Data-driven customer insights: The key to successful product development

Despite this paradigm shift, our Digital Disconnect signature research found that only 37% of companies are collecting the data needed to understand what drives external/customer behavior. Time and resources are being wasted during product development if decisions aren’t informed by what customers actually desire. And in a time where customer loyalty is fickle, missteps can be costly.

Embracing this mindset helps ensure that products not only meet current customer needs but are also adaptable to future demands—which is essential for successfully building quality, scalable products.

Understanding customer behavior is essential

Developing a cycle to collect and act on customer behavior information is no longer just a recommendation but rather table stakes for product development. This information is the backbone for understanding user engagement and helping businesses ensure a deep, continuous understanding of customer needs. The companies that can do this well will be better positioned to create engaging, user-centric products that stand out in crowded, competitive markets.

This begins with data collection: understanding user preferences and patterns, monitoring product performance and errors, and identifying which moments matter most in the user experience. Companies must also develop processes for data analysis that enable teams to measure success and develop actionable insights that drive continuous product improvement.

These processes will work best when data collection is a key consideration in product development and not just an afterthought for improvement. To this end, products should be designed with telemetry data collection in mind.

While this piece focuses primarily on user data, which tells you what customers are doing, comprehensive customer behavior data also includes qualitative feedback, which helps provide context for why customers are making certain decisions or have certain preferences. Both must be leveraged to successfully paint a full picture.

Create a cycle for developing and testing hypotheses

Once data collection and analysis methods have been codified, the next step is to turn this data into insights. As the amount of data collected continues to increase, it can be difficult to find a starting point. One recommendation is to start by forming several hypotheses based on the initial product idea. For products already in market, and updated beyond the initial product idea, hypotheses could instead be based on the most recent round of updates.

User data can then be used to test the accuracy of these hypotheses and make informed decisions that align with customer needs. As these guess-and-test methods are refined, this process can help create more responsive and dynamic product development cycles.

This approach not only helps companies continue to meet their customers' ever-shifting expectations, but also helps mitigate risk when developing new features. Resources can be more efficiently allocated – with reduced risk of a misstep – when decision-making is reinforced with a data-backed rationale. And in the instances when a hypothesis fails, data can be used to quickly identify what went wrong and reduce the cost and time of recalibrating the next iteration of the product.

Turn insights into action

It can be especially rewarding when a new product hypothesis is validated by user data. But companies must also ask themselves: So what? When a hypothesis is confirmed, or is rejected and revised, what comes next?

Data-backed insights are next-to-meaningless without a plan to productize them. It is important to think about how this information can be woven into practical use. While this is primarily focused on the actual product, insights may also be used to inform other areas where customers are interacting with a company.

A real-world example: Customers aren’t always external

West Monroe has been working with St. Jude Children’s Research Hospital to redesign their intranet and enhance their employees’ digital experience. After the initial build, we gathered both qualitative and quantitative data via the following:

• Online employee survey
• 25 employee focus groups
• Intranet analytics

This helped us gain a holistic understanding of both employee sentiment and where time was being inefficiently spent, and we used this data to work through pain points when designing the next set of new features. By using data to ensure continued alignment, this project helped save 31,000 hours annually in time back to St. Jude’s—worth approximately $650,000 in staff time.

Identify and mitigate challenges with customer data

Developing data-backed methods for product development does come with challenges. A chief concern is maintaining data integrity: Insights are only as reliable as the data they come from, and mistakes made because of inaccurate data can be costly. Data privacy and security are also top-of-mind priorities for businesses, as companies in any industry don’t need to look far to find examples of data breaches that were costly in terms of time, resources, and company reputation.

To alleviate these challenges, companies should develop and ensure adherence to strict data governance processes and procedures that are followed at any point that data is being collected and used. These processes should be revisited and reviewed often, especially as new products are introduced.

Companies may also find it more challenging to find meaningful patterns in their user data as the amount of data being collected continues to grow. AI and machine learning technologies, such as predictive analytics and sentiment analysis, can help companies more effectively sift through massive amounts of data to find and analyze relevant, useful data points.

Conclusion

Collecting and analyzing customer data is not a “check-the-box” task; it’s an ongoing, iterative process. Companies that can stand up a process for data-backed decision-making are equipped to develop innovative products that are grounded in what users need and adaptable to new possibilities.

By embracing design strategies that are driven by user behavior, companies can ensure their products not only remain relevant in rapidly changing markets, but also resonate deeply with their customers.