Quelle est la meilleure façon d’intégrer les commentaires des data scientists et des analystes dans votre feuille de route produit ?

🚀 Integrating feedback from Data Scientists and Analysts into your product roadmap is crucial for startup success! Before reaching out to your data team, clarity is key. Define your product goals and metrics for success. 📊✨ For instance, envision how Apple's visionary founder approached product development. He meticulously outlined the desired outcome and measurement strategies before engaging his team. By aligning their efforts with clear objectives, innovations like the iPhone were born. 📱💡 Remember, clarity empowers your data experts to provide actionable insights, shaping a roadmap that drives tangible results. 🛤️ Embrace this approach, and watch your startup soar! 🚀 #Startups #DataDriven #DecisionMaking

In my experience, embedding at least one core member of the data science team into the Customer Success organization is vital to the product roadmap and prioritization process. Not only does it enable the whole data science team to be a part of the Customer Journey but it reduces re prioritization of effort and thereby increases efficiency of execution (and spend!). When the Data Science team just “gets it” they get to yes much quicker and are excited to have direct customer feedback and validation for their initiatives as well. This is a total win-win.

At Array Insights, we integrate our data scientists' feedback into the product roadmap by involving them consistently through working sessions. The Array team prioritizes data-driven insights; therefore, we use our data scientists' expertise to help shape the Tallulah platform. This ensures their expertise shapes not only the current features, but the product roadmap effectively, aligning with our technical and business objectives of secure, insight-forward products.

Collecting feedback from the Data team helps in validating the product strategy. For example, for the product feature that I was working on requirements gathering and were unable to find any patterns for concluding that it will be a feature that will solve the problem for end users. So, I'd to involve data analysts in the process for the validation of strategy. It'll also be helpful to define product goals and KPIs for the product roadmap.

In my experience, data team should be leveraged before and after launching a product to continuously evolve the product to create a feedback loop. First, ensure we have the right metrics to decide what’s going to move the needle in terms of annualized revenue or whatever success looks like and also evaluate the LOE against it. After MVP, monitor the data to gauge the forecasted vs actual lift, what’s working vs not and decide on a second iteration of the product also keeping in mind user feedback. Launching with an A/B Test when possible is recommended so that based on the results from data team, either damage control can be done to stop the conversion impact as well as scale faster if the lift in CVR is really high.