What’s common with companies that build the most successful products?

They build great products, and they build great growth and distribution strategies. A growth strategy will make or break your company. A great product growth strategy is enabled by a solid engineering strategy that builds primitives and accelerators of growth.

I recently completed the Reforge Growth Series, and it is hands down the best growth product content and community out there. I learned a ton of fundamentals on product strategy-led growth, which I wanted to capture here (credits to reforge for all the product content below).

As an engineering manager and engineer at heart, I also wanted to add tips on engineering strategies to enable and effectively execute the growth strategy. This will cover high-level pointers on systems to build, and in-depth detail may be covered in a future post.

Why Growth

Growth is the winning sauce for companies because:

It creates defensibility
Attracts more resources
Enables quick and deep learning
Growth is self compounding

Growth systems

Growth systems allow hypothesis-driven experiments that uncover the truth on what drives activation, retention, engagement, and monetisation. They are systematic, deterministic, sustainable and repeatable.

I will cover below the fundamentals of retention and engagement in this first post and cover activation and resurrection in the follow-up post. I will also interleave high-level engineering strategies for each after the product fundamentals.

Part one: Retention

Why retention? It separates the top one per cent. Retention powers acquisition, engagement and monetisation.

Also Read: Behind the product: How Igloo plans to support insurance sales intermediaries with its new platform

It increases viral touch points to acquire more users (think slack, dropbox, LinkedIn, Medium)
It increases LTV, which allows us to invest more in new acquisition channels.
It improves conversion rates for subscriptions, ads, transactions and premium services
Ultimately, increased retention brings more engagement and leads to higher monetisation.

Retention is the silent killer

If not paid attention to, it will slow down the growth and ultimately kill the product.

We can have fast top-of-funnel growth, but if we have poor retention, LTV, DAU/MAU will die down.
Poor retention is easy to cover up as it is easy to use the wrong metric, has a long-term view and can be deprioritised.

Retention is the output

Retention = (Activation + Engagement + Resurrection)

Common mistakes when setting up Retention

Wrong Frequency of usage
Choosing the wrong core action
Optimising for the wrong audience

Qualitative Definition of retention for your product

At a high level, our goal is to build multiple use cases based on the main use case and push users into a habit zone

Retention metric

Setting up the quantitative definition of retention metric:

Make sure to align with a direct and natural frequency of usage.
Do not combine multiple actions into your retention metric. It can easily mask issues and give false positives.
Do not optimise for revenue. Monetisation is the output of retention.

Determining the right usage frequency:

Select a use case or the core action.
Create the action histogram-
Analyse distribution, daily/weekly/monthly, and so on.

Validating the Core Action:

Form groups that performed the core action.
Create a cohort chart and compare the retention curves.

Ultimately the retention curve is the output of how you performed through the inputs. Activation, engagement and resurrection.

Engineering tips

As shared above, retention is the output and is usually a lagging indicator. Most of our engineering foundations will invest in engagement, activation and resurrection. However, there are a few areas where teams should invest in setting the right foundations (in no particular order).

Experimentation: Invest in a scalable and configurable experimentation framework that allows us to validate or invalidate your hypothesis quickly. Great experimentation tools speed us up instead of slowing you down. It’s a red flag if the A/B testing setup is easy to trip on or takes too much time to configure and roll out tests.

Analytics and event tracking: This will form the basis of collecting data that will help us understand current baselines. The shelf like amplitude, google analytics etc are good starting points, and we can build in-house once we have reached the inflexion point of the scale. If you can’t measure it, you can’t improve it

Scalable data platform: This depends on scaling up experimentation, analysis, machine learning/personalisation and leveraging data to create new use cases. As our user base and data grows, having these capabilities in-house provides a step function change on how we can transform data and tools to meet our needs.

Also Read: How to pursue a product idea into a successful business

Dynamic content: Build or integrate with a platform that allows serving content like strings and media without new releases. This will unlock experimentation velocity and serve the ultimate purpose of validating the hypothesis quickly.

Part two: Engagement

Engagement is non-binary, unlike retention. It’s a spectrum of depth. It’s a measure of how “engaged” is your user base with the product and features. Once a user is engaged, they have formed the habit and are in the “loop”. There are two main habit loops, organic and manufactured.

Organic habit loop

Manufactured habit loop

Successful products can build great manufactured habit loops. It has four steps:

Manufactured cue or trigger: Products use data and opportunity to create them. We can choose a combination of these triggers to engage the user per our product. The data foundations built based on the product growth strategy will enable these loops. There are five broad categories: time-based, location-based, update/change-based, network/peer-based, and programmatic.
Channel: Medium is used to communicate with the user. Email, notifications (push or browser), in-app, digital ads or traditional mail.
Action: The core action we want the user to perform in response.
Reward: Reward for the user taking action. There are three major types of rewards: extrinsic (time, money, information), intrinsic (completion, mastery, joy), and social (recognition, confirmation, competition).

Pro Tips

Leave the user with one more trigger.
Combine loops. Create the core loop and create supplemental loops to fuel the core loop.
For example, LinkedIn core loop: New Connection. Supplemental loop: job update, recommendations, people you may know, skill endorsement, messaging.

Engineering tips

Communications platform and delivery time/channel optimisation: Invest in a communication platform that enables different channels as discussed before (email, push, in-app, ads etc.). The efficacy of lifecycle comms starts to increase when we optimise the choice of channel and timing of the delivery.
Targeting system: Build a targeting system that allows cherry-picking users to match a certain set of attributes for specific actions. Since engagement is a spectrum, the same message and action will not work for all users alike. It is imperative to understand where each user is in their journey and then communicate with them appropriately.
Personalisation and ML: Personalisation is a muscle that every product needs to build over time. This will touch and serve multiple aspects of product and engineering systems. Invest in building a decoupled and composable system that can be deployed to learn and personalise the user experience. Users use a product to solve their unique problems at the right time.

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