This is part two of a three-part series. Make sure you check out part one and three) 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 a sustainable strategy during this transition into what she calls ‘phase two of COVID-19’.
Collecting your data effectively
After identifying the data she needs from Product Financials, Product Usage, and Effort investments, Dimitra starts gathering it together. Two important parts of her process are collecting the data, but also then questioning it.
These two parts of her process help her build trust — with her data, as well as with her team. She can’t trust her decisions if she can’t trust her data: “I need to make sure that I’m certain about the correctness of my data; otherwise, whatever decisions I’m making will be questionable.”
Her two-step process here is as follows:
Collect the data
The approach Dimitra uses to collect her data can be qualitative or quantitative, and will depend on the kind of insights she’s looking for.
For example, if Dimitra wants information on user behaviour for her Product Usage data, she uses both qualitative and quantitative research: “You would very often have some form of qualitative research already in early stage startups — for example often CEOs go out and speak to customers directly. But there are also many user behaviour tracking software solutions out there [for quantitative research].”
For these tools, she uses apps like Amplitude and Mixpanel, and also sets up a data hub like Segment that feeds into both of those tools, and many more.
This quantitative data is useful because they can be used to get insights to drive decisions, but they can also be used to deliver functionality in her product.
That said, not all the data Dimitra needs is generated from the product. For other (sometimes qualitative) data, she collaborates with people from different departments in her organisation: “The product will not generate all the data that drives those kinds of insights. So, I partner with finance, with customer success, with support, and make sure that I bring all the alliances to the table, and plan how to contribute to those insights.”
Question the data
In Dimitra’s experience, critically assessing the quality of her data before using it is equally important to collecting it effectively. This is because she needs to question her assumptions about the data to make sure she doesn’t see it through a biased lens:
“You should always question the correctness of the data and find ways to QA as the data comes together. First figure out where you’re making assumptions, and then decide if your insights about the data are ones you can trust.”
A useful way of questioning data is to apply the same data to different contexts, and see if it shows you different things. When she starts combining the data in the next step, doing this, helps her see potential blind spots in the data: “There is so much to gain from looking at data with a different pair of glasses. When you combine the context with the data, you get a much better decision-making process.”
Once she has collected and questioned all of her data, Dimitra can start pulling it together. Combining the data and applying it to her product strategy is the next and final step to building a more sustainable product operations strategy, that is more resilient to phase 2 of COVID-19.
Missed part one? Check it out! Otherwise, carry on to part three!
