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Unleashing the Potential of Growth and Optimization Strategies
A Playbook to Achieve High-Impact Results with Fast Paced Opportunity exploration
Most product teams’ strategies are optimization ones. Yet, the majority of teams achieve mediocre results with them.
If you work on a somewhat mature product and aim to grow in customers, revenues, retention, and so on, you are pursuing this type of strategy.
The goals are straightforward, and the playground is well-known. But you should not be deceived by its simplicity. Most teams fail to leverage the potential that this solid ground provides to achieve high impact.
In a highly competitive business landscape, to stay ahead of the curve, you must develop the muscles to quickly identify opportunities, accurately assess them, and quickly iterate over the most promising ones.
But how should we tackle its execution? How do you define goals? How should you approach discovery? What’s the role of the product vision? In this article, we will explore how to connect this type of strategy with a high-impact execution playbook.
Note: this article is part of a series describing three types of strategies and their corresponding execution playbook! (Read the first one here)
Table of contents
Successfully executing of Grow and Optimization Strategies (this article)
Portfolio strategy and managing different types simultaneously
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What is a good Grow and Optimization (G&O) Strategy?
In the first article of this series, we introduced this strategy as “doubling down on what we already have. We are not drastically changing the value proposition, business model, or user groups. We are trying to improve how we cover the set of needs and markets we are currently working with.”
This doesn’t mean that we will just be tweaking the interface to extract a few more clicks from our users! It simply means that we want to keep increasing how we serve used underserved needs.
While that definitely implies improving the UX to reduce friction, complexity, and anxiety, it also covers more topics, such as:
Finding adjacent use cases (for example, if you already book appointments, send appointment reminders)
Extend existing use cases (if you connect with Google Calendar, add iCal integration)
Reducing effort or automating tasks
… and the list can go on. All these paths for a G&O strategy can have a significant impact on your product's core KPIs.
How does a good Grow and Optimization Strategy look?
To make it more concrete, a good one should have:
Clear directives of what underserved needs we address: “We will improve X% conversion rate” is not a good optimization strategy. “Extend integrations to cover 2/3 of the market not using our current integration options” or “Reduce 50% of the time spent on the application step” could be much better.
A set of concrete levers we want to move: the best strategies will go beyond the high-level KPIs and identify concrete and segmented metrics we want to move (for example, “increase revenues” versus “improve 7-day retention for young users signing up from referrals”)
Reasons and assumptions: to complete the strategic context for teams, leaders should highlight why these directives and levers are important. For example, “focus on retention of young users because they are the key growth driver with an expected LTV of €X over the next Y years.” Similarly, the assumption of the strategic driver should be clear (“By extending integrations, we expect the sign-up rate to go up Z%”). These will help teams focus their experimentation and provide early feedback about the strategy's potential success.
The execution playbook for Grow and Optimization
In the last article, we introduced the elements that interconnect the strategy with the execution: (1) Vision, (2) Strategy & Models, (3) Goals & Assumptions, (4) Opportunity space & definition, and (5) Delivery cycles & iterations.
Let’s see how they should be used to achieve success with a G&O Strategy.
In a G&O Strategy, vision is not a critical component because, since we are doubling down on what we have, there is a small risk of pursuing options that will not fit it.
Of course, I would always advise having one, but it would not play a role as relevant as in other types of strategies.
2. Strategy and Models
In the previous section, I described a good G&O strategy, and as you have seen is rather simple, with a few straightforward directives and goals.
Similarly, models like JTBD, Journeys, KPI Trees, or any other high-level description of the product “system” and your users’ needs will remain stable, focusing on refining how the system works to deliver and capture more value.
3. Goals, Risks, and Assumptions
Similarly, the goals would also be straightforward, focusing mostly on improving existing core KPIs. Part of identifying optimization opportunities is the ability to slice and dice those KPIs to identify user segments or parts of the journey where the results are below expectations.
This also means that while there will always be a risk, it is not as high as when we are going to uncharted territories where we don’t even know how to measure.
4. Opportunity Space and Assessment
Here is where things get interesting, and how you do discovery work can have an important effect on your results.
In essence, we are not trying to uncover new high-level opportunities. We are trying to improve coverage of the key opportunities that already achieve some success. If you think from the perspective of an Opportunity Tree, we will work on the lower branches and leaves. What may be the implications in your practices:
More quick prototype testing based on the existing product
Generative research more focused on a deeper understanding of the existing use cases and current limiting factors
Quick jumps from qualitative to quantitative (survey, fake door, or MVPs) to validate if we have a chance of moving the core KPIs.
Don’t get me wrong: this is no “checklist,” and you should use your judgment. My recommendation is to keep a strong focus and prioritize fast-result methods.
It also doesn’t prevent innovation: many of your discoveries can lead to very different ways to solve an existing and challenging problem!
5. Delivery cycles and iteration
It will be no surprise that it’s critical to iterate fast. We want iterations with a value hypothesis, and we need some infrastructure to cheaply run A/B tests and measure all the behavior changes introduced by our new product increments.
Furthermore, sometimes (especially for small experience changes), the cost of learning through discovery can be higher than just developing and A/B testing a change. For example, at this stage, your product has a growth engine or flywheel, and much of the work will be testing changes that can lubricate and fuel the machine. The opportunities we uncover through slicing and dicing segments and activities are candidates to jump to our discovery backlog as optimization iterations of existing features.
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Conclusion and variations
Focused discovery and fast delivery experimentation are critical to winning in this strategy.
The iteration speed and measurement capabilities are critical. Is this not desired in other types of strategies? Let’s be more precise: since these products are already serving customers, the need is to have robust pipelines that ensure rapidly scalable releases and high-fidelity measurement.
The same is true for discovery: if sourcing users for a prototype test is costly and cumbersome, we will never get to the speed we need to test multiple variations quickly. While this is also true for other strategies, in this case, we need to be able to source the right customers. When we slice and dice to uncover opportunities, we need to talk with users within those segments and needs quickly.
Having (or building) these capabilities can be outside the scope of the product discipline, but it’s our role to state that if we want to succeed in a G&O strategy, we need to have it.
In our next articles, we will go over the tricks of the successful execution of an Expand and Mutate strategy.