With companies gaining access to large volumes of internal and external data, product management decisions are becoming increasingly data-driven. The best product managers realize that data is not just numbers, but rather the voice of the customer. Why is this so? It is because product success is typically measured using metrics such as customer engagement, retention, and conversion and it is product managers who can influence these metrics the most. According to a survey by AIPMM, educating executives on product management and training product management staff is important for product success.

What makes product management increasingly data-driven?

In addition to the data deluge that organizations face today, there are several other factors that are making product management increasingly data-driven. The nature of the products handled by today’s product managers has changed drastically. For example, in the widely deployed Software as a Service (SaaS) model, product managers have to manage multiple product bundles, pricing tiers, upselling paths, and pricing strategies in a highly dynamic market environment.

Moreover, the product lifecycle continues to grow in complexity as the products exist in an inter-related ecosystem of services and businesses, with the constant addition of new features as well as frequent improvements and upgrades after purchase. In this intricate digital world, data science can help product managers closely understand not only the needs of the customers, but also the inter-relationship between the variables in the product ecosystem.

How is abundant data changing product management careers?

Complex product environments and lifecycles require product managers to optimize product roadmaps. They do this by using customer feedback to foster new ideas and data to propel better decisions. Relying on just qualitative studies and intuition might result in the creation of products that turn out to be engineering or manufacturing marvels, but may fail to drive customer acceptance. Great ideas, on the other hand, might be shot down just because of gut feeling or due to the incorrect use of market research data. This is where data science comes to the rescue of product managers by ensuring that product related decision-making is rigorous and the validity of decisions is statistically tested.

The role of product managers is undergoing a paradigm shift. Data literacy is no longer a ‘good-to-have’ skill, but a core competency. What’s more, in today’s product teams, product managers have to manage not just developers and testers, but also operation specialists, designers, product marketers, and data scientists – all working in close collaboration with processes enriched by data. To understand and effectively manage these resources, product managers have no other option but to master data science skills. A quick review of product manager job profiles on popular job portals will reveal that even junior positions invariably require analytical capabilities.

The evolving product manager role

In the immediate future, product managers will have to be at the top of their game by acquiring strong data skills and relying less on analysts. According to a McKinsey report, over the next three to five years, the role of a product manager will continue to evolve towards a deeper focus on data with an attempt to develop empathy with end users.

Not only that, product managers will increasingly apply machine learning concepts and tools specifically designed to augment their role. With data-driven decision-making, product managers will also gain greater salience and influence over strategic, non-product decisions.

Are you ready to play the role of the product manager of the future?