’t just an email. It affects each and every social message, press mention, and blog post referral, too.
It can even affect your organic search traffic.
Groupon found this out the hard way. Literally. By completely de-indexing themselves for a few hours.
What did those crazy couponers find? That nearly 60% of their direct traffic was actually coming from organic search.
Sixty-freaking-percent.
But don’t freak out just yet. There are solutions here.
First, you can use Google’s UTM builder to make sure you are properly tagging your links. This means any and everything you have control over.
Manually tag them before they head out the door, or copy & paste into a lightweight app like Terminus.
If you’ve got long, cumbersome URL, you can be pretty sure that any traffic to that page didn’t come from Direct traffic.
People aren’t going to remember it. Which means they aren’t going to just spontaneously type it in.
Instead, these peeps probably came from another place, like an organic search or email.
However, in the same breath, you can probably consider homepage traffic to be legitimate Direct.
So create a segment based on these URLs and traffic sources to pinpoint “Dark Traffic” in its tracks. And prevent it from ruining your data in the future.
Lie #3. Top of the Funnel Performance = Results
Yes, we want traffic.
Yes, we want pageviews.
They make us feel all warm and fuzzy and proud. Like our hard work isn’t going unnoticed.
But they should not be the end-all, be-all.
Use them to see how you’re doing over last month. But don’t misunderstand numbers to be the Holy Grail, either.
Like this, for instance:
Looking at only this, you walk away feeling like a boss for all the numbers you’ve racked up. Seriously, I can’t even count that high.
But what about when you consider the bounce and exit rates for each of those pages? Are people staying? No? Color you embarrassed.
Are you still so excited by your thousands of pageviews if most of them left immediately?
Bounce rates are real. And you’ve gotta consider them when you are looking at your metrics.
They mean that people haven’t had the chance to interact with your soft micro conversions. They haven’t had a chance to activate.
So take a look at the big picture.
Are your blog posts and site pulling people in, but not making them stay?
This isn’t a horrible problem to have, because it’s a problem you can pinpoint.
The traffic is there. They just don’t really like what they are seeing once they get to your site. Which you can fix.
First, set up some events to get a better idea of what’s happening on your pages. Then, make sure you have actionable goals that will allow for movement you can track.
Or use the Kissmetrics’ Customer Engagement Automation tool to analyze what people are actually doing on your site and with your products. Then, you can interact with behavior-based messaging to keep them around longer. Or keep them coming back for more.
That way, you can increase conversions, engagement, and retention without the guesswork.
Just always remember that numbers don’t tell the whole story. Use them with a grain of salt and a little bit of context.
Lie #4. Deceptive A/B “Wins”
I’m just going to be honest with you. Those A/B testing “wins” you just got? Don’t always have the best track record.
I’m sorry to be so harsh right off the bat. Sometimes the truth hurts.
What’s even more worrisome? Oftentimes, tests will look like they have succeeded. But that’s not always the case (or at least, not the whole picture).
Start with Google Analytics content experiments, instead.
You can use it to contrast your varying pages to see if there are any sizeable adjustments that cause positive changes.
Instead, it allows you to compare different page variations to see which ‘bigger’ changes result in improvements. Maybe this works a little better because it adds an extra letter– it’s an A/B/N test.
The problem with this test is when you get a little too grab-happy.
You can quickly and easily remove fields to get better results, for instance. A simple reduction of three fields will increase your conversions by 11%.
Or, you can take away specific conversion-busters like the need to add a credit card for a non-paying trial. Sure, this will up your “conversions.”
But remember how far that got you a few lies ago?
That credit card field you took away? It was a huge indicator for which of your customers will eventually buy. 50% of people who put in their credit card will end up converting. While only 15% will of those who don’t enter a credit card will.
And we’re talkin’, conversions-conversions here. Like, bottom-of-the-funnel, paying customer conversions.
Context is key when you are looking at analytics.
Don’t test landing pages or simple changes to fields while only evaluating the top of the funnel. Make sure you dig in to see how the changes affect the rest of the customer experience and journey.
To do that, use the Funnel Report so you can see exactly how top-of-the-funnel changes are impacting bottom-of-the-funnel sales.
Lie #5. Your Channel Source Attribution
A Forrester Research study years ago found that 33% of all transactions of all transactions happened after new customers had gone through more than one touchpoint.
That number jumps to 48% when considering repeat customers.
The same report showed that paid search is the highest source of conversions.
Is it, though?
Or is it just the last point most commonly used before a sale?
Just because it’s the last one, doesn’t mean it’s the only one.
What other marketing tactics are working to increase growth? Forrester went on to declare that while email works for repeat conversions, social media brings in less than 1% of sales.
Ok. Then how do you explain SpearmintLOVE?
You know, the freaking baby blog that boosted their revenue by 991% in year using Facebook and Instagram.
The only reason I know about them? Because my wife has bought clothes from them. After discovering them on Facebook and Instagram.
One, simple Google graph puts this myth to bed. Fast.
If you look at the left side, or “assist interactions,” you’ll see that social channels will put new products in front of people.
As you move toward the middle, customers get more information about products and options using search. At the end, they’re on their way to the website.
Notice all the possible interaction options here. It’s not just the last-touch that brought the customer to the website. They can take many steps to get there.
Google Analytics has a few different attribution options built-in to help you change how conversions are assigned.
Image Source
These include:
Last Non-Direct Click: This will overlook Direct clicks and go to the channel used right before.
First Interaction: This uses the social or advertisement that got them to the website.
Linear: Here, each channel that a customer used before purchasing will get equal attribution.
Time Decay: This will consider the channel that was used immediately before conversion, rather than channels used in the past day/week/month.
Position: This model gives priority to the first and last channel used before conversion. Anything in the middle gets less attribution.
The depressing part, though?
There’s no right answer here. The attribution model you pick largely depends on your sales cycle, your customers, and even what specific objective you’re trying to figure out.
For example, if you’re spending a ton on ads, you might want to see how the First Interaction looks. Especially when using social ads that often bring people into your ecosystem for the very first time.
In other cases? It would be a terrible choice.
The trick is to know what you’re solving for, first. Then working backwards.
Conclusion
Data is important. It’s huge.
YOOGE.
But, be careful.
Google Analytics is a marvelous, cost effective, game changing tool.
However, it’s been known to lie a little from time-to-time. (Yes, we’re still talking about Google Analytics here.)
Remember that conversion results aren’t always spot on. Direct traffic data might not be correct. Vanity metrics aren’t everything. A/B results can fire off false positives. And last touch isn’t everything.
Uncovering biases is never fun.
But it’s the key to creating campaigns that actually achieve results.
Without just blowing a lot of hot air.
About the Author: Brad Smith is the founder of Codeless, a B2B content creation company. Frequent contributor to Kissmetrics, Unbounce, WordStream, AdEspresso, Search Engine Journal, Autopilot, and more.
from Search Results for “analytics” – The Kissmetrics Marketing Blog http://ift.tt/2wkTx6Q
#Digital #Analytics #Website
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5 Lies You Tell Yourself About Your Analytics (And How to Fix It)
Consulting data is good.
But being a slave to data is not.
There is such a thing as being too data-obsessed. Confirmation bias pops up. And you miss the good, albeit, intangible stuff that comes along with your efforts.
The solution is to uncover those biases and misunderstandings that lead you astray.
It’s not easy. Or even intuitive. But it’s the only way to avoid these five analytics blinders.
Here’s how it strikes when you least expect it.
Here’s why you fall for it.
And here’s how to avoid it by bringing in other types of feedback and analysis.
Lie #1. Your “Conversions” Are Flawless
You’ve got three AdWords campaigns.
The first brings in zero leads on $78 bucks spent.
The second brings in one at a cost of $135.31.
The third brings in two at $143.28 per lead.
Nine times out of ten, the campaign with more “conversions” is declared the winner.
But what do you really, truly, know about this scenario?
Which campaign is actually performing the best? Which is putting the most money back into your pocket?
There’s simply no way to tell at this point.
First and foremost, these “conversions” are leads — not closed customers.
Second, they might be for different products or services. So different average order values or LTVs come into play.
Third, this is nowhere close to statistical significance. For example, the third campaign has the most leads because you’ve spent the most money on it.
Not because it’s “better.”
What if you simply spend the same amount on the first two? What if you let them both get to around the same ~$150/per mark?
See what I mean?
Too many “what ifs” for my taste.
Yet this is exactly what happens inside any marketing department. The same end result pops up after each client or superior meeting.
Everyone points to the third campaign. It gets the adulation. It gets the increased budget. It gets the additional staff and resources.
So it becomes a self-fulfilling prophecy.
One solution to figure all this out is closed loop analysis.
Ideally, your goal is to match up the customer’s information (name, email, phone, credit card) to the lead data you’re seeing inside Google Analytics.
Haha — just kidding.
That would mean you were gathering Personally Identifiable Information, which is a big no-no in Google Analytics.
Do it and they’ll delete your account right away.
The simplest alternative is to just use a tool that gives you this power, without jeopardizing your data. Hint, hint.
Lie #2. Your “Top” Traffic Sources
What are your top sources of traffic?
A quick glance inside Google Analytics usually tells you (1) organic search and (2) direct. Maybe a little (3) referrals thrown in for good measure if you got some press last month.
Here’s the problem.
Two of those three are legit. The other is not.
The problem is that your direct traffic isn’t, in reality, all that “direct.”
Technically, this should be the number of people typing in your website URL to the address bar and hitting “Enter.”
Instead, it’s a healthy mix of email, social media, and good ol’ organic search.
The bigger the site, the bigger this problem usually is.
For example, The Atlantic couldn’t account for or explain how 25% of their visitors came to their site.
One of the biggest publishers in the world. One of the most respected. Who gets paid based on the number of visitors and page views they get. Has no idea how a quarter of their traffic is getting to their site.
That ain’t good.
But how can you really tell where people are coming from, if most analytics programs can’t tell you with any degree of accuracy?
For instance, let’s say your new, fantastic-looking email campaign is about to go out.
It’s been given the green light. “Legal” gives you the A-OK.
But wait! You didn’t tag the promo links correctly.
Now, you’ve spent all that time on a campaign that won’t have anything to show for it, because the traffic you get will now end up in the dumpster pile officially known as “Direct traffic.”
This isn’t just an email. It affects each and every social message, press mention, and blog post referral, too.
It can even affect your organic search traffic.
Groupon found this out the hard way. Literally. By completely de-indexing themselves for a few hours.
What did those crazy couponers find? That nearly 60% of their direct traffic was actually coming from organic search.
Sixty-freaking-percent.
But don’t freak out just yet. There are solutions here.
First, you can use Google’s UTM builder to make sure you are properly tagging your links. This means any and everything you have control over.
Manually tag them before they head out the door, or copy & paste into a lightweight app like Terminus.
If you’ve got long, cumbersome URL, you can be pretty sure that any traffic to that page didn’t come from Direct traffic.
People aren’t going to remember it. Which means they aren’t going to just spontaneously type it in.
Instead, these peeps probably came from another place, like an organic search or email.
However, in the same breath, you can probably consider homepage traffic to be legitimate Direct.
So create a segment based on these URLs and traffic sources to pinpoint “Dark Traffic” in its tracks. And prevent it from ruining your data in the future.
Lie #3. Top of the Funnel Performance = Results
Yes, we want traffic.
Yes, we want pageviews.
They make us feel all warm and fuzzy and proud. Like our hard work isn’t going unnoticed.
But they should not be the end-all, be-all.
Use them to see how you’re doing over last month. But don’t misunderstand numbers to be the Holy Grail, either.
Like this, for instance:
Looking at only this, you walk away feeling like a boss for all the numbers you’ve racked up. Seriously, I can’t even count that high.
But what about when you consider the bounce and exit rates for each of those pages? Are people staying? No? Color you embarrassed.
Are you still so excited by your thousands of pageviews if most of them left immediately?
Bounce rates are real. And you’ve gotta consider them when you are looking at your metrics.
They mean that people haven’t had the chance to interact with your soft micro conversions. They haven’t had a chance to activate.
So take a look at the big picture.
Are your blog posts and site pulling people in, but not making them stay?
This isn’t a horrible problem to have, because it’s a problem you can pinpoint.
The traffic is there. They just don’t really like what they are seeing once they get to your site. Which you can fix.
First, set up some events to get a better idea of what’s happening on your pages. Then, make sure you have actionable goals that will allow for movement you can track.
Or use the Kissmetrics’ Customer Engagement Automation tool to analyze what people are actually doing on your site and with your products. Then, you can interact with behavior-based messaging to keep them around longer. Or keep them coming back for more.
That way, you can increase conversions, engagement, and retention without the guesswork.
Just always remember that numbers don’t tell the whole story. Use them with a grain of salt and a little bit of context.
Lie #4. Deceptive A/B “Wins”
I’m just going to be honest with you. Those A/B testing “wins” you just got? Don’t always have the best track record.
I’m sorry to be so harsh right off the bat. Sometimes the truth hurts.
What’s even more worrisome? Oftentimes, tests will look like they have succeeded. But that’s not always the case (or at least, not the whole picture).
Start with Google Analytics content experiments, instead.
You can use it to contrast your varying pages to see if there are any sizeable adjustments that cause positive changes.
Instead, it allows you to compare different page variations to see which ‘bigger’ changes result in improvements. Maybe this works a little better because it adds an extra letter– it’s an A/B/N test.
The problem with this test is when you get a little too grab-happy.
You can quickly and easily remove fields to get better results, for instance. A simple reduction of three fields will increase your conversions by 11%.
Or, you can take away specific conversion-busters like the need to add a credit card for a non-paying trial. Sure, this will up your “conversions.”
But remember how far that got you a few lies ago?
That credit card field you took away? It was a huge indicator for which of your customers will eventually buy. 50% of people who put in their credit card will end up converting. While only 15% will of those who don’t enter a credit card will.
And we’re talkin’, conversions-conversions here. Like, bottom-of-the-funnel, paying customer conversions.
Context is key when you are looking at analytics.
Don’t test landing pages or simple changes to fields while only evaluating the top of the funnel. Make sure you dig in to see how the changes affect the rest of the customer experience and journey.
To do that, use the Funnel Report so you can see exactly how top-of-the-funnel changes are impacting bottom-of-the-funnel sales.
Lie #5. Your Channel Source Attribution
A Forrester Research study years ago found that 33% of all transactions of all transactions happened after new customers had gone through more than one touchpoint.
That number jumps to 48% when considering repeat customers.
The same report showed that paid search is the highest source of conversions.
Is it, though?
Or is it just the last point most commonly used before a sale?
Just because it’s the last one, doesn’t mean it’s the only one.
What other marketing tactics are working to increase growth? Forrester went on to declare that while email works for repeat conversions, social media brings in less than 1% of sales.
Ok. Then how do you explain SpearmintLOVE?
You know, the freaking baby blog that boosted their revenue by 991% in year using Facebook and Instagram.
The only reason I know about them? Because my wife has bought clothes from them. After discovering them on Facebook and Instagram.
One, simple Google graph puts this myth to bed. Fast.
If you look at the left side, or “assist interactions,” you’ll see that social channels will put new products in front of people.
As you move toward the middle, customers get more information about products and options using search. At the end, they’re on their way to the website.
Notice all the possible interaction options here. It’s not just the last-touch that brought the customer to the website. They can take many steps to get there.
Google Analytics has a few different attribution options built-in to help you change how conversions are assigned.
Image Source
These include:
Last Non-Direct Click: This will overlook Direct clicks and go to the channel used right before.
First Interaction: This uses the social or advertisement that got them to the website.
Linear: Here, each channel that a customer used before purchasing will get equal attribution.
Time Decay: This will consider the channel that was used immediately before conversion, rather than channels used in the past day/week/month.
Position: This model gives priority to the first and last channel used before conversion. Anything in the middle gets less attribution.
The depressing part, though?
There’s no right answer here. The attribution model you pick largely depends on your sales cycle, your customers, and even what specific objective you’re trying to figure out.
For example, if you’re spending a ton on ads, you might want to see how the First Interaction looks. Especially when using social ads that often bring people into your ecosystem for the very first time.
In other cases? It would be a terrible choice.
The trick is to know what you’re solving for, first. Then working backwards.
Conclusion
Data is important. It’s huge.
YOOGE.
But, be careful.
Google Analytics is a marvelous, cost effective, game-changing tool.
However, it’s been known to lie a little from time-to-time. (Yes, we’re still talking about Google Analytics here.)
Remember that conversion results aren’t always spot on. Direct traffic data might not be correct. Vanity metrics aren’t everything. A/B results can fire off false positives. And last touch isn’t everything.
Uncovering biases is never fun.
But it’s the key to creating campaigns that actually achieve results.
Without just blowing a lot of hot air.
About the Author: Brad Smith is the founder of Codeless, a B2B content creation company. Frequent contributor to Kissmetrics, Unbounce, WordStream, AdEspresso, Search Engine Journal, Autopilot, and more.
http://ift.tt/2eSlhd1
from MarketingRSS http://ift.tt/2xW6h58
via Youtube
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Text
5 Lies You Tell Yourself About Your Analytics (And How to Fix It)
Consulting data is good.
But being a slave to data is not.
There is such a thing as being too data-obsessed. Confirmation bias pops up. And you miss the good, albeit, intangible stuff that comes along with your efforts.
The solution is to uncover those biases and misunderstandings that lead you astray.
It’s not easy. Or even intuitive. But it’s the only way to avoid these five analytics blinders.
Here’s how it strikes when you least expect it.
Here’s why you fall for it.
And here’s how to avoid it by bringing in other types of feedback and analysis.
Lie #1. Your “Conversions” Are Flawless
You’ve got three AdWords campaigns.
The first brings in zero leads on $78 bucks spent.
The second brings in one at a cost of $135.31.
The third brings in two at $143.28 per lead.
Nine times out of ten, the campaign with more “conversions” is declared the winner.
But what do you really, truly, know about this scenario?
Which campaign is actually performing the best? Which is putting the most money back into your pocket?
There’s simply no way to tell at this point.
First and foremost, these “conversions” are leads — not closed customers.
Second, they might be for different products or services. So different average order values or LTVs come into play.
Third, this is nowhere close to statistical significance. For example, the third campaign has the most leads because you’ve spent the most money on it.
Not because it’s “better.”
What if you simply spend the same amount on the first two? What if you let them both get to around the same ~$150/per mark?
See what I mean?
Too many “what ifs” for my taste.
Yet this is exactly what happens inside any marketing department. The same end result pops up after each client or superior meeting.
Everyone points to the third campaign. It gets the adulation. It gets the increased budget. It gets the additional staff and resources.
So it becomes a self-fulfilling prophecy.
One solution to figure all this out is closed loop analysis.
Ideally, your goal is to match up the customer’s information (name, email, phone, credit card) to the lead data you’re seeing inside Google Analytics.
Haha — just kidding.
That would mean you were gathering Personally Identifiable Information, which is a big no-no in Google Analytics.
Do it and they’ll delete your account right away.
The simplest alternative is to just use a tool that gives you this power, without jeopardizing your data. Hint, hint.
Lie #2. Your “Top” Traffic Sources
What are your top sources of traffic?
A quick glance inside Google Analytics usually tells you (1) organic search and (2) direct. Maybe a little (3) referrals thrown in for good measure if you got some press last month.
Here’s the problem.
Two of those three are legit. The other is not.
The problem is that your direct traffic isn’t, in reality, all that “direct.”
Technically, this should be the number of people typing in your website URL to the address bar and hitting “Enter.”
Instead, it’s a healthy mix of email, social media, and good ol’ organic search.
The bigger the site, the bigger this problem usually is.
For example, The Atlantic couldn’t account for or explain how 25% of their visitors came to their site.
One of the biggest publishers in the world. One of the most respected. Who gets paid based on the number of visitors and page views they get. Has no idea how a quarter of their traffic is getting to their site.
That ain’t good.
But how can you really tell where people are coming from, if most analytics programs can’t tell you with any degree of accuracy?
For instance, let’s say your new, fantastic-looking email campaign is about to go out.
It’s been given the green light. “Legal” gives you the A-OK.
But wait! You didn’t tag the promo links correctly.
Now, you’ve spent all that time on a campaign that won’t have anything to show for it, because the traffic you get will now end up in the dumpster pile officially known as “Direct traffic.”
This isn’t just an email. It affects each and every social message, press mention, and blog post referral, too.
It can even affect your organic search traffic.
Groupon found this out the hard way. Literally. By completely de-indexing themselves for a few hours.
What did those crazy couponers find? That nearly 60% of their direct traffic was actually coming from organic search.
Sixty-freaking-percent.
But don’t freak out just yet. There are solutions here.
First, you can use Google’s UTM builder to make sure you are properly tagging your links. This means any and everything you have control over.
Manually tag them before they head out the door, or copy & paste into a lightweight app like Terminus.
If you’ve got long, cumbersome URL, you can be pretty sure that any traffic to that page didn’t come from Direct traffic.
People aren’t going to remember it. Which means they aren’t going to just spontaneously type it in.
Instead, these peeps probably came from another place, like an organic search or email.
However, in the same breath, you can probably consider homepage traffic to be legitimate Direct.
So create a segment based on these URLs and traffic sources to pinpoint “Dark Traffic” in its tracks. And prevent it from ruining your data in the future.
Lie #3. Top of the Funnel Performance = Results
Yes, we want traffic.
Yes, we want pageviews.
They make us feel all warm and fuzzy and proud. Like our hard work isn’t going unnoticed.
But they should not be the end-all, be-all.
Use them to see how you’re doing over last month. But don’t misunderstand numbers to be the Holy Grail, either.
Like this, for instance:
Looking at only this, you walk away feeling like a boss for all the numbers you’ve racked up. Seriously, I can’t even count that high.
But what about when you consider the bounce and exit rates for each of those pages? Are people staying? No? Color you embarrassed.
Are you still so excited by your thousands of pageviews if most of them left immediately?
Bounce rates are real. And you’ve gotta consider them when you are looking at your metrics.
They mean that people haven’t had the chance to interact with your soft micro conversions. They haven’t had a chance to activate.
So take a look at the big picture.
Are your blog posts and site pulling people in, but not making them stay?
This isn’t a horrible problem to have, because it’s a problem you can pinpoint.
The traffic is there. They just don’t really like what they are seeing once they get to your site. Which you can fix.
First, set up some events to get a better idea of what’s happening on your pages. Then, make sure you have actionable goals that will allow for movement you can track.
Or use the Kissmetrics’ Customer Engagement Automation tool to analyze what people are actually doing on your site and with your products. Then, you can interact with behavior-based messaging to keep them around longer. Or keep them coming back for more.
That way, you can increase conversions, engagement, and retention without the guesswork.
Just always remember that numbers don’t tell the whole story. Use them with a grain of salt and a little bit of context.
Lie #4. Deceptive A/B “Wins”
I’m just going to be honest with you. Those A/B testing “wins” you just got? Don’t always have the best track record.
I’m sorry to be so harsh right off the bat. Sometimes the truth hurts.
What’s even more worrisome? Oftentimes, tests will look like they have succeeded. But that’s not always the case (or at least, not the whole picture).
Start with Google Analytics content experiments, instead.
You can use it to contrast your varying pages to see if there are any sizeable adjustments that cause positive changes.
Instead, it allows you to compare different page variations to see which ‘bigger’ changes result in improvements. Maybe this works a little better because it adds an extra letter– it’s an A/B/N test.
The problem with this test is when you get a little too grab-happy.
You can quickly and easily remove fields to get better results, for instance. A simple reduction of three fields will increase your conversions by 11%.
Or, you can take away specific conversion-busters like the need to add a credit card for a non-paying trial. Sure, this will up your “conversions.”
But remember how far that got you a few lies ago?
That credit card field you took away? It was a huge indicator for which of your customers will eventually buy. 50% of people who put in their credit card will end up converting. While only 15% will of those who don’t enter a credit card will.
And we’re talkin’, conversions-conversions here. Like, bottom-of-the-funnel, paying customer conversions.
Context is key when you are looking at analytics.
Don’t test landing pages or simple changes to fields while only evaluating the top of the funnel. Make sure you dig in to see how the changes affect the rest of the customer experience and journey.
To do that, use the Funnel Report so you can see exactly how top-of-the-funnel changes are impacting bottom-of-the-funnel sales.
Lie #5. Your Channel Source Attribution
A Forrester Research study years ago found that 33% of all transactions of all transactions happened after new customers had gone through more than one touchpoint.
That number jumps to 48% when considering repeat customers.
The same report showed that paid search is the highest source of conversions.
Is it, though?
Or is it just the last point most commonly used before a sale?
Just because it’s the last one, doesn’t mean it’s the only one.
What other marketing tactics are working to increase growth? Forrester went on to declare that while email works for repeat conversions, social media brings in less than 1% of sales.
Ok. Then how do you explain SpearmintLOVE?
You know, the freaking baby blog that boosted their revenue by 991% in year using Facebook and Instagram.
The only reason I know about them? Because my wife has bought clothes from them. After discovering them on Facebook and Instagram.
One, simple Google graph puts this myth to bed. Fast.
If you look at the left side, or “assist interactions,” you’ll see that social channels will put new products in front of people.
As you move toward the middle, customers get more information about products and options using search. At the end, they’re on their way to the website.
Notice all the possible interaction options here. It’s not just the last-touch that brought the customer to the website. They can take many steps to get there.
Google Analytics has a few different attribution options built-in to help you change how conversions are assigned.
Image Source
These include:
Last Non-Direct Click: This will overlook Direct clicks and go to the channel used right before.
First Interaction: This uses the social or advertisement that got them to the website.
Linear: Here, each channel that a customer used before purchasing will get equal attribution.
Time Decay: This will consider the channel that was used immediately before conversion, rather than channels used in the past day/week/month.
Position: This model gives priority to the first and last channel used before conversion. Anything in the middle gets less attribution.
The depressing part, though?
There’s no right answer here. The attribution model you pick largely depends on your sales cycle, your customers, and even what specific objective you’re trying to figure out.
For example, if you’re spending a ton on ads, you might want to see how the First Interaction looks. Especially when using social ads that often bring people into your ecosystem for the very first time.
In other cases? It would be a terrible choice.
The trick is to know what you’re solving for, first. Then working backwards.
Conclusion
Data is important. It’s huge.
YOOGE.
But, be careful.
Google Analytics is a marvelous, cost effective, game-changing tool.
However, it’s been known to lie a little from time-to-time. (Yes, we’re still talking about Google Analytics here.)
Remember that conversion results aren’t always spot on. Direct traffic data might not be correct. Vanity metrics aren’t everything. A/B results can fire off false positives. And last touch isn’t everything.
Uncovering biases is never fun.
But it’s the key to creating campaigns that actually achieve results.
Without just blowing a lot of hot air.
About the Author: Brad Smith is the founder of Codeless, a B2B content creation company. Frequent contributor to Kissmetrics, Unbounce, WordStream, AdEspresso, Search Engine Journal, Autopilot, and more.
0 notes
Text
5 Lies You Tell Yourself About Your Analytics (And How to Fix It)
Consulting data is good.
But being a slave to data, is not.
There is such a thing as being too data-obsessed. Confirmation bias pops up. And you miss the good, albeit, intangible stuff that comes along with your efforts.
The solution is to uncover those biases and misunderstandings that lead you astray.
It’s not easy. Or even intuitive. But it’s the only way to avoid these five analytics blinders.
Here’s how it strikes when you least expect it.
Here’s why you fall for it.
And here’s how to avoid it by bringing in other types of feedback and analysis.
Lie #1. Your “conversions” are flawless
You’ve got three AdWords campaigns.
The first brings in zero leads on $78 bucks spent.
The second brings in one at a cost of $135.31.
The third brings in two at $143.28 per lead.
Nine times out of ten, the campaign with more “conversions” is declared the winner.
But what do you really, truly, know about this scenario?
Which campaign is actually performing the best? Which is putting the most money back into your pocket?
There’s simply no way to tell at this point.
First and foremost, these “conversions” are leads — not closed customers.
Second, they might be for different products or services. So different average order values or LTVs come into play.
Third, this is nowhere close to statistical significance. For example, the third campaign has the most leads because you’ve spent the most money on it.
Not because it’s “better.”
What if you simply spend the same amount on the first two? What if you let them both get to around the same ~$150/per mark?
See what I mean?
Too many “what ifs” for my taste.
Yet this is exactly what happens inside any marketing department. The same end result pops up after each client or superior meeting.
Everyone points to the third campaign. It gets the adulation. It gets the increased budget. It gets the additional staff and resources.
So it becomes a self-fulfilling prophecy.
One solution to figure all this out is closed loop analysis.
Ideally, your goal is to match up the customer’s information (name, email, phone, credit card) to the lead data you’re seeing inside Google Analytics.
Haha — just kidding.
That would mean you were gathering Personally Identifiable Information, which is a big no-no in Google Analytics.
Do it and they’ll delete your account right away.
The simplest alternative is to just use a tool that gives you this power, without jeopardizing your data. Hint, hint.
Lie #2. Your “top” traffic sources
What are your top sources of traffic?
A quick glance inside Google Analytics usually tells you (1) organic search and (2) direct. Maybe a little (3) referrals thrown in for good measure if you got some press last month.
Here’s the problem.
Two of those three are legit. The other is not.
The problem is that your direct traffic isn’t, in reality, all that “direct.”
Technically, this should be the number of people typing in your website URL to the address bar and hitting “Enter.”
Instead, it’s a healthy mix of email, social media, and good ol’ organic search.
The bigger the site, the bigger this problem usually is.
For example, The Atlantic couldn’t account for or explain how 25% of their visitors came to their site.
One of the biggest publishers in the world. One of the most respected. Who gets paid based on the number of visitors and page views they get. Has no idea how a quarter of their traffic is getting to their site.
That ain’t good.
But how can you really tell where people are coming from, if most analytics programs can’t tell you with any degree of accuracy?
For instance, let’s say your new, fantastic-looking email campaign is about to go out.
It’s been given the green light. “Legal” gives you the A-OK.
But wait! You didn’t tag the promo links correctly.
Now, you’ve spent all that time on a campaign that won’t have anything to show for it, because the traffic you get will now end up in the dumpster pile officially known as “Direct traffic.”
This isn’t just an email. It affects each and every social message, press mention, and blog post referral, too.
It can even affect your organic search traffic.
Groupon found this out the hard way. Literally. By completely de-indexing themselves for a few hours.
What did those crazy couponers find? That nearly 60% of their direct traffic was actually coming from organic search.
Sixty-freaking-percent.
But don’t freak out just yet. There are solutions here.
First, you can use Google’s UTM builder to make sure you are properly tagging your links. This means any and everything you have control over.
Manually tag them before they head out the door, or copy & paste into a lightweight app like Terminus.
If you’ve got long, cumbersome URL, you can be pretty sure that any traffic to that page didn’t come from Direct traffic.
People aren’t going to remember it. Which means they aren’t going to just spontaneously type it in.
Instead, these peeps probably came from another place, like an organic search or email.
However, in the same breath, you can probably consider homepage traffic to be legitimate Direct.
So create a segment based on these URLs and traffic sources to pinpoint “Dark Traffic” in its tracks. And prevent it from ruining your data in the future.
Lie #3. Top of the funnel performance = results
Yes, we want traffic.
Yes, we want pageviews.
They make us feel all warm and fuzzy and proud. Like our hard work isn’t going unnoticed.
But they should not be the end-all, be-all.
Use them to see how you’re doing over last month. But don’t misunderstand numbers to be the Holy Grail, either.
Like this, for instance:
Looking at only this, you walk away feeling like a boss for all the numbers you’ve racked up. Seriously, I can’t even count that high.
But what about when you consider the bounce and exit rates for each of those pages? Are people staying? No? Color you embarrassed.
Are you still so excited by your thousands of pageviews if most of them left immediately?
Bounce rates are real. And you’ve gotta consider them when you are looking at your metrics.
They mean that people haven’t had the chance to interact with your soft micro conversions. They haven’t had a chance to activate.
So take a look at the big picture.
Are your blog posts and site pulling people in, but not making them stay?
This isn’t a horrible problem to have, because it’s a problem you can pinpoint.
The traffic is there. They just don’t really like what they are seeing once they get to your site. Which you can fix.
First, set up some events to get a better idea of what’s happening on your pages. Then, make sure you have actionable goals that will allow for movement you can track.
Or use the Kissmetrics’ Customer Engagement Automation tool to analyze what people are actually doing on your site and with your products. Then, you can interact with behavior-based messaging to keep them around longer. Or keep them coming back for more.
That way, you can increase conversions, engagement, and retention without the guesswork.
Just always remember that numbers don’t tell the whole story. Use them with a grain of salt and a little bit of context.
Lie #4. Deceptive A/B “wins”
I’m just going to be honest with you. Those A/B testing “wins” you just got? Don’t always have the best track record.
I’m sorry to be so harsh right off the bat. Sometimes the truth hurts.
What’s even more worrisome? Oftentimes, tests will look like they have succeeded. But that’s not always the case (or at least, not the whole picture).
Start with Google Analytics content experiments, instead.
You can use it to contrast your varying pages to see if there are any sizeable adjustments that cause positive changes.
Instead, it allows you to compare different page variations to see which ‘bigger’ changes result in improvements. Maybe this works a little better because it adds an extra letter– it’s an A/B/N test.
The problem with this test is when you get a little too grab-happy.
You can quickly and easily remove fields to get better results, for instance. A simple reduction of three fields will increase your conversions by 11%.
Or, you can take away specific conversion-busters like the need to add a credit card for a non-paying trial. Sure, this will up your “conversions.”
But remember how far that got you a few lies ago?
That credit card field you took away? It was a huge indicator for which of your customers will eventually buy. 50% of people who put in their credit card will end up converting. While only 15% will of those who don’t enter a credit card will.
And we’re talkin’, conversions-conversions here. Like, bottom-of-the-funnel, paying customer conversions.
Context is key when you are looking at analytics.
Don’t test landing pages or simple changes to fields while only evaluating the top of the funnel. Make sure you dig in to see how the changes affect the rest of the customer experience and journey.
To do that, use the Funnel Report so you can see exactly how top-of-the-funnel changes are impacting bottom-of-the-funnel sales.
Lie #5. Your channel source attribution
A Forrester Research study years ago found that 33% of all transactions of all transactions happened after new customers had gone through more than one touchpoint.
That number jumps to 48% when considering repeat customers.
The same report showed that paid search is the highest source of conversions.
Is it, though?
Or is it just the last point most commonly used before a sale?
Just because it’s the last one, doesn’t mean it’s the only one.
What other marketing tactics are working to increase growth? Forrester went on to declare that while email works for repeat conversions, social media brings in less than 1% of sales.
Ok. Then how do you explain SpearmintLOVE?
You know, the freaking baby blog that boosted their revenue by 991% in year using Facebook and Instagram.
The only reason I know about them? Because my wife has bought clothes from them. After discovering them on Facebook and Instagram.
One, simple Google graph puts this myth to bed. Fast.
If you look at the left side, or “assist interactions,” you’ll see that social channels will put new products in front of people.
As you move toward the middle, customers get more information about products and options using search. At the end, they’re on their way to the website.
Notice all the possible interaction options here. It’s not just the last-touch that brought the customer to the website. They can take many steps to get there.
Google Analytics has a few different attribution options built-in to help you change how conversions are assigned.
Image Source
These include:
Last Non-Direct Click: This will overlook Direct clicks and go to the channel used right before.
First Interaction: This uses the social or advertisement that got them to the website.
Linear: Here, each channel that a customer used before purchasing will get equal attribution.
Time Decay: This will consider the channel that was used immediately before conversion, rather than channels used in the past day/week/month.
Position: This model gives priority to the first and last channel used before conversion. Anything in the middle gets less attribution.
The depressing part, though?
There’s no right answer here. The attribution model you pick largely depends on your sales cycle, your customers, and even what specific objective you’re trying to figure out.
For example, if you’re spending a ton on ads, you might want to see how the First Interaction looks. Especially when using social ads that often bring people into your ecosystem for the very first time.
In other cases? It would be a terrible choice.
The trick is to know what you’re solving for, first. Then working backwards.
Conclusion
Data is important. It’s huge.
YOOGE.
But, be careful.
Google Analytics is a marvelous, cost effective, game changing tool.
However, it’s been known to lie a little from time-to-time. (Yes, we’re still talking about Google Analytics here.)
Remember that conversion results aren’t always spot on. Direct traffic data might not be correct. Vanity metrics aren’t everything. A/B results can fire off false positives. And last touch isn’t everything.
Uncovering biases is never fun.
But it’s the key to creating campaigns that actually achieve results.
Without just blowing a lot of hot air.
About the Author: Brad Smith is the founder of Codeless, a B2B content creation company. Frequent contributor to Kissmetrics, Unbounce, WordStream, AdEspresso, Search Engine Journal, Autopilot, and more.
from WordPress https://reviewandbonuss.wordpress.com/2017/09/11/5-lies-you-tell-yourself-about-your-analytics-and-how-to-fix-it/
0 notes
Text
5 Lies You Tell Yourself About Your Analytics (And How to Fix It)
Consulting data is good.
But being a slave to data, is not.
There is such a thing as being too data-obsessed. Confirmation bias pops up. And you miss the good, albeit, intangible stuff that comes along with your efforts.
The solution is to uncover those biases and misunderstandings that lead you astray.
It’s not easy. Or even intuitive. But it’s the only way to avoid these five analytics blinders.
Here’s how it strikes when you least expect it.
Here’s why you fall for it.
And here’s how to avoid it by bringing in other types of feedback and analysis.
Lie #1. Your “conversions” are flawless
You’ve got three AdWords campaigns.
The first brings in zero leads on $78 bucks spent.
The second brings in one at a cost of $135.31.
The third brings in two at $143.28 per lead.
Nine times out of ten, the campaign with more “conversions” is declared the winner.
But what do you really, truly, know about this scenario?
Which campaign is actually performing the best? Which is putting the most money back into your pocket?
There’s simply no way to tell at this point.
First and foremost, these “conversions” are leads — not closed customers.
Second, they might be for different products or services. So different average order values or LTVs come into play.
Third, this is nowhere close to statistical significance. For example, the third campaign has the most leads because you’ve spent the most money on it.
Not because it’s “better.”
What if you simply spend the same amount on the first two? What if you let them both get to around the same ~$150/per mark?
See what I mean?
Too many “what ifs” for my taste.
Yet this is exactly what happens inside any marketing department. The same end result pops up after each client or superior meeting.
Everyone points to the third campaign. It gets the adulation. It gets the increased budget. It gets the additional staff and resources.
So it becomes a self-fulfilling prophecy.
One solution to figure all this out is closed loop analysis.
Ideally, your goal is to match up the customer’s information (name, email, phone, credit card) to the lead data you’re seeing inside Google Analytics.
Haha — just kidding.
That would mean you were gathering Personally Identifiable Information, which is a big no-no in Google Analytics.
Do it and they’ll delete your account right away.
The simplest alternative is to just use a tool that gives you this power, without jeopardizing your data. Hint, hint.
Lie #2. Your “top” traffic sources
What are your top sources of traffic?
A quick glance inside Google Analytics usually tells you (1) organic search and (2) direct. Maybe a little (3) referrals thrown in for good measure if you got some press last month.
Here’s the problem.
Two of those three are legit. The other is not.
The problem is that your direct traffic isn’t, in reality, all that “direct.”
Technically, this should be the number of people typing in your website URL to the address bar and hitting “Enter.”
Instead, it’s a healthy mix of email, social media, and good ol’ organic search.
The bigger the site, the bigger this problem usually is.
For example, The Atlantic couldn’t account for or explain how 25% of their visitors came to their site.
One of the biggest publishers in the world. One of the most respected. Who gets paid based on the number of visitors and page views they get. Has no idea how a quarter of their traffic is getting to their site.
That ain’t good.
But how can you really tell where people are coming from, if most analytics programs can’t tell you with any degree of accuracy?
For instance, let’s say your new, fantastic-looking is about to go out.
It’s been given the green light. “Legal” gives you the A-OK.
But wait! You didn’t tag the promo links correctly.
Now, you’ve spent all that time on a campaign that won’t have anything to show for it, because the traffic you get will now end up in the dumpster pile officially known as “Direct traffic.”
This isn’t just an email. It affects each and every social message, press mention, and blog post referral, too.
It can even affect your organic search traffic.
Groupon found this out the hard way. Literally. By completely de-indexing themselves for a few hours.
What did those crazy couponers find? That nearly 60% of their direct traffic was actually coming from organic search.
Sixty-freaking-percent.
But don’t freak out just yet. There are solutions here.
First, you can use Google’s UTM builder to make sure you are properly tagging your links. This means any and everything you have control over.
Manually tag them before they head out the door, or copy & paste into a lightweight app like Terminus.
If you’ve got long, cumbersome URL, you can be pretty sure that any traffic to that page didn’t come from Direct traffic.
People aren’t going to remember it. Which means they aren’t going to just spontaneously type it in.
Instead, these peeps probably came from another place, like an organic search or email.
However, in the same breath, you can probably consider homepage traffic to be legitimate Direct.
So create a segment based on these URLs and traffic sources to pinpoint “Dark Traffic” in its tracks. And prevent it from ruining your data in the future.
Lie #3. Top of the funnel performance = results
Yes, we want traffic.
Yes, we want pageviews.
They make us feel all warm and fuzzy and proud. Like our hard work isn’t going unnoticed.
But they should not be the end-all, be-all.
Use them to see how you’re doing over last month. But don’t misunderstand numbers to be the Holy Grail, either.
Like this, for instance:
Looking at only this, you walk away feeling like a boss for all the numbers you’ve racked up. Seriously, I can’t even count that high.
But what about when you consider the bounce and exit rates for each of those pages? Are people staying? No? Color you embarrassed.
Are you still so excited by your thousands of pageviews if most of them left immediately?
Bounce rates are real. And you’ve gotta consider them when you are looking at your metrics.
They mean that people haven’t had the chance to interact with your soft micro conversions. They haven’t had a chance to activate.
So take a look at the big picture.
Are your blog posts and site pulling people in, but not making them stay?
This isn’t a horrible problem to have, because it’s a problem you can pinpoint.
The traffic is there. They just don’t really like what they are seeing once they get to your site. Which you can fix.
First, set up some events to get a better idea of what’s happening on your pages. Then, make sure you have actionable goals that will allow for movement you can track.
Or use the Kissmetrics’ Customer Engagement Automation tool to analyze what people are actually doing on your site and with your products. Then, you can interact with behavior-based messaging to keep them around longer. Or keep them coming back for more.
That way, you can increase conversions, engagement, and retention without the guesswork.
Just always remember that numbers don’t tell the whole story. Use them with a grain of salt and a little bit of context.
Lie #4. Deceptive A/B “wins”
I’m just going to be honest with you. Those A/B testing “wins” you just got? Don’t always have the best track record.
I’m sorry to be so harsh right off the bat. Sometimes the truth hurts.
What’s even more worrisome? Oftentimes, tests will look like they have succeeded. But that’s not always the case (or at least, not the whole picture).
Start with Google Analytics content experiments, instead.
You can use it to contrast your varying pages to see if there are any sizeable adjustments that cause positive changes.
Instead, it allows you to compare different page variations to see which ‘bigger’ changes result in improvements. Maybe this works a little better because it adds an extra letter– it’s an A/B/N test.
The problem with this test is when you get a little too grab-happy.
You can quickly and easily remove fields to get better results, for instance. A simple reduction of three fields will increase your conversions by 11%.
Or, you can take away specific conversion-busters like the need to add a credit card for a non-paying trial. Sure, this will up your “conversions.”
But remember how far that got you a few lies ago?
That credit card field you took away? It was a huge indicator for which of your customers will eventually buy. 50% of people who put in their credit card will end up converting. While only 15% will of those who don’t enter a credit card will.
And we’re talkin’, conversions-conversions here. Like, bottom-of-the-funnel, paying customer conversions.
Context is key when you are looking at analytics.
Don’t test landing pages or simple changes to fields while only evaluating the top of the funnel. Make sure you dig in to see how the changes affect the rest of the customer experience and journey.
To do that, use the Funnel Report so you can see exactly how top-of-the-funnel changes are impacting bottom-of-the-funnel sales.
Lie #5. Your channel source attribution
A Forrester Research study years ago found that 33% of all transactions of all transactions happened after new customers had gone through more than one touchpoint.
That number jumps to 48% when considering repeat customers.
The same report showed that paid search is the highest source of conversions.
Is it, though?
Or is it just the last point most commonly used before a sale?
Just because it’s the last one, doesn’t mean it’s the only one.
What other marketing tactics are working to increase growth? Forrester went on to declare that while email works for repeat conversions, social media brings in less than 1% of sales.
Ok. Then how do you explain SpearmintLOVE?
You know, the freaking baby blog that boosted their revenue by 991% in year using Facebook and Instagram.
The only reason I know about them? Because my wife has bought clothes from them. After discovering them on Facebook and Instagram.
One, simple Google graph puts this myth to bed. Fast.
If you look at the left side, or “assist interactions,” you’ll see that social channels will put new products in front of people.
As you move toward the middle, customers get more information about products and options using search. At the end, they’re on their way to the website.
Notice all the possible interaction options here. It’s not just the last-touch that brought the customer to the website. They can take many steps to get there.
Google Analytics has a few different attribution options built-in to help you change how conversions are assigned.
Image Source
These include:
Last Non-Direct Click: This will overlook Direct clicks and go to the channel used right before.
First Interaction: This uses the social or advertisement that got them to the website.
Linear: Here, each channel that a customer used before purchasing will get equal attribution.
Time Decay: This will consider the channel that was used immediately before conversion, rather than channels used in the past day/week/month.
Position: This model gives priority to the first and last channel used before conversion. Anything in the middle gets less attribution.
The depressing part, though?
There’s no right answer here. The attribution model you pick largely depends on your sales cycle, your customers, and even what specific objective you’re trying to figure out.
For example, if you’re spending a ton on ads, you might want to see how the First Interaction looks. Especially when using social ads that often bring people into your ecosystem for the very first time.
In other cases? It would be a terrible choice.
The trick is to know what you’re solving for, first. Then working backwards.
Conclusion
Data is important. It’s huge.
YOOGE.
But, be careful.
Google Analytics is a marvelous, cost effective, game changing tool.
However, it’s been known to lie a little from time-to-time. (Yes, we’re still talking about Google Analytics here.)
Remember that conversion results aren’t always spot on. Direct traffic data might not be correct. Vanity metrics aren’t everything. A/B results can fire off false positives. And last touch isn’t everything.
Uncovering biases is never fun.
But it’s the key to creating campaigns that actually achieve results.
Without just blowing a lot of hot air.
About the Author: Brad Smith is the founder of Codeless, a B2B content creation company. Frequent contributor to Kissmetrics, Unbounce, WordStream, AdEspresso, Search Engine Journal, Autopilot, and more.
from DIYS http://ift.tt/2wkTx6Q
0 notes
Text
5 Lies You Tell Yourself About Your Analytics (And How to Fix It)
Consulting data is good.
But being a slave to data, is not.
There is such a thing as being too data-obsessed. Confirmation bias pops up. And you miss the good, albeit, intangible stuff that comes along with your efforts.
The solution is to uncover those biases and misunderstandings that lead you astray.
It’s not easy. Or even intuitive. But it’s the only way to avoid these five analytics blinders.
Here’s how it strikes when you least expect it.
Here’s why you fall for it.
And here’s how to avoid it by bringing in other types of feedback and analysis.
Lie #1. Your “conversions” are flawless
You’ve got three AdWords campaigns.
The first brings in zero leads on $78 bucks spent.
The second brings in one at a cost of $135.31.
The third brings in two at $143.28 per lead.
Nine times out of ten, the campaign with more “conversions” is declared the winner.
But what do you really, truly, know about this scenario?
Which campaign is actually performing the best? Which is putting the most money back into your pocket?
There’s simply no way to tell at this point.
First and foremost, these “conversions” are leads — not closed customers.
Second, they might be for different products or services. So different average order values or LTVs come into play.
Third, this is nowhere close to statistical significance. For example, the third campaign has the most leads because you’ve spent the most money on it.
Not because it’s “better.”
What if you simply spend the same amount on the first two? What if you let them both get to around the same ~$150/per mark?
See what I mean?
Too many “what ifs” for my taste.
Yet this is exactly what happens inside any marketing department. The same end result pops up after each client or superior meeting.
Everyone points to the third campaign. It gets the adulation. It gets the increased budget. It gets the additional staff and resources.
So it becomes a self-fulfilling prophecy.
One solution to figure all this out is closed loop analysis.
Ideally, your goal is to match up the customer’s information (name, email, phone, credit card) to the lead data you’re seeing inside Google Analytics.
Haha — just kidding.
That would mean you were gathering Personally Identifiable Information, which is a big no-no in Google Analytics.
Do it and they’ll delete your account right away.
The simplest alternative is to just use a tool that gives you this power, without jeopardizing your data. Hint, hint.
Lie #2. Your “top” traffic sources
What are your top sources of traffic?
A quick glance inside Google Analytics usually tells you (1) organic search and (2) direct. Maybe a little (3) referrals thrown in for good measure if you got some press last month.
Here’s the problem.
Two of those three are legit. The other is not.
The problem is that your direct traffic isn’t, in reality, all that “direct.”
Technically, this should be the number of people typing in your website URL to the address bar and hitting “Enter.”
Instead, it’s a healthy mix of email, social media, and good ol’ organic search.
The bigger the site, the bigger this problem usually is.
For example, The Atlantic couldn’t account for or explain how 25% of their visitors came to their site.
One of the biggest publishers in the world. One of the most respected. Who gets paid based on the number of visitors and page views they get. Has no idea how a quarter of their traffic is getting to their site.
That ain’t good.
But how can you really tell where people are coming from, if most analytics programs can’t tell you with any degree of accuracy?
For instance, let’s say your new, fantastic-looking email campaign is about to go out.
It’s been given the green light. “Legal” gives you the A-OK.
But wait! You didn’t tag the promo links correctly.
Now, you’ve spent all that time on a campaign that won’t have anything to show for it, because the traffic you get will now end up in the dumpster pile officially known as “Direct traffic.”
This isn’t just an email. It affects each and every social message, press mention, and blog post referral, too.
It can even affect your organic search traffic.
Groupon found this out the hard way. Literally. By completely de-indexing themselves for a few hours.
What did those crazy couponers find? That nearly 60% of their direct traffic was actually coming from organic search.
Sixty-freaking-percent.
But don’t freak out just yet. There are solutions here.
First, you can use Google’s UTM builder to make sure you are properly tagging your links. This means any and everything you have control over.
Manually tag them before they head out the door, or copy & paste into a lightweight app like Terminus.
If you’ve got long, cumbersome URL, you can be pretty sure that any traffic to that page didn’t come from Direct traffic.
People aren’t going to remember it. Which means they aren’t going to just spontaneously type it in.
Instead, these peeps probably came from another place, like an organic search or email.
However, in the same breath, you can probably consider homepage traffic to be legitimate Direct.
So create a segment based on these URLs and traffic sources to pinpoint “Dark Traffic” in its tracks. And prevent it from ruining your data in the future.
Lie #3. Top of the funnel performance = results
Yes, we want traffic.
Yes, we want pageviews.
They make us feel all warm and fuzzy and proud. Like our hard work isn’t going unnoticed.
But they should not be the end-all, be-all.
Use them to see how you’re doing over last month. But don’t misunderstand numbers to be the Holy Grail, either.
Like this, for instance:
Looking at only this, you walk away feeling like a boss for all the numbers you’ve racked up. Seriously, I can’t even count that high.
But what about when you consider the bounce and exit rates for each of those pages? Are people staying? No? Color you embarrassed.
Are you still so excited by your thousands of pageviews if most of them left immediately?
Bounce rates are real. And you’ve gotta consider them when you are looking at your metrics.
They mean that people haven’t had the chance to interact with your soft micro conversions. They haven’t had a chance to activate.
So take a look at the big picture.
Are your blog posts and site pulling people in, but not making them stay?
This isn’t a horrible problem to have, because it’s a problem you can pinpoint.
The traffic is there. They just don’t really like what they are seeing once they get to your site. Which you can fix.
First, set up some events to get a better idea of what’s happening on your pages. Then, make sure you have actionable goals that will allow for movement you can track.
Or use the Kissmetrics’ Customer Engagement Automation tool to analyze what people are actually doing on your site and with your products. Then, you can interact with behavior-based messaging to keep them around longer. Or keep them coming back for more.
That way, you can increase conversions, engagement, and retention without the guesswork.
Just always remember that numbers don’t tell the whole story. Use them with a grain of salt and a little bit of context.
Lie #4. Deceptive A/B “wins”
I’m just going to be honest with you. Those A/B testing “wins” you just got? Don’t always have the best track record.
I’m sorry to be so harsh right off the bat. Sometimes the truth hurts.
What’s even more worrisome? Oftentimes, tests will look like they have succeeded. But that’s not always the case (or at least, not the whole picture).
Start with Google Analytics content experiments, instead.
You can use it to contrast your varying pages to see if there are any sizeable adjustments that cause positive changes.
Instead, it allows you to compare different page variations to see which ‘bigger’ changes result in improvements. Maybe this works a little better because it adds an extra letter– it’s an A/B/N test.
The problem with this test is when you get a little too grab-happy.
You can quickly and easily remove fields to get better results, for instance. A simple reduction of three fields will increase your conversions by 11%.
Or, you can take away specific conversion-busters like the need to add a credit card for a non-paying trial. Sure, this will up your “conversions.”
But remember how far that got you a few lies ago?
That credit card field you took away? It was a huge indicator for which of your customers will eventually buy. 50% of people who put in their credit card will end up converting. While only 15% will of those who don’t enter a credit card will.
And we’re talkin’, conversions-conversions here. Like, bottom-of-the-funnel, paying customer conversions.
Context is key when you are looking at analytics.
Don’t test landing pages or simple changes to fields while only evaluating the top of the funnel. Make sure you dig in to see how the changes affect the rest of the customer experience and journey.
To do that, use the Funnel Report so you can see exactly how top-of-the-funnel changes are impacting bottom-of-the-funnel sales.
Lie #5. Your channel source attribution
A Forrester Research study years ago found that 33% of all transactions of all transactions happened after new customers had gone through more than one touchpoint.
That number jumps to 48% when considering repeat customers.
The same report showed that paid search is the highest source of conversions.
Is it, though?
Or is it just the last point most commonly used before a sale?
Just because it’s the last one, doesn’t mean it’s the only one.
What other marketing tactics are working to increase growth? Forrester went on to declare that while email works for repeat conversions, social media brings in less than 1% of sales.
Ok. Then how do you explain SpearmintLOVE?
You know, the freaking baby blog that boosted their revenue by 991% in year using Facebook and Instagram.
The only reason I know about them? Because my wife has bought clothes from them. After discovering them on Facebook and Instagram.
One, simple Google graph puts this myth to bed. Fast.
If you look at the left side, or “assist interactions,” you’ll see that social channels will put new products in front of people.
As you move toward the middle, customers get more information about products and options using search. At the end, they’re on their way to the website.
Notice all the possible interaction options here. It’s not just the last-touch that brought the customer to the website. They can take many steps to get there.
Google Analytics has a few different attribution options built-in to help you change how conversions are assigned.
Image Source
These include:
Last Non-Direct Click: This will overlook Direct clicks and go to the channel used right before.
First Interaction: This uses the social or advertisement that got them to the website.
Linear: Here, each channel that a customer used before purchasing will get equal attribution.
Time Decay: This will consider the channel that was used immediately before conversion, rather than channels used in the past day/week/month.
Position: This model gives priority to the first and last channel used before conversion. Anything in the middle gets less attribution.
The depressing part, though?
There’s no right answer here. The attribution model you pick largely depends on your sales cycle, your customers, and even what specific objective you’re trying to figure out.
For example, if you’re spending a ton on ads, you might want to see how the First Interaction looks. Especially when using social ads that often bring people into your ecosystem for the very first time.
In other cases? It would be a terrible choice.
The trick is to know what you’re solving for, first. Then working backwards.
Conclusion
Data is important. It’s huge.
YOOGE.
But, be careful.
Google Analytics is a marvelous, cost effective, game changing tool.
However, it’s been known to lie a little from time-to-time. (Yes, we’re still talking about Google Analytics here.)
Remember that conversion results aren’t always spot on. Direct traffic data might not be correct. Vanity metrics aren’t everything. A/B results can fire off false positives. And last touch isn’t everything.
Uncovering biases is never fun.
But it’s the key to creating campaigns that actually achieve results.
Without just blowing a lot of hot air.
About the Author: Brad Smith is the founder of Codeless, a B2B content creation company. Frequent contributor to Kissmetrics, Unbounce, WordStream, AdEspresso, Search Engine Journal, Autopilot, and more.
Read more here - http://review-and-bonuss.blogspot.com/2017/09/5-lies-you-tell-yourself-about-your.html
0 notes
Text
5 Lies You Tell Yourself About Your Analytics (And How to Fix It)
Consulting data is good.
But being a slave to data, is not.
There is such a thing as being too data-obsessed. Confirmation bias pops up. And you miss the good, albeit, intangible stuff that comes along with your efforts.
The solution is to uncover those biases and misunderstandings that lead you astray.
It’s not easy. Or even intuitive. But it’s the only way to avoid these five analytics blinders.
Here’s how it strikes when you least expect it.
Here’s why you fall for it.
And here’s how to avoid it by bringing in other types of feedback and analysis.
Lie #1. Your “conversions” are flawless
You’ve got three AdWords campaigns.
The first brings in zero leads on $78 bucks spent.
The second brings in one at a cost of $135.31.
The third brings in two at $143.28 per lead.
Nine times out of ten, the campaign with more “conversions” is declared the winner.
But what do you really, truly, know about this scenario?
Which campaign is actually performing the best? Which is putting the most money back into your pocket?
There’s simply no way to tell at this point.
First and foremost, these “conversions” are leads — not closed customers.
Second, they might be for different products or services. So different average order values or LTVs come into play.
Third, this is nowhere close to statistical significance. For example, the third campaign has the most leads because you’ve spent the most money on it.
Not because it’s “better.”
What if you simply spend the same amount on the first two? What if you let them both get to around the same ~$150/per mark?
See what I mean?
Too many “what ifs” for my taste.
Yet this is exactly what happens inside any marketing department. The same end result pops up after each client or superior meeting.
Everyone points to the third campaign. It gets the adulation. It gets the increased budget. It gets the additional staff and resources.
So it becomes a self-fulfilling prophecy.
One solution to figure all this out is closed loop analysis.
Ideally, your goal is to match up the customer’s information (name, email, phone, credit card) to the lead data you’re seeing inside Google Analytics.
Haha — just kidding.
That would mean you were gathering Personally Identifiable Information, which is a big no-no in Google Analytics.
Do it and they’ll delete your account right away.
The simplest alternative is to just use a tool that gives you this power, without jeopardizing your data. Hint, hint.
Lie #2. Your “top” traffic sources
What are your top sources of traffic?
A quick glance inside Google Analytics usually tells you (1) organic search and (2) direct. Maybe a little (3) referrals thrown in for good measure if you got some press last month.
Here’s the problem.
Two of those three are legit. The other is not.
The problem is that your direct traffic isn’t, in reality, all that “direct.”
Technically, this should be the number of people typing in your website URL to the address bar and hitting “Enter.”
Instead, it’s a healthy mix of email, social media, and good ol’ organic search.
The bigger the site, the bigger this problem usually is.
For example, The Atlantic couldn’t account for or explain how 25% of their visitors came to their site.
One of the biggest publishers in the world. One of the most respected. Who gets paid based on the number of visitors and page views they get. Has no idea how a quarter of their traffic is getting to their site.
That ain’t good.
But how can you really tell where people are coming from, if most analytics programs can’t tell you with any degree of accuracy?
For instance, let’s say your new, fantastic-looking email campaign is about to go out.
It’s been given the green light. “Legal” gives you the A-OK.
But wait! You didn’t tag the promo links correctly.
Now, you’ve spent all that time on a campaign that won’t have anything to show for it, because the traffic you get will now end up in the dumpster pile officially known as “Direct traffic.”
This isn’t just an email. It affects each and every social message, press mention, and blog post referral, too.
It can even affect your organic search traffic.
Groupon found this out the hard way. Literally. By completely de-indexing themselves for a few hours.
What did those crazy couponers find? That nearly 60% of their direct traffic was actually coming from organic search.
Sixty-freaking-percent.
But don’t freak out just yet. There are solutions here.
First, you can use Google’s UTM builder to make sure you are properly tagging your links. This means any and everything you have control over.
Manually tag them before they head out the door, or copy & paste into a lightweight app like Terminus.
If you’ve got long, cumbersome URL, you can be pretty sure that any traffic to that page didn’t come from Direct traffic.
People aren’t going to remember it. Which means they aren’t going to just spontaneously type it in.
Instead, these peeps probably came from another place, like an organic search or email.
However, in the same breath, you can probably consider homepage traffic to be legitimate Direct.
So create a segment based on these URLs and traffic sources to pinpoint “Dark Traffic” in its tracks. And prevent it from ruining your data in the future.
Lie #3. Top of the funnel performance = results
Yes, we want traffic.
Yes, we want pageviews.
They make us feel all warm and fuzzy and proud. Like our hard work isn’t going unnoticed.
But they should not be the end-all, be-all.
Use them to see how you’re doing over last month. But don’t misunderstand numbers to be the Holy Grail, either.
Like this, for instance:
Looking at only this, you walk away feeling like a boss for all the numbers you’ve racked up. Seriously, I can’t even count that high.
But what about when you consider the bounce and exit rates for each of those pages? Are people staying? No? Color you embarrassed.
Are you still so excited by your thousands of pageviews if most of them left immediately?
Bounce rates are real. And you’ve gotta consider them when you are looking at your metrics.
They mean that people haven’t had the chance to interact with your soft micro conversions. They haven’t had a chance to activate.
So take a look at the big picture.
Are your blog posts and site pulling people in, but not making them stay?
This isn’t a horrible problem to have, because it’s a problem you can pinpoint.
The traffic is there. They just don’t really like what they are seeing once they get to your site. Which you can fix.
First, set up some events to get a better idea of what’s happening on your pages. Then, make sure you have actionable goals that will allow for movement you can track.
Or use the Kissmetrics’ Customer Engagement Automation tool to analyze what people are actually doing on your site and with your products. Then, you can interact with behavior-based messaging to keep them around longer. Or keep them coming back for more.
That way, you can increase conversions, engagement, and retention without the guesswork.
Just always remember that numbers don’t tell the whole story. Use them with a grain of salt and a little bit of context.
Lie #4. Deceptive A/B “wins”
I’m just going to be honest with you. Those A/B testing “wins” you just got? Don’t always have the best track record.
I’m sorry to be so harsh right off the bat. Sometimes the truth hurts.
What’s even more worrisome? Oftentimes, tests will look like they have succeeded. But that’s not always the case (or at least, not the whole picture).
Start with Google Analytics content experiments, instead.
You can use it to contrast your varying pages to see if there are any sizeable adjustments that cause positive changes.
Instead, it allows you to compare different page variations to see which ‘bigger’ changes result in improvements. Maybe this works a little better because it adds an extra letter– it’s an A/B/N test.
The problem with this test is when you get a little too grab-happy.
You can quickly and easily remove fields to get better results, for instance. A simple reduction of three fields will increase your conversions by 11%.
Or, you can take away specific conversion-busters like the need to add a credit card for a non-paying trial. Sure, this will up your “conversions.”
But remember how far that got you a few lies ago?
That credit card field you took away? It was a huge indicator for which of your customers will eventually buy. 50% of people who put in their credit card will end up converting. While only 15% will of those who don’t enter a credit card will.
And we’re talkin’, conversions-conversions here. Like, bottom-of-the-funnel, paying customer conversions.
Context is key when you are looking at analytics.
Don’t test landing pages or simple changes to fields while only evaluating the top of the funnel. Make sure you dig in to see how the changes affect the rest of the customer experience and journey.
To do that, use the Funnel Report so you can see exactly how top-of-the-funnel changes are impacting bottom-of-the-funnel sales.
Lie #5. Your channel source attribution
A Forrester Research study years ago found that 33% of all transactions of all transactions happened after new customers had gone through more than one touchpoint.
That number jumps to 48% when considering repeat customers.
The same report showed that paid search is the highest source of conversions.
Is it, though?
Or is it just the last point most commonly used before a sale?
Just because it’s the last one, doesn’t mean it’s the only one.
What other marketing tactics are working to increase growth? Forrester went on to declare that while email works for repeat conversions, social media brings in less than 1% of sales.
Ok. Then how do you explain SpearmintLOVE?
You know, the freaking baby blog that boosted their revenue by 991% in year using Facebook and Instagram.
The only reason I know about them? Because my wife has bought clothes from them. After discovering them on Facebook and Instagram.
One, simple Google graph puts this myth to bed. Fast.
If you look at the left side, or “assist interactions,” you’ll see that social channels will put new products in front of people.
As you move toward the middle, customers get more information about products and options using search. At the end, they’re on their way to the website.
Notice all the possible interaction options here. It’s not just the last-touch that brought the customer to the website. They can take many steps to get there.
Google Analytics has a few different attribution options built-in to help you change how conversions are assigned.
Image Source
These include:
Last Non-Direct Click: This will overlook Direct clicks and go to the channel used right before.
First Interaction: This uses the social or advertisement that got them to the website.
Linear: Here, each channel that a customer used before purchasing will get equal attribution.
Time Decay: This will consider the channel that was used immediately before conversion, rather than channels used in the past day/week/month.
Position: This model gives priority to the first and last channel used before conversion. Anything in the middle gets less attribution.
The depressing part, though?
There’s no right answer here. The attribution model you pick largely depends on your sales cycle, your customers, and even what specific objective you’re trying to figure out.
For example, if you’re spending a ton on ads, you might want to see how the First Interaction looks. Especially when using social ads that often bring people into your ecosystem for the very first time.
In other cases? It would be a terrible choice.
The trick is to know what you’re solving for, first. Then working backwards.
Conclusion
Data is important. It’s huge.
YOOGE.
But, be careful.
Google Analytics is a marvelous, cost effective, game changing tool.
However, it’s been known to lie a little from time-to-time. (Yes, we’re still talking about Google Analytics here.)
Remember that conversion results aren’t always spot on. Direct traffic data might not be correct. Vanity metrics aren’t everything. A/B results can fire off false positives. And last touch isn’t everything.
Uncovering biases is never fun.
But it’s the key to creating campaigns that actually achieve results.
Without just blowing a lot of hot air.
About the Author: Brad Smith is the founder of Codeless, a B2B content creation company. Frequent contributor to Kissmetrics, Unbounce, WordStream, AdEspresso, Search Engine Journal, Autopilot, and more.
0 notes
Text
5 Lies You Tell Yourself About Your Analytics (And How to Fix It)
Consulting data is good.
But being a slave to data, is not.
There is such a thing as being too data-obsessed. Confirmation bias pops up. And you miss the good, albeit, intangible stuff that comes along with your efforts.
The solution is to uncover those biases and misunderstandings that lead you astray.
It’s not easy. Or even intuitive. But it’s the only way to avoid these five analytics blinders.
Here’s how it strikes when you least expect it.
Here’s why you fall for it.
And here’s how to avoid it by bringing in other types of feedback and analysis.
Lie #1. Your “conversions” are flawless
You’ve got three AdWords campaigns.
The first brings in zero leads on $78 bucks spent.
The second brings in one at a cost of $135.31.
The third brings in two at $143.28 per lead.
Nine times out of ten, the campaign with more “conversions” is declared the winner.
But what do you really, truly, know about this scenario?
Which campaign is actually performing the best? Which is putting the most money back into your pocket?
There’s simply no way to tell at this point.
First and foremost, these “conversions” are leads — not closed customers.
Second, they might be for different products or services. So different average order values or LTVs come into play.
Third, this is nowhere close to statistical significance. For example, the third campaign has the most leads because you’ve spent the most money on it.
Not because it’s “better.”
What if you simply spend the same amount on the first two? What if you let them both get to around the same ~$150/per mark?
See what I mean?
Too many “what ifs” for my taste.
Yet this is exactly what happens inside any marketing department. The same end result pops up after each client or superior meeting.
Everyone points to the third campaign. It gets the adulation. It gets the increased budget. It gets the additional staff and resources.
So it becomes a self-fulfilling prophecy.
One solution to figure all this out is closed loop analysis.
Ideally, your goal is to match up the customer’s information (name, email, phone, credit card) to the lead data you’re seeing inside Google Analytics.
Haha — just kidding.
That would mean you were gathering Personally Identifiable Information, which is a big no-no in Google Analytics.
Do it and they’ll delete your account right away.
The simplest alternative is to just use a tool that gives you this power, without jeopardizing your data. Hint, hint.
Lie #2. Your “top” traffic sources
What are your top sources of traffic?
A quick glance inside Google Analytics usually tells you (1) organic search and (2) direct. Maybe a little (3) referrals thrown in for good measure if you got some press last month.
Here’s the problem.
Two of those three are legit. The other is not.
The problem is that your direct traffic isn’t, in reality, all that “direct.”
Technically, this should be the number of people typing in your website URL to the address bar and hitting “Enter.”
Instead, it’s a healthy mix of email, social media, and good ol’ organic search.
The bigger the site, the bigger this problem usually is.
For example, The Atlantic couldn’t account for or explain how 25% of their visitors came to their site.
One of the biggest publishers in the world. One of the most respected. Who gets paid based on the number of visitors and page views they get. Has no idea how a quarter of their traffic is getting to their site.
That ain’t good.
But how can you really tell where people are coming from, if most analytics programs can’t tell you with any degree of accuracy?
For instance, let’s say your new, fantastic-looking email campaign is about to go out.
It’s been given the green light. “Legal” gives you the A-OK.
But wait! You didn’t tag the promo links correctly.
Now, you’ve spent all that time on a campaign that won’t have anything to show for it, because the traffic you get will now end up in the dumpster pile officially known as “Direct traffic.”
This isn’t just an email. It affects each and every social message, press mention, and blog post referral, too.
It can even affect your organic search traffic.
Groupon found this out the hard way. Literally. By completely de-indexing themselves for a few hours.
What did those crazy couponers find? That nearly 60% of their direct traffic was actually coming from organic search.
Sixty-freaking-percent.
But don’t freak out just yet. There are solutions here.
First, you can use Google’s UTM builder to make sure you are properly tagging your links. This means any and everything you have control over.
Manually tag them before they head out the door, or copy & paste into a lightweight app like Terminus.
If you’ve got long, cumbersome URL, you can be pretty sure that any traffic to that page didn’t come from Direct traffic.
People aren’t going to remember it. Which means they aren’t going to just spontaneously type it in.
Instead, these peeps probably came from another place, like an organic search or email.
However, in the same breath, you can probably consider homepage traffic to be legitimate Direct.
So create a segment based on these URLs and traffic sources to pinpoint “Dark Traffic” in its tracks. And prevent it from ruining your data in the future.
Lie #3. Top of the funnel performance = results
Yes, we want traffic.
Yes, we want pageviews.
They make us feel all warm and fuzzy and proud. Like our hard work isn’t going unnoticed.
But they should not be the end-all, be-all.
Use them to see how you’re doing over last month. But don’t misunderstand numbers to be the Holy Grail, either.
Like this, for instance:
Looking at only this, you walk away feeling like a boss for all the numbers you’ve racked up. Seriously, I can’t even count that high.
But what about when you consider the bounce and exit rates for each of those pages? Are people staying? No? Color you embarrassed.
Are you still so excited by your thousands of pageviews if most of them left immediately?
Bounce rates are real. And you’ve gotta consider them when you are looking at your metrics.
They mean that people haven’t had the chance to interact with your soft micro conversions. They haven’t had a chance to activate.
So take a look at the big picture.
Are your blog posts and site pulling people in, but not making them stay?
This isn’t a horrible problem to have, because it’s a problem you can pinpoint.
The traffic is there. They just don’t really like what they are seeing once they get to your site. Which you can fix.
First, set up some events to get a better idea of what’s happening on your pages. Then, make sure you have actionable goals that will allow for movement you can track.
Or use the Kissmetrics’ Customer Engagement Automation tool to analyze what people are actually doing on your site and with your products. Then, you can interact with behavior-based messaging to keep them around longer. Or keep them coming back for more.
That way, you can increase conversions, engagement, and retention without the guesswork.
Just always remember that numbers don’t tell the whole story. Use them with a grain of salt and a little bit of context.
Lie #4. Deceptive A/B “wins”
I’m just going to be honest with you. Those A/B testing “wins” you just got? Don’t always have the best track record.
I’m sorry to be so harsh right off the bat. Sometimes the truth hurts.
What’s even more worrisome? Oftentimes, tests will look like they have succeeded. But that’s not always the case (or at least, not the whole picture).
Start with Google Analytics content experiments, instead.
You can use it to contrast your varying pages to see if there are any sizeable adjustments that cause positive changes.
Instead, it allows you to compare different page variations to see which ‘bigger’ changes result in improvements. Maybe this works a little better because it adds an extra letter– it’s an A/B/N test.
The problem with this test is when you get a little too grab-happy.
You can quickly and easily remove fields to get better results, for instance. A simple reduction of three fields will increase your conversions by 11%.
Or, you can take away specific conversion-busters like the need to add a credit card for a non-paying trial. Sure, this will up your “conversions.”
But remember how far that got you a few lies ago?
That credit card field you took away? It was a huge indicator for which of your customers will eventually buy. 50% of people who put in their credit card will end up converting. While only 15% will of those who don’t enter a credit card will.
And we’re talkin’, conversions-conversions here. Like, bottom-of-the-funnel, paying customer conversions.
Context is key when you are looking at analytics.
Don’t test landing pages or simple changes to fields while only evaluating the top of the funnel. Make sure you dig in to see how the changes affect the rest of the customer experience and journey.
To do that, use the Funnel Report so you can see exactly how top-of-the-funnel changes are impacting bottom-of-the-funnel sales.
Lie #5. Your channel source attribution
A Forrester Research study years ago found that 33% of all transactions of all transactions happened after new customers had gone through more than one touchpoint.
That number jumps to 48% when considering repeat customers.
The same report showed that paid search is the highest source of conversions.
Is it, though?
Or is it just the last point most commonly used before a sale?
Just because it’s the last one, doesn’t mean it’s the only one.
What other marketing tactics are working to increase growth? Forrester went on to declare that while email works for repeat conversions, social media brings in less than 1% of sales.
Ok. Then how do you explain SpearmintLOVE?
You know, the freaking baby blog that boosted their revenue by 991% in year using Facebook and Instagram.
The only reason I know about them? Because my wife has bought clothes from them. After discovering them on Facebook and Instagram.
One, simple Google graph puts this myth to bed. Fast.
If you look at the left side, or “assist interactions,” you’ll see that social channels will put new products in front of people.
As you move toward the middle, customers get more information about products and options using search. At the end, they’re on their way to the website.
Notice all the possible interaction options here. It’s not just the last-touch that brought the customer to the website. They can take many steps to get there.
Google Analytics has a few different attribution options built-in to help you change how conversions are assigned.
Image Source
These include:
Last Non-Direct Click: This will overlook Direct clicks and go to the channel used right before.
First Interaction: This uses the social or advertisement that got them to the website.
Linear: Here, each channel that a customer used before purchasing will get equal attribution.
Time Decay: This will consider the channel that was used immediately before conversion, rather than channels used in the past day/week/month.
Position: This model gives priority to the first and last channel used before conversion. Anything in the middle gets less attribution.
The depressing part, though?
There’s no right answer here. The attribution model you pick largely depends on your sales cycle, your customers, and even what specific objective you’re trying to figure out.
For example, if you’re spending a ton on ads, you might want to see how the First Interaction looks. Especially when using social ads that often bring people into your ecosystem for the very first time.
In other cases? It would be a terrible choice.
The trick is to know what you’re solving for, first. Then working backwards.
Conclusion
Data is important. It’s huge.
YOOGE.
But, be careful.
Google Analytics is a marvelous, cost effective, game changing tool.
However, it’s been known to lie a little from time-to-time. (Yes, we’re still talking about Google Analytics here.)
Remember that conversion results aren’t always spot on. Direct traffic data might not be correct. Vanity metrics aren’t everything. A/B results can fire off false positives. And last touch isn’t everything.
Uncovering biases is never fun.
But it’s the key to creating campaigns that actually achieve results.
Without just blowing a lot of hot air.
About the Author: Brad Smith is the founder of Codeless, a B2B content creation company. Frequent contributor to Kissmetrics, Unbounce, WordStream, AdEspresso, Search Engine Journal, Autopilot, and more.
5 Lies You Tell Yourself About Your Analytics (And How to Fix It)
0 notes
Text
5 Lies You Tell Yourself About Your Analytics (And How to Fix It)
Consulting data is good.
But being a slave to data, is not.
There is such a thing as being too data-obsessed. Confirmation bias pops up. And you miss the good, albeit, intangible stuff that comes along with your efforts.
The solution is to uncover those biases and misunderstandings that lead you astray.
It’s not easy. Or even intuitive. But it’s the only way to avoid these five analytics blinders.
Here’s how it strikes when you least expect it.
Here’s why you fall for it.
And here’s how to avoid it by bringing in other types of feedback and analysis.
Lie #1. Your “conversions” are flawless
You’ve got three AdWords campaigns.
The first brings in zero leads on $78 bucks spent.
The second brings in one at a cost of $135.31.
The third brings in two at $143.28 per lead.
Nine times out of ten, the campaign with more “conversions” is declared the winner.
But what do you really, truly, know about this scenario?
Which campaign is actually performing the best? Which is putting the most money back into your pocket?
There’s simply no way to tell at this point.
First and foremost, these “conversions” are leads — not closed customers.
Second, they might be for different products or services. So different average order values or LTVs come into play.
Third, this is nowhere close to statistical significance. For example, the third campaign has the most leads because you’ve spent the most money on it.
Not because it’s “better.”
What if you simply spend the same amount on the first two? What if you let them both get to around the same ~$150/per mark?
See what I mean?
Too many “what ifs” for my taste.
Yet this is exactly what happens inside any marketing department. The same end result pops up after each client or superior meeting.
Everyone points to the third campaign. It gets the adulation. It gets the increased budget. It gets the additional staff and resources.
So it becomes a self-fulfilling prophecy.
One solution to figure all this out is closed loop analysis.
Ideally, your goal is to match up the customer’s information (name, email, phone, credit card) to the lead data you’re seeing inside Google Analytics.
Haha — just kidding.
That would mean you were gathering Personally Identifiable Information, which is a big no-no in Google Analytics.
Do it and they’ll delete your account right away.
The simplest alternative is to just use a tool that gives you this power, without jeopardizing your data. Hint, hint.
Lie #2. Your “top” traffic sources
What are your top sources of traffic?
A quick glance inside Google Analytics usually tells you (1) organic search and (2) direct. Maybe a little (3) referrals thrown in for good measure if you got some press last month.
Here’s the problem.
Two of those three are legit. The other is not.
The problem is that your direct traffic isn’t, in reality, all that “direct.”
Technically, this should be the number of people typing in your website URL to the address bar and hitting “Enter.”
Instead, it’s a healthy mix of email, social media, and good ol’ organic search.
The bigger the site, the bigger this problem usually is.
For example, The Atlantic couldn’t account for or explain how 25% of their visitors came to their site.
One of the biggest publishers in the world. One of the most respected. Who gets paid based on the number of visitors and page views they get. Has no idea how a quarter of their traffic is getting to their site.
That ain’t good.
But how can you really tell where people are coming from, if most analytics programs can’t tell you with any degree of accuracy?
For instance, let’s say your new, fantastic-looking is about to go out.
It’s been given the green light. “Legal” gives you the A-OK.
But wait! You didn’t tag the promo links correctly.
Now, you’ve spent all that time on a campaign that won’t have anything to show for it, because the traffic you get will now end up in the dumpster pile officially known as “Direct traffic.”
This isn’t just an email. It affects each and every social message, press mention, and blog post referral, too.
It can even affect your organic search traffic.
Groupon found this out the hard way. Literally. By completely de-indexing themselves for a few hours.
What did those crazy couponers find? That nearly 60% of their direct traffic was actually coming from organic search.
Sixty-freaking-percent.
But don’t freak out just yet. There are solutions here.
First, you can use Google’s UTM builder to make sure you are properly tagging your links. This means any and everything you have control over.
Manually tag them before they head out the door, or copy & paste into a lightweight app like Terminus.
If you’ve got long, cumbersome URL, you can be pretty sure that any traffic to that page didn’t come from Direct traffic.
People aren’t going to remember it. Which means they aren’t going to just spontaneously type it in.
Instead, these peeps probably came from another place, like an organic search or email.
However, in the same breath, you can probably consider homepage traffic to be legitimate Direct.
So create a segment based on these URLs and traffic sources to pinpoint “Dark Traffic” in its tracks. And prevent it from ruining your data in the future.
Lie #3. Top of the funnel performance = results
Yes, we want traffic.
Yes, we want pageviews.
They make us feel all warm and fuzzy and proud. Like our hard work isn’t going unnoticed.
But they should not be the end-all, be-all.
Use them to see how you’re doing over last month. But don’t misunderstand numbers to be the Holy Grail, either.
Like this, for instance:
Looking at only this, you walk away feeling like a boss for all the numbers you’ve racked up. Seriously, I can’t even count that high.
But what about when you consider the bounce and exit rates for each of those pages? Are people staying? No? Color you embarrassed.
Are you still so excited by your thousands of pageviews if most of them left immediately?
Bounce rates are real. And you’ve gotta consider them when you are looking at your metrics.
They mean that people haven’t had the chance to interact with your soft micro conversions. They haven’t had a chance to activate.
So take a look at the big picture.
Are your blog posts and site pulling people in, but not making them stay?
This isn’t a horrible problem to have, because it’s a problem you can pinpoint.
The traffic is there. They just don’t really like what they are seeing once they get to your site. Which you can fix.
First, set up some events to get a better idea of what’s happening on your pages. Then, make sure you have actionable goals that will allow for movement you can track.
Or use the Kissmetrics’ Customer Engagement Automation tool to analyze what people are actually doing on your site and with your products. Then, you can interact with behavior-based messaging to keep them around longer. Or keep them coming back for more.
That way, you can increase conversions, engagement, and retention without the guesswork.
Just always remember that numbers don’t tell the whole story. Use them with a grain of salt and a little bit of context.
Lie #4. Deceptive A/B “wins”
I’m just going to be honest with you. Those A/B testing “wins” you just got? Don’t always have the best track record.
I’m sorry to be so harsh right off the bat. Sometimes the truth hurts.
What’s even more worrisome? Oftentimes, tests will look like they have succeeded. But that’s not always the case (or at least, not the whole picture).
Start with Google Analytics content experiments, instead.
You can use it to contrast your varying pages to see if there are any sizeable adjustments that cause positive changes.
Instead, it allows you to compare different page variations to see which ‘bigger’ changes result in improvements. Maybe this works a little better because it adds an extra letter– it’s an A/B/N test.
The problem with this test is when you get a little too grab-happy.
You can quickly and easily remove fields to get better results, for instance. A simple reduction of three fields will increase your conversions by 11%.
Or, you can take away specific conversion-busters like the need to add a credit card for a non-paying trial. Sure, this will up your “conversions.”
But remember how far that got you a few lies ago?
That credit card field you took away? It was a huge indicator for which of your customers will eventually buy. 50% of people who put in their credit card will end up converting. While only 15% will of those who don’t enter a credit card will.
And we’re talkin’, conversions-conversions here. Like, bottom-of-the-funnel, paying customer conversions.
Context is key when you are looking at analytics.
Don’t test landing pages or simple changes to fields while only evaluating the top of the funnel. Make sure you dig in to see how the changes affect the rest of the customer experience and journey.
To do that, use the Funnel Report so you can see exactly how top-of-the-funnel changes are impacting bottom-of-the-funnel sales.
Lie #5. Your channel source attribution
A Forrester Research study years ago found that 33% of all transactions of all transactions happened after new customers had gone through more than one touchpoint.
That number jumps to 48% when considering repeat customers.
The same report showed that paid search is the highest source of conversions.
Is it, though?
Or is it just the last point most commonly used before a sale?
Just because it’s the last one, doesn’t mean it’s the only one.
What other marketing tactics are working to increase growth? Forrester went on to declare that while email works for repeat conversions, social media brings in less than 1% of sales.
Ok. Then how do you explain SpearmintLOVE?
You know, the freaking baby blog that boosted their revenue by 991% in year using Facebook and Instagram.
The only reason I know about them? Because my wife has bought clothes from them. After discovering them on Facebook and Instagram.
One, simple Google graph puts this myth to bed. Fast.
If you look at the left side, or “assist interactions,” you’ll see that social channels will put new products in front of people.
As you move toward the middle, customers get more information about products and options using search. At the end, they’re on their way to the website.
Notice all the possible interaction options here. It’s not just the last-touch that brought the customer to the website. They can take many steps to get there.
Google Analytics has a few different attribution options built-in to help you change how conversions are assigned.
Image Source
These include:
Last Non-Direct Click: This will overlook Direct clicks and go to the channel used right before.
First Interaction: This uses the social or advertisement that got them to the website.
Linear: Here, each channel that a customer used before purchasing will get equal attribution.
Time Decay: This will consider the channel that was used immediately before conversion, rather than channels used in the past day/week/month.
Position: This model gives priority to the first and last channel used before conversion. Anything in the middle gets less attribution.
The depressing part, though?
There’s no right answer here. The attribution model you pick largely depends on your sales cycle, your customers, and even what specific objective you’re trying to figure out.
For example, if you’re spending a ton on ads, you might want to see how the First Interaction looks. Especially when using social ads that often bring people into your ecosystem for the very first time.
In other cases? It would be a terrible choice.
The trick is to know what you’re solving for, first. Then working backwards.
Conclusion
Data is important. It’s huge.
YOOGE.
But, be careful.
Google Analytics is a marvelous, cost effective, game changing tool.
However, it’s been known to lie a little from time-to-time. (Yes, we’re still talking about Google Analytics here.)
Remember that conversion results aren’t always spot on. Direct traffic data might not be correct. Vanity metrics aren’t everything. A/B results can fire off false positives. And last touch isn’t everything.
Uncovering biases is never fun.
But it’s the key to creating campaigns that actually achieve results.
Without just blowing a lot of hot air.
About the Author: Brad Smith is the founder of Codeless, a B2B content creation company. Frequent contributor to Kissmetrics, Unbounce, WordStream, AdEspresso, Search Engine Journal, Autopilot, and more.
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