My MBA research into data product lifetime value separated the problem into two dimensions.
Product creation and management.
The data, use cases, quality and product experience.
Market execution and enablement.
How the product is positioned, sold, embedded, priced, renewed and differentiated.
Both dimensions need to change through the product lifecycle.
Growth changes the job.
Early growth may come from solving a clear problem for an obvious customer group. Sustained value requires something different: stronger account management, deeper integration into customer decisions, deliberate differentiation and a commercial model that still fits how the product is used.
Continuing to add features while leaving positioning, pricing and customer enablement untouched is not a growth strategy.
When a data product slows down, the useful question is not only what to build next. It is whether the whole product and commercial model is still moving with the market.