Software Usage Data and What to Do with It

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Click to learn more about author Victor DeMarines.

April 13 marks the day of one of the greatest success stories I can think of that began with the unthinkable. The crew of Apollo 13 was completing a routine check when, a day shy of walking on the moon, an oxygen tank exploded on the spacecraft and forced the men into what seemed an impossible race to get back to earth safely.

In one of the scenes I most remember from the movie, several NASA engineers and a doctor rush to flight director Gene Kranz (or Ed Harris if you’re picturing this in your mind) to inform him that the crew’s CO2 levels were dangerously high. Simply put, there wasn’t exactly the right equipment on board to solve the problem: the filters on the lunar module (where the astronauts are) were round, and ones that could be used as backups from the command module were square.

“This just isn’t a contingency we planned for,” the engineer tells Kranz.

I think it’s something we can relate to in product management. It’s hard to plan for every way in which our products will be used by customers – or every possible contingency that will prevent them from deploying it in the ways we intended. But it sure would have helped if the people building the command module had greater visibility into how the systems designed for the lunar module would actually be used.

In a similar (albeit much less intense) manner, if product management can’t see that one of the key modules isn’t really being used by customers, it can’t inform marketing campaigns and sales strategies that might encourage said adoption and spur further success for the release.

Software Usage Analytics enables visibility and alignment. Having insight into the ways in which our products are used can help everyone across the spectrum of product development and delivery work together to achieve the same goal – software that is relevant to what the customer is trying to do.

Let’s look at a common scenario, and how access to Usage Analytics benefits different roles within the organization, while helping to achieve end-to-end visibility.

You’ve just launched a free, two-week trial version of your software. Just before that time period is up, marketing will reach out to users with an offer to purchase the full version. But the offer is falling flat, and it’s difficult to determine why, and as a result, adjust strategies for this contingency.

Now, consider this scenario with Usage Analytics. You launch the same, two-week trial, but after two days of downloads by customers, you start to notice a pattern. Runtime analysis shows that product use drops off after about an hour, at which point users seem to walk away. Drilling further into the anonymous data, you can see the specific behavior of those users – time spent in the application, functions accessed, and more. This data lends visibility into something absolutely crucial – users aren’t able to properly use the software because they’re getting stuck on something in the configuration wizard tool, and dumping the product as a result.

There is another possibility here – that users like the software so much, they’re looking for cracked versions to hang on without paying a bit longer. A usage intelligence strategy focused on license compliance will lend insight into where that is occurring, and inform a strategy around it.

Armed with this information, those across the spectrum of product development and delivery can make several key changes that save the release. Engineering can improve the usability of the configuration wizard. Marketing can then communicate with the impacted user segment with carefully crafted messaging that boosts adoption of the new trial. Sales reps have a host of qualified leads to follow up on to boost conversion. Or, in the event that piracy seems to be the issue, compliance has qualified information to recover revenue.

Let’s take one more step, and consider that Usage Analytics was leveraged in the creation of the product itself. It’s likely that in testing this product, engineering reached out to customers with which it has strong relationships. Engaged customers are often super users, who don’t necessarily represent the technical capabilities of the entire user base or prospects. Because of their prowess, they might not have seen a problem in beta tests they participated in, which of course informed the release.

Now consider combining Usage Analytics with in-app messaging to anonymously invite a wider range of customers to participate in beta trials based on usage profiles. They are more likely to respond to that invitation because they’re engaged with the software and you can automatically collect metrics with Usage Analytics that can be segmented by usage profiles. In such a way, the problem with the wizard can be identified even before the final product is released.

Visibility across the product makes the jobs of everyone easier – especially for the person you should care about most, the end user, so we’re not forced to develop stopgaps, and call on anyone to fit square pegs in round holes.

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