Audio: Data-Driven Product Roadmaps: How to Combine Data and Apply it to Product Strategy (Part 3)

Data-Driven Product Roadmaps: How to Combine Data and Apply it to Product Strategy (Part 3)

By Jomiro Eming


This is part three of a three-part series. Make sure you check out part one and two as well!

COVID-19 has had a major impact on companies around the world, and as a result many of them have been forced into survival mode. This has inevitably impacted how they think about their product roadmaps, and the gut instinct for many has been to save money wherever possible. Dimitra Retsina, CPO (a.i.) at ReceiptBank, however, has found that relying on gut feeling alone — especially during such uncertainty – won’t benefit your company in the long run. Instead, she relies on data to inform her decisions.

Here’s how she approaches working with data to build out sustainable strategies during this transition into what she calls ‘phase two of COVID-19’.


In the previous two posts, Dimitra unpacked how she figures out which data she needs, and how she approaches collecting and questioning it. This helps her make sure she’s testing her assumptions about the data, and removing any bias before using it to build product strategies. You can read about those in part one and two of this series.

However, once she has all the data she needs, the next step is for her to combine it together in a useful way, and then apply it to her product strategy.

Combining your data by merging it into one view

Data in isolation is less useful. This is why Dimitra consolidates her data into one view, so that she can use it to inform her product roadmaps and product strategies. In her experience, isolated data can tell you an incomplete story about your product, and thus could steer you in the wrong direction.

To illustrate this, she recalls an example from a past experience, where Company X was trying to build a strategy using only their financial data:

Company X’s goal was to save on costs, so they looked only at their product financials in isolation, and assessed discontinuing product add-on that was not profitable. However, what they weren’t doing was weighing that up against which features were used, which not, and the total customer value. Discontinuing such add-on could affect their customer base. Dimitra explains:

“It’s not just the financial value of that add-on; it’s the total value to the customer as well. It’s so important to get a range of insights, and partner with the rest of the organisation to navigate the realisation of those decisions intelligently.”

In order to combine data effectively, Dimitra uses tools like Data Lake or simple business intelligence (BI) tools to bring all of the data into one place. This lets her extract a variety of insights, side-by-side, and be able to make direct comparisons but also assess howone data set might affect another.

She says that there are many other BI tools out there you could use. What’s most important is to find a way to aggregate different data sources in one central place. Then, depending on what the problem is you need to have an answer for, start combining the different contexts to derive different insights.

Applying data to your strategy through conversation with the broader team

At the end of the day, how you apply your data to your roadmap doesn’t have a one-size fits all approach. Dimitra says that over-and-above the value that data has for building robust and adaptable product strategies, having conversations with her teams has been one of the most useful development exercises of this process.

A data-driven approach to product strategies enables her teams to have useful conversations not only about the decisions they make, but — on a more meta-level — have useful conversations about how they make decisions in general.

To illustrate what she means by this, Dimitra explains how bringing data to the table has sparked conversation that eventually changed the outcome of their strategy for the better:

“I recently had this conversation with one of my product managers: He was initially prioritising a certain set of initiatives, but when he did his business case analysis he understood that his priorities needed to be completely different — which was totally a different outcome from what he anticipated in the beginning. That sparked a lot of conversation as to how we go about making decisions, and that’s an invaluable and a healthy conversation to have within any product organisation.”

If you have any questions about building a data-driven product strategy, feel free to reach out to Dimitra on LinkedIn. And, if you missed part one and part two of this series, make sure to check them out as well!

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