Growth Engineering Basics

I have worked as a Growth Engineer for SaaS-based developer tools long enough to learn many of the tactics, processes, experimentation, funnels, and mindset of a Growth Engineer. With this series of posts, I want to log my knowledge for future reference and share it with someone who wants to transition to a Growth Engineer role. Let’s start by introducing Growth Engineering.

What is Growth Engineering?

Growth Engineering is a combined effort of marketing, product & engineering to grow a business or product. Providing the new age of SaaS-based products, where the nature of business has changed a lot, marketing had to evolve as well. Growth Engineers follow the tactics of traditional marketing but optimize them with an engineering approach.

For example, running ads in traditional marketing was considered a good way to bring in new customers, but growth engineers don’t solely rely on ads instead they try to avoid spending on ads until they exhaust other channels.

Why it’s called Engineering?

As discussed in the introduction it’s not just marketing, engineering is the core of Growth Engineering — it relies heavily on technical solutions, automation, and experimentation to optimize and scale.

Growth engineering applies software development, data science, and automation to drive user acquisition, retention, and revenue. Here are some examples of how engineering is at the centre of Growth.

• Growth Engineering heavily depends on automation, you need engineering to automate a referral system like Dropbox’s popular referral program, which helped them grow by 3900%.
• Optimizing User Experience with Data like analytics, events tracking & personalization algorithms to improve engagement rate. Engineering is required to collect and analyze huge data and craft a personalized experience. Netflix’s recommendation engine, an engineering-intensive experiment, gave them an edge over TV. Around 80% of the content watched on Netflix comes from the recommendation engine.
• Marketing was always dependent on engineering teams for running and iterating over any kind of experimentation. In Growth Engineering experimentation and iteration become much faster and controlled. For example, LinkedIn’s “People You May Know” feature was fine-tuned through continuous A/B testing.

Growth Process

Although growth engineering deals with unknowns most of the time but being an engineering field it still follows a defined process. It’s a systematic approach to optimize different areas of a user’s journey from identifying a problem → buying the solution you provide →  referring others to your solution. The process can be defined in 4 steps below:

1. Identify Problem: Identify which areas of business like marketing, operations, or product are not performing. Also, identify a metric that will be improved if we solve the problem correctly.
2. Understand Problem: Understand the problem and determine ways to improve it, we call these solutions hypothesis as most of the time we don’t know if the solution will work.
3. Implement & Monitor Hypothesis: Implement the solution and monitor the growth metric if it improves. We can also run A/B/C experiments if we have multiple hypotheses and enough user base to test the hypothesis on sample users instead of the whole user base.
4. Evaluate Hypothesis & Iterate: If the changes led to growth metrics’ improvement you can further drill down on the variant with more hypotheses to improve it further until you max out on the hypothesis following the same steps from #1. Once you find the best-performing variant, you can scale it whole audience if you were testing it on a small sample and monitor.

There can be multiple representations of this process but the base of the process remains the same Identify Problem → Draw Hypothesis → Implement → Monitor → Iterate → Scale.

Growth Funnel

Growth Funnel is the way to categorize users based on their intent and level of interaction with the solution you are offering. Different users can be at different stages some might be using your product, while others might be discovering your product, and some might be very excited about your product and referring it to everyone they meet.

We can also call it the different stages of the user journey to become a loyal customer. The growth funnel has 6 stages:

1. Awareness — User’s that are facing the problem we are solving. They may or may not be aware of the problem, but they are essentially facing the problem. They may or may not be aware of the solution we are offering. They can be users of our competitors. Our goal at this stage is to make the user problem-aware and our solution-aware. Success metrics can be traffic on the website or product landing page.
2. Acquisition — Once aware, our next goal is to make them use our product at least once. Users at this stage may or may not like our product. They might be considering our competition products. They are well aware of the problem & solution we are offering, they just want to be sure that we are offering the exact solution they are looking for. By experimenting and collecting enough user signals we can determine what user wants and displaying what they want will help in user acquisition.
3. Activation — Once the user tries your solution, we aim for the user’s activation. Actions taken at this stage are majorly performed inside the product/solution we are offering. In some cases, it can be outside of the product like product documentation. At this stage we first define the number of actions required from a user to activate them like signing up, using a set of features. Defining the activation metric and making it easy and upfront for the user to perform these actions is our goal at this stage.
4. Retention — This stage is similar to Activation, just the difference is that the activation stage focuses on ensuring the user drives the first value while the retention stage focuses on driving continuous value. You define the success metric of this stage as the number of activities performed by a user over a period of time. Netflix’s recommendations engine is one such experiment that improved user retention by suggesting to them the content they can watch for a longer period.
   Note: The sequence of Retention, Revenue & referral stages can change from business to business. Some businesses might prioritize revenue over retention and referral, while others might want to retain users first and then go for revenue.
5. Revenue — At this stage, your focus goes on optimizing for overall revenue generation — The number of users paying for the solution and amount they are paying for the solution. You might have seen changes in the pricing of various online products or services and different pricing packages per solution — all these are pricing experiments. ARR and MRR are the main success metrics at this stage.
6. Referral — Often overlooked by traditional marketers, the referral stage is very important as if implemented right it can grow a business exponentially. Remember the 3900% growth of Dropbox through its referral program. At this stage, our goal is to encourage our users with incentives or enough value that users start referring our solution to their network. This not only brings new customers but also helps in enhancing credibility and trust in the solution and brand.

Growth Mindset

The growth mindset not only helps in achieving success in Growth Engineering, but it helps in other areas of life as well like career, health, finances etc. Growth mindset is continuously repeating — experiment, measure, learn and improve. We can apply it to marketing efforts, our product and even in different areas of life. Let’s dive deeper into the 4 components of a growth mindset.

1. Experiment — At any stage of the funnel, fixing a problem starts by drawing some hypothesis to solve a particular problem and then running an experiment to see if the desired results are generated or not. In growth engineering, you will be monitoring your funnel and keep looking for areas of improvement by drawing clear, testable hypotheses. Then run the experiment by rapid prototyping. Here we make small improvements rather than putting in extensive upfront effort. 
2. Measure — To measure the success of an experiment accurate measurement is important. Defining key metrics, and collecting enough data to measure the success of an experiment ensures we are drawing the right conclusions. 
3. Learn — After measuring the results, learning from data and identifying the patterns help share future experiments and strategies. Spend enough time with data to convert that into actionable knowledge.
4. Improve — Once you have gained enough knowledge from the previous experiment, use it make incremental development and scaling successful strategies.

Growth Toolset

Growth Engineers need various tools at different stages of the funnel. Some of the most common tools are as follows:

1. Customer segmentation and management
2. Email Automation
3. Data Scrapping
4. Email Marketing
5. Social Media Marketing
6. Website Analytics
7. Search Engine Analytics
8. Communities
9. A/B Experiment tools
10. Product Analytics
11. Behavioral analytics tools
12. Survey Tools
13. Competitive Analysis Tools
14. AI
15. Project Management

We will see each category in detail with tools coming under this category in upcoming posts.