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.

1 comment:

Alon said...

Important post. I've been contemplating this issue myself and from asking around analysts and marketing execs in a few respectable companies it seems like awareness to this problem is not as high as it should be. I'd like to add another dimension of complexity to the problem. From what I've seen in my analysis, the multi-touch journey also spans across several people, not just the same person. For example, someone can see a PPC ad and go to our site, realize he's probably not the person with the pain point or the one that should lead evaluation and implementation of the product, and before closing the tab, he'll grab the link and share it with a colleague/manager in an email.

Another issue is the time different between the touches. Sometimes prospects come back 6 or 12 months after the initial touch. Should you disregard the first touch altogether? What if it was 30 days instead of 12 months?

Thanks again for the post. Would be great to see a discussion going around the issue.