Decoding User Behaviors to Create Customer-Centric Features

Insights/Action/Outcome: The Ironclad team thought users were coming to the Ironclad Repository to import contracts and create reports, but instead they wanted to engage more with searching for terms and clauses within their contracts—a feature that wasn't included in the initial activation metric. They adjusted the activation metric (the point of value at the end of a set of actions) based on user behavior, increasing the number of leads and seeing the activation rate jump 14%.

Product management is incredibly customer-centric. If you can keep a finger on the pulse of what users want, then you can deliver it to them. The only way to understand users is to drill down into their behavior, seeing how they interact with the product and specific features. The right tools can help product teams pinpoint specific user behaviors and react accordingly, while the wrong tools will keep you guessing.

Data-centric roadmap development

I’m a Senior Product Manager at Ironclad. We’re a digital contracting platform that makes it easy for businesses to make and manage contracts, as well as understand the data inside them. We built our service from the ground up to handle every type of contract, including legal, sales, marketing, procurement, HR, and financial agreements.

At Ironclad, we prioritize customer experience. Ironclad is a data-centric company of 500 people, and we use metrics to track everything we build. We analyze every feature to ensure its impact on specific measures, including time to value and daily and monthly active users. In addition to building new features based on customer requests and feedback, a lot of what we build is based on data and analytics where we optimize for a specific metric. Data is fundamental to refining and prioritizing Ironclad’s roadmap, and the data we get from Amplitude Analytics is key to understanding how we can deliver the greatest value to our customers.

With cohorts, we can better target audiences

Before I arrived, Ironclad had adopted Analytics as an additional tool for our product team. Unlike other platforms, Analytics gives us visibility into how customers use Ironclad. Instead of showing us raw data, Analytics aggregates user behaviors, generating an event stream that shows what customers are doing with our platform and how they’re doing it. I can see and understand how features impact users and the paths they take to complete specific actions, like creating a contract workflow. From an activation standpoint, I can see what users are doing the first few times they use our product to identify where activations and drop-offs happen.

I use Analytics’ cohorting feature to analyze onboarding and time-to-value flows in Ironclad. I segregate customers into three groups during pre- and post-sales: free trial users, self-start users who deploy Ironclad independently, and customers who have bought a service package or used our consulting services. I can then build dashboards to monitor specific behaviors and analyze use cases, leading to better features for each of these audiences.

These analyses help us understand what our customers want. One market segment we focus on—SMB and mid-market, or companies with fewer than 1,000 employees—are price sensitive and hesitant to enter into traditional sales negotiations. They would rather go the “self-checkout” route, trying Ironclad on their own and making a purchase with a credit card—mirroring a typical e-commerce purchase. We suspected this was the case, and Analytics confirmed our hunch, backing our guesswork with data.

In many ways, my experience with Analytics echoes our customers’ self-service journey. The platform is intuitive enough that people can start exploring without any training. After just an hour playing around in it, I could start building dashboards that track new features. The self-service element helped drive Analytics adoption within Ironclad, and over a dozen product managers now use the platform to back up strategic decisions with data—and correct any wrong assumptions.

For example, we initially optimized the Ironclad free trial for importing contracts and generating reports and insights. We set up activation metrics to track this activity and learned that our assumption was wrong. Instead, trial users were searching contracts for keywords, common phrases, and poorly worded clauses. This insight caused us to rethink and retool our activation metrics, helping us align our definition of success with the priorities that our trialers were showing us through the data. Doing this increased the number of leads and our activation rate increased by 14%.

Another use case for Analytics is to track results of our free trial experiments. These are hyper-iterative changes we make within our trial environment to optimize for user behavior while concurrently building an Analytics dashboard to test its impact on the user experience. Seeing what they’re doing is far more impactful and efficient than asking a customer to complete a survey or schedule an interview to get feedback. Our self-serve customers don’t even want to get on the phone, but we can still get insight into their needs, which is a big win for the team.

Ironclad uses Bento to onboard our self-service customers and walk them through our platform. Earlier this year, Bento announced an Amplitude integration to augment its internal analytics, allowing for a broader understanding of user behavior and impact across a wider set of data points. As a result, we now have more data about how users interact with our onboarding guide. We aggregated this information in Analytics dashboards to see where trial users are dropping off versus converting. We also use this information to monitor our free trial product funnel and make improvements in response to user behavior trends.

Customer-led success

Ironclad's Free Trial and Self-Start programs are built to help our customers be more successful more quickly. Analytics has shown us that the best way to deliver value quickly is by understanding how and why customers use—or want to use—our product. The insights Analytics provides are key in helping us define our activation metric. We’ll continue running experiments and tracking the results in Analytics to shape our broader strategic decisions in how we build products in response to customer needs and wants. We want to understand problems as they’re happening instead of dealing with their aftermath.

Many companies claim to be data-centric, but data alone is of little value. Ironclad is using Analytics to understand how our customers interact with our product so we can develop the features they will want to use. We have insight into the unknown and quantitative data that far surpasses anything we could learn from customer interviews and user surveys. We are meeting customers on their terms and supplying the products they’re asking for.