Dec 6, 2022
Dec 6, 2022
by
by
Minami Rojas
Minami Rojas
How to build a product-led GTM strategy
Guide
Guide
A framework to analyze your funnel and build a product-led GTM strategy across sales, marketing, and product
Being product-led and crafting a systematic product-led GTM strategy are two different things. Being product-led means you most likely have great product market fit, an avid user base, and viral adoption.
Crafting a product-led GTM strategy means being data-driven and relentless about turning that funnel into a reliable engine. Building a system is how companies like Atlassian grew to be $3B and well on its way to $10B in revenue – but no one can turn into Atlassian overnight.
This guide will walk you through a framework on how to build a product-led GTM strategy beginning with your free trial or freemium funnel.
A framework to build a product-led GTM strategy
Step 1: Track Product Usage
Whether it be homegrown, in a data warehouse, or using a CDP like Segment, properly tracked data is the must have before embarking on this journey. Follow best practices to build a data tracking plan to help connect the goals across the GTM and engineering teams who will be implementing the tracks.
The recommended data model to follow when implementing tracking is to make sure all your data ladders together from raw events, to the user who took the action, to the workspace or instance they are a part of, and finally the domain or company they belong to.
What or who you’ll need help from
Engineering to embed tracking code
Product / Growth / Revenue to outline key product events to track
Step 2: Baseline your funnel
Now that you have product data tracked, it’s time to combine it with marketing and revenue data to start mapping out your funnel.
The most common funnel product-led companies have is their acquisition funnel. To map this out, use 6 month averages for the following data points:
Website Sessions
Sign Ups
Converted to Paid
Avg ACV for trial to paid conversion
Depending on your internal KPIs, you may have more metrics you can add along the funnel analysis like trials who have reached “Set Up”, “Value”, or “A-ha” moments. However the above is the simplest place to start.
What or who you’ll need help from
Data access using data warehouse, BI tools or data exports
Analytics team or Growth team to perform analysis
Resources such as OpenView 2022 Benchmarks to compare how your funnel metrics stack up against other SaaS companies.
Step 3: Analyze for propensity
Now that you have your baseline funnel mapped, you know your average paid conversion rate. To improve it, you want to define what the best converting trial looks like and find ways to make more trials like them.
To do so, first identify the key user actions that correlate to the highest propensity to buy and calculate what % of users are going through the “ideal” path. This lays the foundation for what you will focus on as part of your GTM strategy. Examples of high propensity product signals could be anything from setting up a specific integration, using a specific feature, or taking an action on the first day of trial. These will be unique to your product and your user – and you may find signals that surprise you!
What or who you’ll need help from
Analysis or a data science approach to analyze across all your product and trial data for a point in time propensity model
Or use Autotune by Endgame that uses ML to analyze your data and identify product signals
Step 4: Make data available real time
Congratulations! Now you know what actions your trials should take to convert at the highest rate. But right now, all you have is a point in time analysis.
In order to apply this, you need to productionize the learnings into your systems so it can be applied to new users coming in real time. This requires a backend that can run analysis as new trials sign up and engage daily, determine a score (PQA for example) or data points (they completed X action) and then push data into GTM tools like SFDC, Iterable, or back into Segment.
What or who you’ll need help from
Engineering and data science to deploy propensity model
Data and RevOps team to pipe data from model into GTM tools
5. Transform data into experiments
Determine the channel (sales, marketing, product) that will focus on improving each score. For example, product may experiment with a new onboarding flow to improve a key activation event, while sales focuses on high potential, medium conversion accounts with product-led outreach.
It’s important to note that siloed channels don’t own separate components. Instead, the entire GTM strategy is aligned to ensure all experiments are working in harmony to deliver the best customer experience and highest throughput.
What or who you’ll need help from
Growth & GTM to partner on strategy
For GTM plays, RevOps teams to build dashboards for easy prioritization
All teams to drive action and feedback
… And do it all again
The beauty of building GTM strategies around data and systems is that you can measure results, validate experiments, and easily repeat! And know that if you’ve gotten this far, you are leading the charge in how product-led companies are thinking about their GTM strategy.
Need help?
Got questions or want some help thinking through this for your team? Send us an email at hello@endgame.io and we’d love to chat
A framework to analyze your funnel and build a product-led GTM strategy across sales, marketing, and product
Being product-led and crafting a systematic product-led GTM strategy are two different things. Being product-led means you most likely have great product market fit, an avid user base, and viral adoption.
Crafting a product-led GTM strategy means being data-driven and relentless about turning that funnel into a reliable engine. Building a system is how companies like Atlassian grew to be $3B and well on its way to $10B in revenue – but no one can turn into Atlassian overnight.
This guide will walk you through a framework on how to build a product-led GTM strategy beginning with your free trial or freemium funnel.
A framework to build a product-led GTM strategy
Step 1: Track Product Usage
Whether it be homegrown, in a data warehouse, or using a CDP like Segment, properly tracked data is the must have before embarking on this journey. Follow best practices to build a data tracking plan to help connect the goals across the GTM and engineering teams who will be implementing the tracks.
The recommended data model to follow when implementing tracking is to make sure all your data ladders together from raw events, to the user who took the action, to the workspace or instance they are a part of, and finally the domain or company they belong to.
What or who you’ll need help from
Engineering to embed tracking code
Product / Growth / Revenue to outline key product events to track
Step 2: Baseline your funnel
Now that you have product data tracked, it’s time to combine it with marketing and revenue data to start mapping out your funnel.
The most common funnel product-led companies have is their acquisition funnel. To map this out, use 6 month averages for the following data points:
Website Sessions
Sign Ups
Converted to Paid
Avg ACV for trial to paid conversion
Depending on your internal KPIs, you may have more metrics you can add along the funnel analysis like trials who have reached “Set Up”, “Value”, or “A-ha” moments. However the above is the simplest place to start.
What or who you’ll need help from
Data access using data warehouse, BI tools or data exports
Analytics team or Growth team to perform analysis
Resources such as OpenView 2022 Benchmarks to compare how your funnel metrics stack up against other SaaS companies.
Step 3: Analyze for propensity
Now that you have your baseline funnel mapped, you know your average paid conversion rate. To improve it, you want to define what the best converting trial looks like and find ways to make more trials like them.
To do so, first identify the key user actions that correlate to the highest propensity to buy and calculate what % of users are going through the “ideal” path. This lays the foundation for what you will focus on as part of your GTM strategy. Examples of high propensity product signals could be anything from setting up a specific integration, using a specific feature, or taking an action on the first day of trial. These will be unique to your product and your user – and you may find signals that surprise you!
What or who you’ll need help from
Analysis or a data science approach to analyze across all your product and trial data for a point in time propensity model
Or use Autotune by Endgame that uses ML to analyze your data and identify product signals
Step 4: Make data available real time
Congratulations! Now you know what actions your trials should take to convert at the highest rate. But right now, all you have is a point in time analysis.
In order to apply this, you need to productionize the learnings into your systems so it can be applied to new users coming in real time. This requires a backend that can run analysis as new trials sign up and engage daily, determine a score (PQA for example) or data points (they completed X action) and then push data into GTM tools like SFDC, Iterable, or back into Segment.
What or who you’ll need help from
Engineering and data science to deploy propensity model
Data and RevOps team to pipe data from model into GTM tools
5. Transform data into experiments
Determine the channel (sales, marketing, product) that will focus on improving each score. For example, product may experiment with a new onboarding flow to improve a key activation event, while sales focuses on high potential, medium conversion accounts with product-led outreach.
It’s important to note that siloed channels don’t own separate components. Instead, the entire GTM strategy is aligned to ensure all experiments are working in harmony to deliver the best customer experience and highest throughput.
What or who you’ll need help from
Growth & GTM to partner on strategy
For GTM plays, RevOps teams to build dashboards for easy prioritization
All teams to drive action and feedback
… And do it all again
The beauty of building GTM strategies around data and systems is that you can measure results, validate experiments, and easily repeat! And know that if you’ve gotten this far, you are leading the charge in how product-led companies are thinking about their GTM strategy.
Need help?
Got questions or want some help thinking through this for your team? Send us an email at hello@endgame.io and we’d love to chat