Unlocking Success: how behavioral data can supercharge your enterprise software development

This blog is based on our recent webinar featuring Craig Mirsky, Director of Product Management at S&P Global Market Intelligence and Rachel Obstler, Chief Product Officer at Heap.

Watch the webinar on-demand to see how product analytics have helped companies like S&P Global Market Intelligence transition to a data-driven culture.

If you’re designing enterprise software, you know the importance of giving users the simplest, happiest path through your product. Unfortunately, snags can happen - especially when user journeys get complex across multiple devices. 

Heap recently chatted with Craig Mirsky, Director of Product Management at S&P Global Market Intelligence, about how behavioral data is changing the game for enterprise software developers. These are the people responsible for keeping track of user journeys across multiple channels, products, roles, and personas. Because this can get challenging, it helps to adopt a data-driven approach when choosing how to enhance your product offerings. Thankfully, as Craig explains, you can do this by implementing robust product analytics!

The power of behavioral data

User behavioral data can offer both a qualitative and quantitative look at how well your product is working. Sure, you can interview users and see what they say about your product, but looking at what they actually do will reveal their true actions and preferences. According to Craig, this matters in today’s marketplace, because the cost of developing enterprise software that businesses rely on is high, and your product strategy can’t miss the mark. 

Building a complete picture of the customer journey

When trying to understand your users’ journeys, context is key. Who are these users, and what are the circumstances surrounding their actions? What are they trying to achieve?

Craig explains that, to give users what they want, you ideally need to understand the specifics of their needs—what features they’re accessing most in various areas of your product, how they’re using them, and why they matter. 

This is where product analytics come into play! The data they’ll capture will help you see where users are getting the most value out of your software. The more you can find behavioral patterns that correlate with positive actions that keep them coming back, the better you can iterate, improve their overall experience, and retain them as customers.

Informing the product roadmap

Gone are the days when you had to rely solely on verbal customer feedback to figure out what updates to build into your product roadmap. While qualitative insights are still important, product analytics now give you hard quantitative data that can instantly let you know where the pain points—or the most-used areas—in your software actually are.

With this information, you can prioritize which parts of your software to fix and which parts to build out. If you’re using the “WSJF” framework (“weighted shortest jobs first”) to do this, as Craig’s team does, good data will always help you determine which updates will have the most impact fastest.

Uncovering unexpected insights

Recently, Craig noticed that one particular area of S&P Global Market Intelligence’s software—a page that was complicated enough to be a mini-application—was getting a ton of user visits. In trying to figure out why, he used product analytics to discover something unexpected: that the part of the page gaining the most traffic was buried well below the fold (the part of the page users first see when it loads). 

Craig soon realized that users were accessing this page mostly to download publicly filed documents from companies, including 10-K and 10-Q forms. For S&P Global Market Intelligence’s clients, these documents are key when they’re needing to research companies. 

Ultimately, Craig realized that such an important feature shouldn’t be buried for users, who hadn’t brought up this pain point in qualitative feedback. With data insights gained through quantitative analytics, he was able to build a quick fix and fill the gap.

Democratizing data across the organization

Also top of mind for Craig is democratization of data across his organization, because ensuring that different teams have easy access to relevant information is key for their strategy development. It can even lead to “splash damage,” or cursory positive effects that might arise from sharing data across teams. 

This once happened when Craig helped bring insights from product analytics into scripts his QA (quality assurance) colleagues were writing for automation testing. They figured that if 90% of users do one particular task that a new update will affect, product analytics will show it. This turned out to be true, and it allowed Craig’s QA team to start incorporating behavioral data into scripts that would automatically test every touchpoint of their customers’ most essential tools before any updates were released.

Building a data-driven culture

Transitioning to being a data-driven culture can be challenging, and Craig is adamant that the change must start with company leaders. 

But how? Well, heads of product can ask teams to use data as a main part of their storytelling early in the prioritization and design processes. What can data tell them about why they should fix this problem for users first? What exactly will it solve? 

Using behavioral data can help teams make better decisions, always with end users in mind. This allows not just for hypothesizing based on more than mere speculation but also for follow-up testing to ensure that software updates actually are improvements that benefit users. The great part about product analytics is that user testing can begin as soon as a feature is released, which feeds the continuous iteration cycle.  

Data-informed experimentation and iteration

A/B testing is a common method of experimentation with software that involves releasing a new feature to a small segment of users and comparing its performance to the older version. During this process, product analytics can then tell you if users are using the new feature as intended and if it’s solving their problems. 

Prior to testing, it’s important to define questions and expected outcomes based on what you know about past user behavior. Once users are experiencing the new iteration, continuous data review can help you make informed decisions about whether to make further improvements or move on to new priorities.

– Behavioral data can empower you to take your product development to the next level by helping you make sound business decisions, understand your users, and continually improve your products. With it, you’ll be better equipped to meet users’ needs, drive innovation, and thrive in an increasingly competitive marketplace!