Sunday, July 27, 2014

A/B testing is like sex at high school

A few days ago I went on record saying that A/B testing is like sex at high school. Everyone talks about it, not very many do it in earnest. I want to follow up on the topic with some additional thoughts (don't worry, I won't stretch the high school analogy any further).

When talking to people about A/B testing I've noticed that there are four (stereo) types of mindsets which prevent companies from successfully using split tests as a tool to improve their conversion funnel.

1) Procrastinative

The favorite answer to suggestions for website or product improvements from people from this camp is "we'll have to A/B test that" – as in "we should A/B test that, some time, when we've added A/B testing capability". It is often used as an excuse for brushing off ideas for improvement, and the fallacy here is that just because the best way to test assumptions is an A/B test doesn't mean that all assumptions are equally good or likely to be true.

Yes, A/B tests are the best way to test product improvements. But if you're not ready for A/B testing yet, that shouldn't stop you from improving your product based on your opinions and instincts.

2) Naive 

People from this group draw conclusions based on data which isn't conclusive. I've seen this several times: Results are not statistically significant, A and B didn't get the same type of traffic, A and B were tested sequentially as opposed to simultaneously, only a small part of the conversion funnel was taken into account – these and all kinds of other methodological errors can lead to erroneous conclusions.

Making decisions based on gut feelings as opposed to data isn't great, but in this case at least you know what you don't know. Making decisions based on wrong data – thinking that you understand something which you actually don't – is much worse.

3) Opinionated

There's a school of thought among designers which says that A/B testing lets you find local maxima only. While I completely agree with my friend Nikos Moraitakis that iterative improvement is no substitute for creativity, I don't see a reason why A/B testing can't be used to test radically different designs, too. 

Designers have to be opinionated. Chances are that out of the 1000s of ideas that you'd like to test, you can only test a handful because the number of statistically significant tests that you can run is limited by your visitor and signup volume. You need talented and convinced designers to tell you which five ideas out of the 1000s are worth a shot. But then do A/B test these five ideas.

4) Disillusioned

The more you learn about topics like A/B testing and marketing attribution analysis, the more you realize how complicated things are and how hard it is to get conclusive, actionable data. 

If you want to test different signup pages for a SaaS product, for example, it's not enough to look at the visitor-to-signup conversion rate. What matters is the entire funnel conversion rate, starting from visitors all through the way to paying customers. It's well possible that the signup page which performs best in terms of visitor-to-signup rate (maybe one which asks the user for minimal data input only) leads to a lower signup-to-paying conversion rate (because signups are less pre-qualified) and that another version of your signup page has a better overall visitor-to-paying conversion. To take that even further, it doesn't stop at the signup-to-paying conversion step as you'll want to track the churn rate of the "A" cohort vs. "B" cohort over time.

If you think about complexities like this, it's easy to give up and conclude that it's not worth the effort. I can relate to that because as mentioned above, nothing is worse than making decisions which you think are data-driven but which actually are not. Nonetheless I recommend that you do use split testing to test potential improvements of your conversion funnel – just know the limitations and be very diligent when you draw conclusions.

What do you think? Did you already fall prey to (or see other people fall prey to) one of the fallacies above? Let me know!

Friday, June 13, 2014

Uber's Wonderlamp

Uber's uber large funding round has been the talk of the day in the tech community in the last week. And it should be, since it doesn't happen very often that a four year old company raises $1.2B at a $17B valuation. In fact, according to this Bloomberg story, Uber's new valuation sets a record for investments into privately-held tech startups.

When I first heard about Uber a few years ago, I didn't quite get it in the beginning. The traditional taxi system works quite well in Germany, and I thought that the advantage of using an app to order a cab as opposed to making a quick call wasn't such a big deal. Also, the expensive "private limo" service, which Uber started with in the beginning, didn't appeal to me.

After using mytaxi in Germany, I started to like the idea, but it was the launch of UberX and my recent two-months stay in San Francisco which turned me into a huge Uber fan. What is it that makes Uber so compelling? It's a number of smaller and bigger factors, which, combined with a slick mobile app, make Uber a highly habit-forming service:

  • Speed: In San Francisco, Uber has such a large number of drivers that no matter where you are in the city, it rarely takes more than 5-10 minutes until your car arrives. It happened to me several times that "my" Uber arrived in less than a minute because a driver was just around the corner, which gives you an Aladdin's wonderlamp feeling: You hit the order button on your phone, and almost instantly a car shows up to pick you up. 
  • Transparency: You get an ETA and you can watch your car on the map as it's getting closer to you, so you know pretty exactly when your car will arrive.
  • Price: The company's budget option, UberX, is cheaper than normal taxis.
  • Convenience: The fact that you only have to enter your credit card once makes the payment process extremely convenient and saves you a lot of time every time you arrive at your destination. Related to that, Uber has constructed its business model in such a way that the drivers aren't allowed to take tips, so you don't have to think about how much tip to give. That leads to another almost magical experience – you arrive at your destination and off you go. No waiting for your credit card to be processed or for the driver to look for change. You don't have to worry about getting a receipt neither, since a receipt is emailed to you after the ride. The driver stops and 5 seconds later you're out of the car. Brilliant.

Last but not least, virtually all of the drivers I drove with were very friendly and courteous. Maybe that was just professional friendliness in some cases, but my feeling was that almost all of them were very happy working for Uber and were genuinely trying to provide a great service (besides making sure that they maintain a great rating).

So Uber is great for riders, and based on what I know, it's good for the drivers, too. But is it also a great business? I think so. If a company delivers so much value to both sides of a marketplace, it can take a significant cut and acquire buyers and sellers profitably. I also think that although driver and rider loyalty might not be huge in principal (as this WSJ piece suggests), Uber will be able to create significant moat around its business through network effects and the building of its brand.

If Uber manages to sign up more and more drivers in an area (something which I don't doubt they'll be able to do), those magical moments which I described above – where your car arrives almost instantly – will occur more and more frequently. Competitors with less driver density won't be able to deliver the same level of uber user experience. In theory, an extremely well-funded competitor might be able to attack one of Uber's markets by offering both drivers and riders a much better deal. In practice that will be very, very difficult given Uber's lead and the quality of its execution. And the fact that Uber has now more than a billion dollars in its war chest won't make it easier.

Is Uber worth $17B? I don't know enough about the company to judge that, but what's clear is that Uber has a very realistic chance to revolutionize the worldwide taxi industry. What's more, Uber's long-term vision is much bigger. As Travis Kalanick puts it, they want to make "car ownership a thing of the  past", and my guess is they'll try to disrupt a few other industries (such as last-mile delivery) along the way. Huge congrats to Bill Gurley and his partners at Benchmark for betting on Uber early!

Thursday, June 05, 2014

Learning More About That Other Half: The Case for Cohort Analysis and Multi-Touch Attribution Analysis (Part 2 of 2)

Note: This is the second part of a post which first appeared on KISSmetrics' blog. The first part is here, and here is the original guest post on the KISSmetrics blog. Thanks go to Bill Macaitis, CMO at Zendesk, for providing extremely valuable input on multi-attribution analysis.

Multi-touch Attribution Analysis – Giving Some Credit to the “Assist”

Multi-touch attribution, as defined in this good and detailed post, is “the process of understanding and assigning credit to marketing channels that eventually lead to conversions. An attribution model is a set of rules that determine how credit for conversions should be attributed to various touch points in conversion paths.”

It’s easier than it sounds, and, since this is the year of the World Cup, let me explain it using a soccer analogy. Multi-touch attribution gives the credit for a goal to not only the scorer but also gives some credit to the players who prepared the goal. Soccer player statistics often calculate scores based on the goals and the assists of the players. That means the statistics are based on what could be called a double-touch analysis that takes into account the last touch and the touch before the last one.

Since the default model in marketing still seems to be “last touch” only, it looks like soccer has overtaken marketing in terms of analytical sophistication. :-)

Time for Marketing to Strike Back!

If you are evaluating the performance of a marketing campaign solely based on the number of conversions, you are missing a large piece of the picture. Like a great midfielder who doesn’t score many goals himself but prepares goals for the strikers, a marketing channel might not be delivering many conversions but could be playing an important role in initiating the conversion process or assisting in the eventual conversion.

This is especially true for B2B SaaS where sales cycles are much longer than in, say, consumer e-commerce. When you’re selling a SaaS solution to a business customer, it’s not unusual for there to be several touch points before a company becomes a qualified lead, and then many more before the lead becomes a paying customer. The process could easily look like this:

  • A piece of content that you produced comes up as an organic search result and the searcher clicks on it
  • A few days later, the person who looked at the content piece sees a retargeting ad
  • A few days later, she sees another retargeting ad, visits your website, and signs up for your newsletter
  • A week after that, she clicks on a link in your newsletter
  • A few days later, she receives an invitation to a webinar, signs up for it, and attends the webinar
  • After the webinar, she signs up for a trial
  • The next day, one of your customer advocates gives her a call
  • Close to the end of her trial, your lead does some more research, happens to click on one of your AdWords ads, and signs up for a paid subscription

If you look at this conversion path, it becomes clear that if you attribute the customer only to the first touch point (SEO) or to the last one (PPC), you’ll draw incorrect conclusions. And keep in mind that the example above is still quite simple. In reality, the number of marketing channels and touch points that contribute to a conversion can be much higher.

Data Integration in a Multi-device World

Maybe you use Google Analytics or KISSmetrics for Web analytics, for CRM, and Zendesk for customer service. If you want to get a (more or less) complete picture of your user’s journey, you need to get and integrate the data from all of the major tools you’re using and track user interactions.

A big complicating factor here is that we now live in a “multi-device world”. It’s very possible that the person in the example conversion path above used a tablet device, a smartphone, and two different computers to access your content and visit your website. Since tracking cookies are tied to one device, there’s no simple way to know that all of these touch points belong to the same person, at least not until the person registers.

Going deeper into the data integration and multi-device attribution problem would go beyond the scope of this post, but there’s a lot of valuable information available on the Web. And, please feel free to ask questions or share experiences in the comments section.

Toward a Better Attribution Model

The next question to tackle is how credit should be distributed to touch points in a conversion path. A simple approach is to use one of these rules:

  • Linear attribution – Each interaction gets equal credit
  • Time decay – More recent interactions get more credit than older ones
  • Position based – For example, 40% credit goes to the first interaction, 40% to the last one, and 20% to the ones in the middle

While using one of these rules is a big improvement over a “first touch only” or “last touch only” model, the problem is that all of the rules are based on assumptions as opposed to real data. If you’re using “linear attribution,” you’re saying “I don’t know how much credit each touch point should get, so let’s give each one equal credit.” If you’re using “time decay” or “position based,” you’re making an assumption that some touch points are more valuable than others, but whether that assumption is true is not certain.

A more sophisticated approach is to use a tool like Convertro, which takes a look at all touch points of all users (including those who didn’t convert!) and then uses a statistical algorithm to distribute attribution credit. The advantage of this approach is that the model gets continuously adjusted based on new incoming data. Explaining exactly how it works, again, would go beyond the scope of this post, but there’s more information available on Convertro’s website, and I assume there are additional tools like this on the market.

Is It Worth It?

Implementing a sophisticated multi-touch attribution model is obviously a large project, and so the next question is whether it’s worth it. The answer depends mainly on these variables:

  • Product complexity and sales cycle – The more complex your product and the longer the sales cycle, the more likely you are to have several touch points before a conversion happens
  • Number of simultaneous campaigns and size of marketing budget – The more campaigns you’re running in parallel and the more you’re spending on marketing, the more important it is to account for multi-touch attribution

While cohort analysis is something you should do as soon as you launch your product, I think multi-touch attribution analysis can usually wait until you’re spending larger amounts of money on advertising. Until then, spending too much money or time getting your attribution model right probably is not the best use of your resources. So, as an early-stage SaaS startup, don’t worry too much about it just yet. Just remember to take your single-touch attribution CACs with a grain of salt.

Wednesday, June 04, 2014

Learning More About That Other Half: The Case for Cohort Analysis and Multi-Touch Attribution Analysis (Part 1 of 2)

Note: This article first appeared as a guest post on the popular KISSmetrics blog. Thanks to Hiten Shah and Sean Work at KISSmetrics for publishing it. I'm republishing the post here as a series of two shorter posts, with a few small edits.

Anyone who has ever worked in marketing or advertising has heard the quote, “Half the money I spend on advertising is wasted; the trouble is I don’t know which half.” It is from John Wanamaker and dates back to the 19th century.

Fortunately, the industry has come a long way since then, and especially in the last 10 to 20 years, new technologies have made advertising more measurable than ever. However, there’s still a considerable gap between what people could measure and what they actually are measuring, and that leads to significant under-optimization of advertising and marketing dollars.

In B2B SaaS, which we at Point Nine Capital focus a lot of our efforts on, there are two techniques that I feel are particularly important but not used widely enough – cohort analysis and multi-touch attribution analysis. In this series of posts, I’ll try to provide a brief introduction to both methodologies and explain why I think they are so important.

A Quick Primer about Cohort Analysis

If you're a reader of this blog or know me a bit, you know that I'm a huge fan of cohort analysis and have written about the topic before. If you’re new to the topic, a cohort analysis can be broadly defined as a dissection of the activities of a group of people (such as users or customers), who share a common characteristic, over time. In SaaS, the most frequently used common characteristic for grouping customers is “join date”; that is, people who signed up or became paying customers in the same period of time (such as a month).

Let’s look at an example, and it will become much clearer:

In this cohort analysis, each row represents all signups that converted to become paying customers in a given month. Each column represents a month in your customer’s life. The cells show the percentage of retained customers of the respective cohort in the respective “lifetime month.”

So What?

Why is it so important to do a cohort analysis when looking at usage metrics or retention and churn? The answer is that if you look at only the overall numbers, such as your overall churn in a calendar month, the number will be a blend of the churn rate of older and newer customers, which can lead to erroneous conclusions.

For example, let’s consider a SaaS business with very high churn in the first few lifetime months and much lower churn from older customers – not unusual in SaaS. If the company starts to grow faster, the blended churn rate will go up, simply because the percentage of newer customers out of all customers will grow. So, if they look at only the blended churn rate, they might start to panic. They would have to do a cohort analysis to see what’s really going on.

What else can you see in a cohort analysis? Whatever the key metrics are in your particular business, a cohort analysis lets you see how those metrics develop over the customer lifetime as well as over what might be called product lifetime:

If you read the chart above (which I've borrowed from my colleague Nicolashorizontally, you can see how your retention develops over the customer lifetime, presumably something that you can link to the quality of your product, operations, and customer support. Reading it vertically shows you the retention at a given lifetime month for different customer cohorts. This might be called product lifetime, an, especially if you look at early lifetime months, it can be linked to the quality of your onboarding experience and the performance of your customer success team.

The Holy Grail of SaaS!

Maybe most importantly, a cohort analysis is the best way to estimate CLT (customer lifetime) and CLTV (customer lifetime value), which informs your decision on how much you can spend to acquire a new customer. As mentioned above, churn usually isn’t distributed linearly over the customer lifetime, so calculating it based on the blended churn rate of the last month doesn’t give you the best estimate. A better way is shown in the second tab of this spreadsheet, where I calculated/estimated the CLT of different cohorts.

A cohort analysis is even more essential when it comes to CLTV. Looking at how revenues of customer cohorts develop over time lets you see the impact of churn, downgrades/contractions, and upgrades/expansions:

This chart shows a cohort analysis of MRR (monthly recurring revenue) of a fictional SaaS business. As you can see in the green cells, it’s a happy fictional SaaS business as it has recently started to enjoy negative churn, which many regard as the holy grail in SaaS.

Still not convinced that you need cohort analyses to understand your SaaS business? :-) Let me know in the comments.

Thursday, May 15, 2014

It's a ZEN day!

Today is a very special day for me as as an entrepreneur and investor. About an hour ago, Zendesk went public on the New York Stock Exchange. The last time I watched an IPO so carefully was when, the company that had bought my price comparison startup, went public – almost ten years ago.

Here are a few visual impressions of my love affair with Zendesk, which began six years ago:

Huge congrats and thanks to the entire Zendesk team – I couldn't be more proud of you guys!

Wednesday, May 07, 2014

Three more ways to look at cohort data

I've just added three new charts to my Excel template for cohort analysis.

The first one shows the MRR development of several customer cohorts over the cohorts' lifetime:

Each of the green lines represents a customer cohort. The x-axis shows the "lifetime month", so the dot at the end of the line at the bottom right, for example, represents the MRR of the January 2013 customer cohort (all customers who converted in January 2013) in their 9th month after converting.
Here are some of the things that you can see in this chart:

The second chart is based on exactly the same data but shows MRR for calendar months as opposed to cohort lifetime months, and it uses a slightly different visualization:

One of the things you can see here is the contribution of older cohorts to your current MRR (something to keep in mind if you're considering a price increase and are thinking about the impact of grandfathering):

The third chart shows cumulated revenues minus CACs for different customer cohorts, i.e. it shows how much revenues a customer cohort has generated less the costs that it took to acquire the cohort:

The purpose of this one is to show if you're getting better or worse with respect to one of the most important SaaS metrics: The CAC payback time, i.e. the time it takes until a customer becomes profitable. Note that for simplicity reasons the chart is based on revenues. If you use it in real life, it should be based on gross profits, i.e. revenues minus CoGS.

What you can see here is that the first cohorts cross the x-axis (a.k.a. become profitable) around the 6th lifetime month, whereas newer cohorts are crossing or can be expected to cross the x-axis further to the left, i.e. become profitable faster.

If you want to take a closer look, here's the latest version of the Excel template, which includes the new charts. Or even better, download it and pay with a tweet! :)

Friday, March 14, 2014

Cohort Analysis: A (practical) Q&A [Guest Post]

My colleague Nicolas wrote a great guide with tips and tricks on how to do cohort analyses which I'd like to share with the readers of this blog. Thanks, Nicolas, for allowing me to guest publish it here. Without further ado, here it is!

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At Point Nine we believe that the only way to get a real sense of user retention and customer lifetime is doing a proper cohort analysis. Much has been said and written about them and Christoph has a published a great template and guide on the topic if the concept is new to you.

With this Q&A I want to focus on some of the more practical questions that might arise when you are actually implementing a cohort analysis for your startup. After close to two years of working with SaaS companies and doing numerous of these analysis I have learned that in most cases there is no perfect step-by-step procedure. But although you will always have to do some customisation for a cohort analysis to perfectly fit your business, there are a handful of questions and pitfalls that I have seen over again and again and want to share so that you can avoid them.

Now let's get into it!

Q: Which users should I include in the base number of the cohort?

There are two parts to the answer as it depends on what you want to measure. If you want to find out your overall user retention and have a free plan, then you should include all signups of a specific month.

However if you are trying to calculate your customer lifetime value, you should only look at the number of paid conversions. I only count an account as a paid one when the user has or will be charged for a period. So if you offer a 30-day free trial for example, wait to see if the user converts into a paying plan before you include him in the cohort. This way the numbers won't be biased with users that actually never paid for your service.

If possible without too much effort, you should also try to eliminate all 'buddy plans' that you have given to friends, your team or investors. If they are not paying, they are not representative for the real cohorts.

Q: How do I treat churn within the first / base month?

There are different approaches here, but in my view taking churn within the first month into account is the most accurate representation of reality. That means that in your first month the retention could be less than 100%, if people cancel their paid subscription within that month. It would look something like this:

I do this because I don't want the analysis to exaggerate churn in the second month and understate it in the first / base month. After all the reasons for churning in the first 1-4 weeks could be very different than after 5-8 weeks.

Q: Should I treat team and individual accounts differently?

If you are at a very early stage or sell mostly (90%+) individual plans it is probably sufficient to mix them all in the same analysis. But when team plans make up a significant part of your paid accounts, or your product has a very different user experience when a whole team uses it, you should probably look at both type of accounts separately.

Findings could include that team accounts are a lot more active, churn less and see a lower drop-off in the first month than individual plans. Or not. :)

Q: What about annual vs. monthly plans?

Again, if you are focusing on how active your users are over their lifetime it is OK to mix both plans. If you just want to see how many of the people that signed up still come back after X months, no need to split hairs.

If you are however focused on churn, you should only look at paid accounts that could have churned in that month. This is one of the 9 Worst Practices in SaaS Metrics and means that you should exclude all annual plans that are not expiring in the respective month. Including these in the denominator would otherwise skew churn numbers.

Q: Now that I have it, what can I take away from it?

The two most obvious take-aways are depicted in this (KISSmetrics) retention grid. Note that this is a most likely an analysis for a mobile app and the numbers for your SaaS solution should be significantly higher:

(click for larger version)

Moving horizontally you can see how the retention of a cohort decreases over the users lifetime. Interesting here is where the highest drop-offs occur and whether the numbers stabilise after a few months.

Vertically, you can (ideally) see how the retention of your cohorts change over the product lifetime. Assuming you are not twiddling your thumbs while catching up with House of Cards or sipping Mai Tai’s at the beach once your product launches, you should see an improvement in user retention with younger cohorts as the product improves. If this is not the case, you should consider whether the hypotheses or features you are working on are the right focus.

Most importantly though, this data will be the basis to give you a sense for your customer lifetime value (CLTV). If you take the weighed retention data for the 6th or ideally 12th month and extrapolate it, you will get an approximation for the average lifetime of your customers. Multiplying this with the average revenue per account (ARPA) or respective plan that you are looking at (e.g individual / team) it will give you your CLTV. This number is really the quint essence of the cohort analysis, as it gives you an idea about how profitable your business model is (=how much more money are you making with than what you are paying to acquire him). Subsequently it will also tell you the highest price you can spend on customer acquisition to grow profitably. It is important to note here that although super valuable, especially in the early stages of a startup this number will always be an estimation and most likely not 100% accurate. So keep in mind to continually track and fine-tune your CLTV calculations.

And one last thing: If you have accounted only for paid subscriptions as defined at the first question above, then the base rates of each month will also give you the most accurate number for paid customer growth and subsequently MRR growth. Two charts you will want to have at hand when talking to investors.

Q: Is that it?

For this post, yup! If you want to learn more about cohort analysis or SaaS Metrics, I would strongly suggest to check out Christoph’s and David Skok’s blog. And in case you have any questions on the above or something is unclear, feel free to ask away in the comments or send me a mail and I will do my best to answer you (or forward the hard questions to Christoph). ;)

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Like this post? Make sure you add Nicolas' blog to your reading list.

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