I’ve written about getting products to market faster. Now, let’s look at making those products better.
Roadmap decisions are still too often based on intuition, customer anecdotes, or the loudest customer, rather than actual customer behavior. The result? A list of problems that often includes:
- Feature proliferation,
- Waste in R&D investments,
- Low adoption of newly launched capabilities, and
- Roadmap prioritization challenges.
Delivering better software products to your customers doesn’t need to involve these issues. The ability to determine what “better” rests in your customers’ evaluations of your products, and it shouldn’t depend on guesswork.
Most companies have amazing insights available to them via software usage data, but haven’t quite dug deeply enough to make the most of this valuable resource. Now’s the time to rely on usage intelligence, which helps companies drive better product, commercial, and customer decisions.
The Role of Product Usage Intelligence
By looking at depth of engagement, outcome achievement, and workflow dependence, you can build great products based on what your customers want. Product usage intelligence can inform:
- Features to build or retire,
- Which capabilities belong in premium editions,
- The AI investments that deserve funding, and
- Which customer segments merit focus.
Analyzing granular data provides clarity into real customer usage, such as feature-level adoption, engagement levels, and service denials. This informs where “better” can come from as part of your software monetization journey. This can strengthen product and renewal decisions based on how customers actively use the software, reduce churn proactively, and spot expansion opportunities.
What might you be missing?
A feature can be heavily used and still not deliver value. Consider what might happen when products are launched without relying on usage data. Would outcomes similar to these be worth it?
- A feature included in every demo may have very low adoption, only 8%.
- A capability that’s heavily used by power users becomes a premium tier candidate, but doesn’t get widely adopted.
- AI assistants generate high cloud costs, but low repeat usage.
Basic reports — for key performance indicators such as downloads, activations, and entitlements — only provide surface-level insights. They don’t illustrate feature-level consumption patterns, identify cross-sell and upsell opportunities that may entice customers to try more of your product portfolio, or capture indications from your power users about the features that drive the most value and that may be essential for upcoming roadmap decisions.
Chances are, you can dig deeper into the data already available to find more robust insights that provide value-driven product decisions. Feature-level usage and consumption trends are what must be evaluated. These data insights are better predictions of customer health, churn risk, and expansion potential. They can reveal unmet needs to highlight areas of product expansion, illustrate the features that are valuable to power users, and flag opportunities to improve product design, onboarding, and customer success prioritization.
This need is amplified in the age of AI. The expenses associated with offering artificial intelligence put pressure on revenue margins. Getting clarity into what’s used, AI’s value as part of the product, and pricing products accordingly is more important than ever.
Take time to consider what insights you’d like about how your software products are being used and what your customers want. Now’s the time to dig deeper to find that information.
Once you collect it, what do you do with software usage data?
Technology companies must move beyond static reports. Software usage data can help drive better product decisions if it’s used for ongoing analysis of engagement and consumption patterns.
Effective reliance on software usage data requires companies to:
- Look at usage signals, which can identify and provide early alerts about renewal risk and expansion opportunities.
- Prioritize organizational alignment, to make sure that product, customer success, and revenue teams are united around a shared view and understanding of customer behavior.
- Set up alerts or triggers, so that teams can avoid late responses, but act quickly when customers’ usage patterns change.
Benefits for Customers Benefit Your Bottom Line
Your customers are telling you what they value, want, use; where they’re abandoning your product; how much they use your product; what their usage patterns are (including for seasonal or project-based needs); and what value they are getting out of your product. We call these indicators “Value Signals”. When you listen to these value signals, it benefits both your organization and customers.
These insights benefit:
- Customer success: Data to strengthen adoption programs, with earlier visibility into renewals and churn risk;
- Sales: Data to inform expansion opportunities
- Product: Data-driven product and roadmap decisions, and
- Executives: Usage intelligence to guide investments
Dig into your software usage data now as the next step toward product improvements. The companies that win will be the ones that continuously align product investments, customer success efforts, pricing models, and AI investments with actual customer behavior.
