KISSmetrics vs Mixpanel

While trying to decide on KISSmetrics or Mixpanel, I decided to write a blog post about it since I’m guessing other people are asking the same question. I am not in any way affiliated to either of them.

Analytics Impact is all about converting data into actionable insights. Though in order to order to find good insights you need to have the right data and be able to easily slice and dice the data as needed.

Google analytics can usually give you 90% of the “right data” for most sites, but it has a few major shortcomings that truly limit it when trying to use it to gain insight for a SaaS site.

  • It does not allow you to track data down to the individual visitor across visits
  • It doesn’t have time based cohort analysis

As I am now in charge of a SaaS site, I found myself needing answers to questions Google Analytics just couldn’t answer. I know there are free add-ons and work-arounds that could handle most of my needs just with Google Analytics, but I would rather pay a reasonable monthly fee than spend hours gluing everything together, and even then I wouldn’t have an easy to use reporting solution. I know because I’ve done it in the past.

What I need is a system to fully understand what visitors are doing on my website and then continue to track them when they sign up for a free account and ultimately become customers. Once they are customers I need to understand how they are using my SaaS site (what features they are or aren’t using) and why we lose customers.

I’ve been using web analytics for a while (even before Urchin became Google Analytics) so I already knew what my shortlist was for my needs:

KISSmetrics or Mixpanel

Let me start by saying that both of them are excellent choices. Neither is “better” in the absolute sense, but I need to decide on one or the other so I started looking deeper into which one would better meet my needs.

I found an excellent blog posting on this exact topic by Sacha Greif

A great read but with one major problem. It’s from March 2012. I know that’s just 8 months ago, but a lot has changed since then.

Here’s a request for both KISSmetrics and Mixpanel. Please provide a simple “changes.txt” type page that easily shows me what’s changed over time. That way if I read an old product review (like this one will be in a year) I’ll be able to easily see what’s changed. Mixpanel kinda has something like this for major changes on their about page.

Back to the comparison. I personally don’t need real-time data so I’m fine with KISSmetrics not being real time (though debugging can be a pain).

Since I really need to easily be able to look at individual user history I was originally leaning towards KISSmetrics as I thought Mixpanel doesn’t support this feature. I shortly found they do but only introduced the feature in July 2012 as a paid add-on.

I wonder why the “people feature” isn’t linked from the main site. If anything it makes the pricing page a bit confusing since they talk about the people plan add-on but don’t provide any further details.

As an ex-coder I must say the online documentation for KISSmetrics seems more comprehensive than the Mixpanel documentation. I was also surprised that Mixpanel doesn’t even link to their documentation from the main site (it’s at ). KISSmetrics has it linked from the footer at

Next I wanted to look more into revenue reporting. I’m guessing that you can store revenue just like any other number in Mixpanel, though I’m a bit concerned that revenue isn’t mentioned anywhere on their site or their docs (I searched).

KISSmetrics on the other hand talks about lifetime value on their homepage and even has a revenue report as I found in their docs.

At this point I was just about to go with KISSmetrics when I stumbled across Mixpanel’s new Engage feature: Basically you can now send targeted emails or notifications with Mixpanel’s targeting criteria.

This is the kind of feature that was science fiction (for an analytics service) a few years ago. It’s interesting to see analytics and marketing automation services like Marketo or Eloqua really start to overlap.

I’m betting than in a few years we’ll see content targeting as an additional feature so you’ll also be able to easily show dynamic based on user behavior (though this has existed for a while as stand-alone products)

BTW, I came across which seems to be very similar to Mixpanel and KISSmetrics though it heavily promotes their email integration as one of the main features (rightfully so). They are pretty new (April 2012) but I’d keep an eye on them.

I also wanted to mention which seems like a no-brainer if all you want is very smartly targeted emails.


I just wanted to include some other services that look interesting and worth looking into for SaaS based analytics: looks interesting as well. It’s laser focused on SaaS sites which I like. Very strong in natively identifying the type of real world data I’d want to look at (ie customers at risk of leaving). It does seem a bit behind in terms of reporting (I didn’t see any time based cohort analysis). Also no pricing info on their site though they were very responsive when I contacted them (a good indicator that they value good customer service).

I’d love to hear your thoughts – KISSmetrics or Mixpanel and why!

Are you making this common split testing mistake?

I was reading a simple case study today.

They were testing two different versions of a banner that was advertising a webinar.
One of the banners had an image of the presenter, while the other did not.
The banner without the image of the presenter won (by over 50%).

One of the comments was something along the lines of:

I guess this audience prefers banners without an image of a person.


If you don’t immediately realize the mistake the commenter made, don’t feel bad. It’s a very common mistake.

Beyond the fact that a specific banner (which did have have an image of the presenter) won over a different specific banner (which did not have an image of the presenter) you really can’t be sure of anything.

The loosing banner might have won with:

  • An image of a different person
  • A different image of the same person
  • The same image of the same person in a different position or size on the banner.
  • The same image of the same person in the same position and size but with different elements on the banner changed.

The point is:

Don’t jump to generalized conclusions based on the outcome of a specific experiment.

Should You Test or Target?

Recently I’ve been hearing more and more online buzz about the benefits of delivering targeted content to your visitors. In simple terms this means a customized message based on information you know about the visitor (opposed to a generic message which all visitors see).

A simple example would be adding a message for international visitors that your site ships to their country. Something more complex would be a 20% discount on ink cartridges for customers that purchased a printer in the past year but have not purchased any ink in the past 90 days (and of course the message would include the name of the printer they already purchased).

Serving up targeted content is indeed a valuable tool which I have used for many of our clients (I work for Adobe), though I invite you to take a step back and look at the greater question:

What content on my website will bring me the best results?

Intuitively it makes sense that targeted content will resonate better with visitors, and ultimately get more sales (or leads, etc).

On the other hand, you can simply test changes on your site which will effect everyone in order to try to improve your conversion rates.

Both are valid methods for optimizing your site and in an ideal world your company would be doing both.

In reality though, you have limited resources to improve your online marketing efforts and you’ll need to prioritize how much targeting you’ll do and how much user experience (common content) testing you’ll do.

Based on my personal experience, most websites still have huge room for improvement by simply optimizing the user experience through split testing. I’ve discussed this with a few other conversion rate professionals who agree. Just look at the case studies out there and you’ll see dozens of examples of how making relatively simple changes to your website can increase conversion rates by double digits.

In other words, you should initially focus on improving the common user experience and then test and test and test and then test some more. Only then does it make the most sense to start targeting (and of course test to see what targeted message performs best).

If you’re site sucks, it will still suck with targeted messaging.

I will add though that some targeting opportunities are very low hanging fruit and I would implement them without even testing. For example any traffic that you are sending to your web site and know what they clicked on to get there (search, display, email, etc) make sure the main message on the landing page is the same as the message they clicked on to get there.

I’d love to hear your targeting success and failures (and I’ll even provide feedback if you want).


What makes a world class conversion optimization organization?

I’ve been thinking about what makes a world class conversion optimization organization for the past couple of days and have come up with what I think are the top 6 criteria.
I wasn’t shooting for 6 but it seems to cover all bases. I’d LOVE to hear your thoughts.

  1. Optimization is embedded in planning, process and corporate culture at all levels.
  2. Optimization efforts are prioritized based on maximum increase of revenue/goals.
  3. Optimization is executed for the entire end-to-end user experience across all lines of business.
  4. Optimization is based on analytical data, previous learnings and best practices.
  5. User experience is targeted to individual visitor or group.
  6. Optimization process itself is efficient (optimized).

In a bit more detail:

1 – Optimization is embedded in planning, process and corporate culture at all levels.
This means two things:
– There is full buy in from the executive team and every employee is on-board and understands that optimization is a commitment, not an add-on.
– All relevant internal processes take into account the opportunity to optimize. Testing is part of the standard process and budget.

2. Optimization efforts are prioritized based on maximum increase of revenue/goals.
What to test (both in terms of where on the site and which page elements) is based on where it makes the most business sense (based on numbers and research), NOT internal politics or personal opinion.

3. Optimization is executed for the entire end-to-end user experience across all site sections.
End-to-end means looking at both off-site (paid search, display, email, etc) and on-site opportunities as well as the making sure the “funnel” starts before they land on your site (ie. how does the messaging in your paid ad match the experience on the landing page).
Across all site sections applies to sites which have multiple competing goals or categories. For example product sales vs. consulting services. The goal is to maximize overall company revenue even if a large lift in one area causes a small decline in another. This also means cross section targeting.

4. Optimization is based on analytical data, non analytical user data (think personas), previous learnings and best practices.
This includes:
– Figuring out where and what to test (what the numbers are telling us)
– Visual site/page review (what is the user experience?)
– What do we know about our visitors (who are they? what makes them tick? what are they truly looking for?)
– What did we learn from previous tests? (Layout X performed better than layout Y on the shirts page).
– Are we just guessing to create challenger experiences or applying best practices (while still keeping an open mind).

5. User experience is targeted to individual visitor or group.
Serving up the same experience to all visitors will only get you so far (even if it’s optimized). I call this “lowest common denominator” optimization. Are you taking advantage of CRM type data (what did they buy in the page) and anonymous data (traffic source, search terms, geo-targeting, visit number, etc).

6. The optimization process itself is efficient (optimized).
It takes a while for the optimization process to go smoothly for all tests. Like anything new it takes a while for all of the parts to be in sync.

I can’t help but thinking a 7th bullet point would make a nicer headline (7 always sounds sexier than 6).
Any thoughts on what to add?

Thanks in advance,

New Features You Need on Apparel Product Pages

A couple of weeks ago I was looking to buy a new spring jacket. While there are plenty of options online, I ultimately made an order based on two features on the product detail page:

  • A video of the product
  • The height and weight of the model as well as the size they are wearing

A couple of examples to see this in action:

The Saks page has the model height and product size on the page:

Saks page with model height and dress size


while the Altec page only has it in the video: product video with model height & weight and product size


I’ve been saying to myself for years that these really should be must-have features for any apparel product detail pages. Just having a picture of the product doesn’t cut it anymore, especially if your competitors are doing it.

This is also an excellent opportunity for clothing manufactures. Creating a video for every product would be cost prohibitive for some smaller online retailers who could use the assets created by the manufacturer.

Are you aware of other examples of providing model height & weight and product size pictured?

More importantly, has anyone tested this? :)

Let me know,

Test Fatigue – Why it Happens

First of all super thanks to all of the great comments on my previous post about Test Fatigue. If you didn’t read my previous post or you don’t know what I mean by Test Fatigue, then please go ahead and read it now. I’ll wait.

Now, to the point – why do we often see the lift from a challenger in a split test decrease after it seems to be going strong and steady?

Statistical significance is for the winner, not the lift.
First and foremost, most split testing tools (I’ve only used Test&Target and Google Website Optimizer extensively) will provide a confidence level for your results. If the control has a conversion rate of 4% and the challenger a conversion rate of 6% (a 50% lift) with a 97% confidence level, the tool is NOT telling you that there is a 97% chance that there will be a 50% lift. The confidence level is referring to the confidence that the the challenger will outperform the control.

You don’t have enough data and there are many variables outside of your control.
We tend to think that in a split test all variables other than the visitor being presented with the control vs. the challenger are identical. In reality there are many external variables outside of our control, some of which we aren’t even aware of. All things being equal, we often see fluctuations in conversion rates even when we don’t make any changes in our site. Meta Brown provided some excellent points in her comments in my previous post.

Results aren’t always reproducible. Learn to live with it.
Lisa Seaman pointed out an excellent article from the New Yorker magazine about this very same phenomenon in other sciences. This is a must read for anyone doing any type of testing in any field. Read it. Now: The Truth Wears Off

What was especially eye opening for me was this part of the article (on page 5). Here is a shortened version of it:

In the late nineteen-nineties, John Crabbe, a neuroscientist at the Oregon Health and Science University, conducted an experiment that showed how unknowable chance events can skew tests of replicability. He performed a series of experiments on mouse behavior in three different science labs: in Albany, New York; Edmonton, Alberta; and Portland, Oregon. Before he conducted the experiments, he tried to standardize every variable he could think of.

The premise of this test of replicability, of course, is that each of the labs should have generated the same pattern of results. “If any set of experiments should have passed the test, it should have been ours,” Crabbe says. “But that’s not the way it turned out.” In one experiment, Crabbe injected a particular strain of mouse with cocaine. In Portland the mice given the drug moved, on average, six hundred centimetres more than they normally did; in Albany they moved seven hundred and one additional centimetres. But in the Edmonton lab they moved more than five thousand additional centimetres. Similar deviations were observed in a test of anxiety. Furthermore, these inconsistencies didn’t follow any detectable pattern. In Portland one strain of mouse proved most anxious, while in Albany another strain won that distinction.

The disturbing implication of the Crabbe study is that a lot of extraordinary scientific data are nothing but noise.

So there you have it. While I know you really want a silver bullet that will make your positive results always stay the same, reality isn’t so simple.

They say that conversion optimization is part art and part science, but I think we have to accept that it’s also part noise :)


Test Fatigue – Conversion Optimization’s Dirty Little Secret

I’m going to expose to you a phenomenon that’s fairly common when split testing, but no one seems to be talking about it (other than veteran split testers) and I don’t think it’s ever been blogged about (please add a comment if I’m wrong).

It has to do with the question:
“Will the lift I see during my split test continue over time”?

Let’s start by looking at a scenario commonly used by practically everyone in the business of split testing.

Your web site currently is currently generating $400k a month is sales which has been steady for the past few months. You hire a conversion optimization company, which does a split test on your checkout page.

After running the test for 3-4 weeks, the challenger version provides a 10% lift in conversion and RPV at a 99% statistical confidence level. The conversion rate company turns off the test and you hard code the winning challenger.

First of all – Wooohoo!!! (Seriously, that’s an excellent win.)

A 10% lift from $400k a month is an extra $40k a month. Annualized that amounts to an extra $480k a year. So your potential increased yearly revenue from using the winning checkout page is almost half a million dollars. Sounds pretty good to me.

Here’s the problem.

All things being equal, by using the winning version of the checkout page and not your old checkout page, there is a good chance you won’t be making an extra $480k in the next 12 months.

Don’t get me wrong. You will indeed be making more money with the winning checkout page than with the old one, but in all likelihood, it will be less than simply annualizing the lift from during the test itself.

The culprit is what I like to call “Test Fatigue” (a term I think I just coined).

Here’s what often happens if instead of stopping your split test after 3-4 weeks you could let it run for an entire year. There is a phenomenon that I’ve often, but not always seen with very long running split tests; after a while (this might be 3 weeks or 3 months) the performance of the winning version and the control (original) version start to converge.

They usually won’t totally converge, but that 10% lift which was going strong for a while with full statistical confidence is now a 9% lift or an 8% lift or a 5% lift or maybe even less.

As I mentioned before this doesn’t always happen and the time frame can change, but this is a very real phenomenon.

Why is does this happen?

Please read my next posting – Why Test Fatigue Happens where I provide some explanations on why this happens.

Also, I’d love to hear if you have also seen this phenomenon with your own tests and what your personal theories are as to why it happens.