Companies are ready for product growth after they’ve developed a fit between what they produce and what their potential customers will buy.
In terms of AI pathfinding, you have narrowed the possible search space. There is approximate coherence between things like customer profile, market definition, revenue model, featureset, and more.
This is an amazing accomplishment, and requires significant vision because there are often not a lot of data telling you where to go. It’s easier to make decisions with data than without it; early stage companies have to infer so much from so little information.
The ad-hoc vision required in of an early stage company should transition to a more formalized and structure growth process, as each iteration of the product gets closer to the optimal outcome (meeting the market’s need in a scalable and repeatable business process, supported by a strong revenue model in a sufficiently large market).
This transition can be difficult due to the inherent difficulty of change, but a good growth product manager should attach to existing teams and processes instead of trying to implement sweeping change.
This article is my perspective and strategic plan for companies looking to build a sustained growth engine after they have made significant strides in creating their business.
Lots of things are working to reach this point. Often, a mix of powerful vision and team cohesion are sufficient, but as the team and product grow, new modes of thinking are required to provide new insight into well studied problems.
The early stages of a product are optimized for learning, whereas growth stage needs a mix of exploration and exploitation when a new channel or feature hits a tippingm point in your primary KPIs.
The rate of change before the growth stage is so rapid that heavy investments in data systems are unwise; they quickly grow outdated, and maintenance slows down the pace that is so crucial early on. In the growth stage, however, the company has a feel for what they should measure, and need to implement a system to start measuring it. This helps with organizational communication, as decisions supported by data receive broad cross-team support from key stakeholders.
First things first
Learn what each team’s primary objectives are, from their perspective. How do they see their work supporting the larger organization objectives. If their culture is a fit for data solutions, learn what things each team cares about. Then work with each team to automate the collection and display of their primary KPIs.
First, work to support each team. Build rapport, but challenge each team to tie their primary metrics to the broader organizational goals. If the org is focused on signup conversion rates, for example, the dev team should be supported to understand how page or app load time affects that rate.
After you understand how teams see themselves, and their piece of the puzzle, work with management to compare their vision with reality. It is crucial that management have data that reflects reality in order to optimally allocate resources.
Protip: automate the collection of data for dashboarding. If it takes effort to produce a report, they won't be used as frequently.
Work with management to understand their vision, and internalize their experience in growing the product to this point. Identify key levers for growth (critical path user activations, unit economics, pre-mortem the risks, etc). Dashboard everything – use data to provide management x-ray insight into the current operations, so they can make better decisions.
End of Phase 1
Ideally, the process above should be completed as quickly as possible, but expect it to last 3-6 months. It’s important to build empathy with each critical stakeholder, so you know what they’re trying to do. The best way to to do that is providing immediate value that helps team leads have better visibility into what they’re supposed to do. Earn points by helping them track and report on what they need to do, to support their resource requests with management.
The next phase of the product growth process is to incorporate innovation at every level of the company DNA. There are so many ways to grow a company, so the primary growth responsibility is to help every team maximize their contributions to the common objectives.
In basketball, this is a new metric called on/off splits, which attempts to measure how much better a team is with a given player in the game. This acknowledges the subtle influence that players can have on each other due to the dynamic nature of basketball; baseball, for example is a turn-based state machine, and this effect is less impactful.
The goal of phase 2 is nicely put here:
But if you’re not influencing others and helping the broader team execute, you’re setting a ceiling on the degree to which you can contribute to the company’s output
Phase 2 will be written about shortly.