The Point of Product Analytics Is Revenue (And Don't You Forget It)
This story is also published on Medium
At Heap, we spend lots of time talking about analytics. If you view our pages, you’ll quickly see the many advantages we bestow on teams: you can answer endless questions! Know what users do in your product! Collect behavioral data without relying on engineering! Pinpoint moments of friction in your user flows! And so on. (There are many more.)
What’s the point of these? Well, in the digital insights world, we often frame our goals in terms of “building a better product,” or “optimizing the user experience.” That’s great and all (it really is!), but here’s the thing: if you’re trying to sell your boss or exec team on the value of a tool like Heap, what matters aren’t these squishy goals. What matters are results.
After all, from the standpoint of the business, what does “building a better product” mean? The answer: it means building a product that more people use. Or one that people use more frequently. And by “use,” what we really mean is: will pay for.
In the same way we say that data isn’t useful on its own, analytics isn’t useful if it isn’t ultimately useful for the business. What this means for our sake: from a business perspective, a “better” product is one that generates more revenue. That’s the point of analytics: to generate more revenue.
Google “Product Analytics” and you’ll find lots of talk about measurement and understanding. That’s great, but …. what about business impact? What about financial goals? What about top-line revenue?
We think it’s time to change the discussion. If analytics should produce revenue, let’s figure out how. What follows is a start at outlining the paths that lead directly from analytics to revenue. If you’re a marketer or PM, here are the top-line benefits a tool like Heap opens to you. As we see it, these paths fall into four larger buckets.
Direct paths from analytics to revenue
This one is easy. Our eCommerce customers are already quite aware of this. For conversion funnels that have a sale at the end, knowing where your customers get stuck and helping smooth out the funnel is literally the key input to company revenue. (Other than what you’re selling, of course.)
The same is true for many companies in FinServ, Telco, Digital Health, and more — any industry for which getting people through a long and/or complicated application funnel is a key component of the business. Analytics gives you the power to understand where people are dropping off and why, and lets you test your experiments in fixing the problem.
One example, from Opploans’ Matt Gomes:
“When Heap showed us, with specificity, how the fourth step in our funnel was broken, we were able to make meaningful improvements. This resulted in a seven-figure lift in revenue.”
For businesses with revenue-producing conversion funnels, analytics is a top-line superpower.
If you want to learn more, read our Guide to CRO.
2. Retention, adoption, and reduced churn
Things are slightly trickier when it comes to retention. This is because analytics tends to tie less directly with retention (or reducing churn) than to the series of intermediate steps that work as leading indicators for retention, revenue, and business health. These are metrics like adoption rate, DAU/MAU, NPS, sessions per user, and the like. (Which ones you use depends on your business model.)
Does this matter? Of course not. As a PM (or other owner of a digital experience) you’re most likely to be goaled on metrics like these. As the owner of a feature or a set of features, it’s your job to create the roadmap and then collaborate with engineers and designers to drive adoption and deliver an experience that users love. That’s the ballgame. But let’s not forget: by “experience that users love” we really mean “an experience that users are willing to pay for, repeatedly.”
Under this bucket we’d also put expansion. The more and better features you can build, the more your team can sell expansions. What’s the best way to create more and better features? Yeah … analytics. (Well, that plus rigor, creativity, and hard work. But analytics certainly helps.)
To learn more, read our Book of Questions.
3. Time to insight / time to market
Here’s a scenario: after doing a bunch of customer interviews, you realize that lots of people drop out about ⅔ of the way through your application funnel. You dig in and find a bug in one of your forms. This bug, it turns out, keeps 23% of people who start an online application from completing it. Worse, you find out that this bug has been there a whopping 10 months.
You fix the problem and conversion rates improve. Good job! However … given the amount of revenue you lost over those 10 months, how much would it have been worth to find that bug, oh, 7 months earlier? (We’ll leave that question open for now.)
Here’s another example: you launch a new feature. After launch, you do focus groups and interviews, and after about 8 months you realize the feature is a dud. So you switch your efforts and build something else. By then it’s been 14 months.
The point? With analytics, you’d be able to dramatically shorten both time frames. Analytics doesn’t always give you the answers, but it gives you the information that helps you find the answers. What are people using? What are they avoiding? Where are they slipping out of your ideal user flows, and why?
Having this information is gold. What if you could reduce that 14 months down to 2–3? You’d have a revenue-generating product in market more than a year sooner. Or if you’d found that bug in 3 months? You’d have enjoyed increased conversion rates for an extra seven months.
Sure we know that time is money, but in these cases, time really is money. If you could increase revenue by 10%, and do it in 3 months instead of 9, wouldn’t you?
4. Improve efficiency
Ok, so this isn’t quite top-line revenue. It’s close. (Technically, increased efficiency qualifies as bottom-line revenue.) Whatever: it’s still real money. This is one of the ways Heap really shines. Our autocapture capability is literally the only solution on the market that can give you 1) a complete set of behavioral data with 2) no need for engineers. And it does this out of the box!
Not having to wrangle engineering time to manually code for every possible event that could be tracked is HUGE for PMs and other digital product owners. So is the ability to dig into product usage without having to wait for analysts to deliver reports. Take it from Redfin’s Nick Smith:
“Heap probably saves us 2–4 days for every single feature we roll out, and we’re shipping between five and ten features a month. And that’s probably just in people hours.”
Along these lines — cost savings — is eliminating calls to the call center. If your complicated user flows tend to provoke panicked calls into your help line, well, you’re in good company.
Again, analytics is key to fixing this. By improving conversion in any of these flows, you’re literally making it easier for people to do what they came to your site to do, and you’re saving money you’d spend staffing your call center.
To give only one example: Esurance was able to use Heap to reduce calls to their call center by 3%. 3% may not be an impressive number, but when you’re talking about thousands upon thousands of people, that’s enormous savings.
Why do these buckets matter? Because in our world of digital insights and behavioral analytics, we often lose sight of the larger point of our efforts. What’s that? Oh yes: running a successful business.
We’re busy building out calculators for all of these things. Expect beta versions soon. But in the meantime, feel free to memorize these four buckets and repeat them to your boss. Or your exec team. Because despite all of our efforts, sometimes “building a great product” comes down to “designing a machine that brings in dough.”