The six things you need from a great product data scientist and how to find them
Product Analytics is one of the most high leverage roles in a company that’s investing in software development. Having millions of touch points with your customers gives you a plethora of opportunities to learn from mistakes faster and build the conviction to take moonshots. However, most companies simply don’t have enough data literacy on their team to interpret the data available to them. This often leads to situations where product managers make decisions based on convenient anecdotes or become too paralyzed to take risks.
Startups that prioritize going viral at launch miss a valuable opportunity to learn from their early users and maintain sustainable growth. Launching a new product is not about attracting as many users as possible through referrals. It’s about building a viable product!
While there are hundreds of well-written articles cataloguing the latest product growth metrics and frameworks used by Consumer teams, the same can’t be said for Enterprise businesses.
True, we have seen a “consumerization” of Enterprise software over the last decade with the proliferation of freemium SAAS models making Enterprise products more and more like Consumer experiences. We have also seen consensus emerge on biz-ops strategy for B2B business models — from structuring sales contracts for higher ARRs to optimizing inbound and marketing conversion.
Sample of behavioral cohorts, conversion funnels and retention analysis from the Amplitude Demo.
I spent the last four years investing in high impact startups and helping them find product-market fit. While they all professed a desire to be data-driven and iterate rapidly, only a few realized that goal. Implementing a good product analytics process and creating a transparent, data-informed culture across an entire team continue to be extremely hard problems — only getting harder as every growing company becomes a “big data” company and wonders how “AI” will affect them. These are tired buzzwords, but have unfortunately real implications.