PLG and Customer Activation – New Paradigm in Marketing Analytics

PLG and customer activation powered by advanced marketing analytics capabilities are among the top priorities for all business and growth marketing leaders across B2B SaaS organizations.

The primary focus of the marketing analytics work that we have been doing with some of the most forward-looking B2B marketing analytics leaders across SaaS organizations is being augmented with the PLG analytics frameworks.

PLG analytics is now seen as the part of the core B2B GTM analytics frameworks

that include metrics like cohort-based funnel conversions, campaigns performance, tracking against funnel goals, marketing attribution, pipeline influence tracking, and database segmentation.

The emergence of PLG as the core GTM strategy has been a game changer not only for the B2B SaaS organizations from the GTM perspective but it has opened up exciting new possibilities for the marketing analytics teams.

PLG is built on the foundation of advanced data analytics capabilities. To drive a successful PLG strategy, marketing and growth teams rely on the analytics teams to understand product engagement data.

This event based product engagement data is made available in the data lakes and the data warehouses to be cross-hatched with the wider sales and marketing data to drive engaging product experience with the users – the essence of PLG strategy. The growing body of work that we have been doing in the past few months has been focused on PLG analytics and customer activation. Although we are very early in our journey towards building our core competency in PLG analytics, this post is an attempt to document our learnings and share the work that we have been doing with some of the most exciting B2B SaaS organizations that have adopted PLG.

PLG is a new paradigm.

In PLG model, the GTM motion relies on the product to drive growth. This is accomplished by letting the users rather than the decision-makers experience the value of the product. The intent is to provide limited friction within the product and let the users discover how the product will help them do their job more effectively on a day-to-day basis.

PLG GTM motion positions the product as the main driver of customer acquisition, activation, retention, and expansion. Most of the B2B SaaS organizations with data-driven marketing and GTM teams are shifting to PLG as their core go-to-market motion to accelerate growth. To date, about 25% of the organizations have some form of PLG motion in play. Given the success rate of the PLG motion at companies like Slack, Twilio, Dropbox, Airtable, Notion, there is a huge opportunity for the organizations to adopt the PLG model.

Strong GTM and marketing analytics capabilities are the core competencies that define the success of a PLG model. Capturing and analyzing a wide breadth of data points and metrics to understand how customers are engaging with the product and how they are deriving value for their businesses is critical to the success of PLG.

PLG takes marketing analytics to a whole new level. PLG presents a new set of metrics that define the success of the approach, and provide the needed intelligence for delivering an engaging customer experience and for continuous optimization of the strategy. Most modern B2B marketing organizations track the key metrics (including funnel performance and revenue metrics) that define the foundation of how marketing and sales teams operate on a day-to-day basis, measure and optimize performance.

Modern marketing technology and scalable data analytics platforms have made it easy to actively track the most detailed product engagements and behavior data within the product platforms. With the right analytics strategy and tools, marketing and growth teams can be empowered with the key insights from detailed product engagement data. This data allows teams to track, measure, and analyze user behavior.

These key data insights further enable growth teams to run go-to-market experiments that lead to incremental improvements to the user journey and funnel conversions.

Making data-driven decisions based on user behavior and product engagement is critical to PLG. Behavioral analytics can track every interaction that occurs within the product. Marketing analytics platforms allow analysts to mine this data to provide insights to help growth teams optimize the user journey, and product engagement behavior and further personalize the product experience through intuitive product design. We have been helping the growth teams define and track KPIs to track the performance of their PLG strategy. Key Metrics that are part of core PLG analytics frameworks include:

Acquisition/Sign ups: is the top of the funnel lead generation in PLG model. These are those leads who have signed up for the product rather than just downloaded a piece of content from a 3rd party website. Sign ups are the starting point in the PLG funnel. Tracking acquisitions from sources (inbound vs outbound) and their conversions down the funnel define the success of the PLG strategy.

PQLs: Product-qualified leads (PQLs) are activated users who have crossed a threshold of engagement by completing a set of milestones within the product.

PQLs are those users who have shown enough engagement within the product and have performed certain actions to derive the intended value from the product. These are the users that are then ready for a sales conversation and these have the highest probability of conversion.

TTV (Time To Value) is an important part of PQL as it helps provide visibility into the PLG funnel performance and goal attainment. The success of PLG is measured by reduced TTV to enable users to realize the value of the product sooner before they churn.

Defining the “activated” user from data perspective is a critical part of the PQL as the success of PLG strategy depends on tracking the activated users and the performance of all downstream funnel metrics in PLG is dependent on this definition.

Given the breadth and depth of behavioral and engagement data tracking that is available within SaaS products, defining “customer activation” and building data models around it is a monumental project for the analytics teams.

To begin with, all the product engagement data is piped from the product into a data lake where it is made available to be cross-hatched with the other data from CRM, marketing, and finance systems. Depending on the product and industry, the product data usually spans these categories:

• Sign ups: to track the number of users/orgs that are being created in a given time. This data is then combined with a data enrichment provider (like Clearbit, Zoominfo) for additional demographic details of the organizations.
• Product Engagement (Events): data tracks various actions that users take within the product. It is from this events data, we can identify the users who have taken certain actions like the frequency of logins, visits to certain pages with the app/product, certain buttons/links click events, etc. Working with the product managers, we identify those key actions that together would help tag the users/orgs who have completed these actions.

From analytics and data modeling perspective, we first spend time understanding the core database architecture of the product, the key actions/events that are being tracked within the product, and review all the data in the data lake to get a high-level overview.

Once all the raw data is assembled in a data lake, the next step towards building the definition of an activated user is to understand everything about the customers.

We identify the paying customers an organization has and start mining the product engagement data for users/orgs to identify patterns, heat-maps, and clusters of the actions that are most common across all the paying customers.

This is the critical part of the data modeling towards understanding the “activated users” and in turn defining the threshold for PQLs. Our goal here is to

identify all the key attributes of the users that define the tipping point when they are ready to convert and become paying customers.

The outcome of this exercise is a set of metrics with a set of ranges that are provided to the growth and the product teams to further contextualize the data. Summary of such analysis consists of data points, which vary depending on the product engagement data that is being captured and fed into the data lake. Here is one such example of customer product engagement analysis:

• 83% of the users had done actions A, B, and C at the time of conversion.
• 65% of the users had logged in 5 times per week in the past 3 weeks leading up to conversion.
• On average, there are about 4 users in the org at the time of the conversion.
• 70% of the orgs have connected the app/product with Slack for active notifications.

Once the core teams (especially growth and product) align on the definitions of what an ideal “activated” customer looks like the criteria are frozen and it becomes the guiding factor for defining and building the segments of engaged users.

These segments are built with the sign-ups (users/orgs) who have been showing the highest probability of meeting the criteria of the activated customers based on their engagements. This multi-dimensional data mining and analysis work results in building such growth segments of users showing a varying degree of alignment with the definition of the ideal activated customers. Generally, the number of such growth segments is related to the number of key metrics that are included in the definition (as described above) of the activated customer. For an organization with the definition of the activated customer as described above, these are the possible growth segments of the engaged users that can be built:

• Users/orgs who have done only 1 or 2 of the 3 critical actions within the product.
• Orgs where in only 50% of the signed-up users are logging in weekly.
• Orgs that are over 100 employees but only have 2 users signed up.
• Only 40% of the orgs that have more than 3 users have integrated the product with Slack.

The core theme in these segments is the product engagement. These segments provide actionable insights for the growth teams to leverage them in the campaigns and sales outreach to provide relevant and meaningful context for driving higher conversions. The primary goal for these campaigns is not to drive sales revenue but take actions (experiments, marketing outreach, targeted digital campaigns) to drive higher product engagement and bring each of these segments closer to the ideal activated customer segments. This increase in the alignment with the ideal customer engagement profile in turn drives the sales conversions.

Although PQL and customer activation are the foundations of the PLG analytics, there are 2 other key metrics in the PLG funnel that we have been tracking:

Trial conversion rate – tracks the percentage of trial users who eventually became paid customers and on average, how long it takes for the conversion (velocity). Tracking and optimizing this leading indicator is critical to have better visibility into pipeline and forecasting.

Expansion Revenue – is the core of the land and expand GTM motion that is driven by PLG. Tracking the additional revenue generated per account is effective to help with the cost-effective expansion strategy and is a key measure for the success of the PLG approach.

This framework has been our experience so far to further drive the success of PLG initiatives for our customers by taking their marketing analytics capabilities to the next level. It keeps getting more exciting every day!

We are always on the lookout for inputs and examples from the marketing community to keep adding value for our customers. We welcome the inputs from other growth marketing leaders and analytics practitioners around the approach to PLG strategy at their organizations. What metrics are you using to track the performance of PLG at your organizations? What does the definition of “activated” customer looks like at your organization?