How to Boost SaaS Growth with PQL Scores and Activation Strategy

Databox
Insights
Published in
10 min readJun 17, 2024

User activation matters, especially if your business has adopted a product-led growth business model.

If you are looking to improve user activation, you should have an activation framework in place and be able to identify product-qualified leads (PQLs).

Why? Because converting them to paying customers becomes way easier.

Implementing a user activation framework is part of the Product-Led Growth (PLG) strategy and can significantly transform your business results.

The good news is that it’s not hard to do.

What is Product-Led Growth?

Product-led growth is a business strategy that makes the product the main driver of acquisition, conversion, and expansion. PLG companies focus on providing a great product experience that attracts, converts, and retains users. Examples of PLG strategies include free trials, freemium versions, or straightforward onboardings that let the user experience the value of the product as soon as possible.

To prioritize customer impact and deliver exceptional value to our users, we use the Customer Lifecycle Framework (CLF), designed to streamline customer acquisition, onboarding, and retention across their journey with our product.

What is an Activation Framework?

An essential part of the CLF is the Activation Framework, an internal framework that helps us measure and improve the activation of new users. It enables us to effectively measure user engagement, identify leads that have derived real value from our product, and acquire and retain more customers.

Activation Framework at Databox
Activation Framework at Databox

In this blog, you’ll learn how activation strategies can deliver value to your users while driving company growth, based on the framework we used at Databox.

How to Unlock Success with an Activation Framework

Activation is a set of self-serve flows designed to facilitate the initial discovery of our product’s value by new users. It is measured differently by different companies, for example Slack, Dropbox and Hubstpo use different activation frameworks depending on their definition or a product-qualified lead. Read more about their strategies in this article.

What is an Activation Score Framework?

An activation score framework is a method used to measure how effectively users are engaging with key features of a product. It helps identify actions that users must take to experience the core value of the product and be considered activated users.

Activation scores are typically based on user behavior. They are used to optimize onboarding and improve user retention.

At Databox, we started tracking activation scores seven years ago after introducing a basic metric called Product Qualified Lead (PQL).

What is a Product-Qualified Lead (PQL)?

A product-qualified lead (PQL) is a user who has interacted with your product through a free trial or limited access and experienced its value. PQLs are more likely to convert into paying customers because their interest is based on firsthand experience with the product.

Activation and PQLs are closely connected. Activation refers to users reaching milestones that show they experienced the product’s value, while PQLs are users who show a likelihood of conversion. Activation scores are used to predict PQLs, which helps the company target and nurture leads with the highest potential for conversion.

There are also other types of leads that can be tracked, but not to be mistaken with PQL.

What is a PQL
What is a PQL?

What is a Marketing-Qualified Lead (MQL)?

A marketing-qualified lead (MQL) is a user who has shown interest in your product via marketing efforts. MQLs usually engage with marketing materials and are not ready for direct sales contact.

What is a Sales-Qualified Lead (SQL)?

A sales-qualified lead (SQL) is a user that the marketing and sales teams have identified as ready for direct sales engagements. SQLs usually showcase a high level of interest and are prioritized by the sales teams.

What is the difference between a PQL, SQL, and MQL?

The difference between PQL, MQL, and SQL is in how they are identified.

  • MQLs are identified through marketing activities, such as a lead who downloads an eBook.
  • SQLs are identified through a combination of marketing and sales processes and are qualified as ready for a conversion, such as a lead who fills out a demo request.
  • PQLs are identified through product usage and are further along in the buying process, such as a lead who uses a product’s core features during a free trial.
MQL vs SQL vs PQL

What is a PQL Score?

A PQL score is a tool in a product-led growth strategy that provides a clear and quantifiable way to measure the potential of each lead based on their interaction with the product. By focusing on PQLs, companies can improve their sales and conversion rates.

Using PQLs and Activation Milestones to Drive Growth at Databox

At Databox, our initial strategy of using PQLs and measuring their scores was effective. However, as our company and market demands evolved, we realized we needed to update it.

We wanted to align our approach with our evolving product and business goals. Consequently, these are the lessons that we’ve learned:

  • Our initial PQL definition lacked nuance and predictive power
  • The introduction of new features demanded a more comprehensive scoring model, giving rise to the activation score
  • Enhanced tracking quality and data acquisition enriched our insights and sparked demand for more stages in the activation process

Due to the reasons above, we expanded our activation framework by including Activation Milestones (Setup, Activated, Habit), PQLs, and Activation Score, which measures the degree of user activation on a scale of 0–100.

How to measure user activation score
Measuring the Activation Score of Users

Activation is critical for every new Databox user as it presents the greatest opportunity for optimization. To maximize that goal, we use an approach with three activation steps (Setup, AHA, and Habit) and the PQL score.

The new framework helps us achieve our primary goal of delivering value to users as quickly as possible and plays a vital role in driving the success of Databox, primarily through customer acquisition (new MRR) and retention (retention rate). It enables us to establish user engagement flows, conditions, and tactics for engaging with our users in the early stages of their journey.

Series of scSteps in Activation Journey at Databox
Series of Steps in Activation Journey at Databox

At Databox, our activation journey begins with a series of defined steps, each crucial for users to experience the full value of our product.

Setup

The first step in the activation journey is called “Setup” and involves the user performing the necessary actions to experience the value our product can offer them. Since Databox is an analytics app where users work with their own data, connecting the first data source, creating the first dashboard (generated automatically during onboarding), and editing that dashboard are key steps to unlock further value.

AHA

This moment marks the point where users experience the “aha” moment and realize the promised (core) value of our product. Through quantitative and qualitative analysis, we’ve determined that creating at least two dashboards using multiple sources (all data in one place) is essential to unlock the more tangible benefits of Databox for the user.

Habit

The final step encompasses a frequency of component use, where the user must be active for at least seven separate days, complete the “AHA”, and has an activation score above 40. This step emphasizes consistent engagement and sustained activation for long-term user success and retention.

PQL

In addition to our activation steps, we also use the Product-qualified leads (PQL) methodology. PQL is a potential customer that fits our target customer profile and has engaged with our product. In general, a PQL is someone who has:

  • Reached a certain level of engagement within our product (has an activation score >40) and have confirmed purchase intent and suggested a sales opportunity.
  • Demonstrated characteristics that make them a good fit for our product based on demographic and firmographic data.

How to Improve Customer Focus with Activation Score

The section above discusses the activation steps, with which we measure if we delivered the value to the users as quickly as possible. Alongside these steps, we also employ the Activation Score to measure the extent of each user’s activation on a scale from 0 to 100. The Activation Score is designed to evaluate the level of engagement and quality of each interaction with our product, providing valuable insights into prospects’ behavior, which guides our customer acquisition strategy and enables us to enhance key metrics such as signup-to-paid conversion and retention rate.

The activation score is calculated based on a range of actions performed within the product relating to its features and functionalities. Each action contributes points to the overall score, with the distribution determined by factors such as adoption rate, signup-to-paid conversion rate, and retention rate associated with each specific event.

Activation Scoring Model at Databox
Activation Scoring Model at Databox

Each user is assigned a score ranging from 0 points to 100. A score of 0 suggests a low likelihood of conversion and retention. In contrast, a score of 100 indicates that the user has fully embraced the value, has been activated, and is highly likely to convert and remain engaged.

In other words, the higher a user’s engagement, the higher their score. The chart below shows how strong a predictor of lead conversion the activation score is for Databox. It validates the effectiveness of our scoring system in accurately segmenting new signups based on their likelihood of conversion.

How Activation Framework Helps Our Teams

The Activation Framework serves as a cornerstone for our teams and enables them to segment and prioritize their initiatives based on the Activation metrics and scores. This empowers us to find the most promising accounts to optimize and engage with and when to do so.

In general, our Activation framework helps our teams get valuable insights, including:

  • early assessments of the quality of signups generated through different marketing tactics and channels
  • initial assessments of the impact of new onboarding initiatives and strategies
  • guidance for our Go-to-Market teams on lead prioritization, optimal timing for outreach, and required follow-up persistence.

This boosts our teams’ efficiency by improving conversion rates and enhancing overall performance while reducing the resources required to achieve better results. Here is a quick overview of our teams’ involvement in that process.

The Product team is presented throughout the whole customer lifecycle by continuously improving the product’s user experience and interface, providing support through in-app campaigns, improving Activation metrics with the PLG methodology by personalizing the onboarding experience, and conducting growth experiments to increase the customer base.

The Product Marketing Team plays a vital role in guiding users through the various stages of activation with targeted nurture campaigns. These campaigns are delivered through a variety of channels, including email and in-app messages, and provide relevant and valuable information to users, such as guidance on product usage, its benefits, and real-life case studies.

The Customer Support team engages with users through in-app chat and email, answering questions with high responsiveness, collecting and sharing user feedback on the roadmap, troubleshooting cases, escalating issues to the Technical Support team, leading the conversation towards setup services or booking calls to drive the adoption of new users and ensure a smooth customer experience.

The Sales team is responsible for guiding prospects through the Trial period by prioritizing outreach based on Activated and PQL thresholds. They conduct video calls to help users reach business objectives, assist with plan selection, and offer account setups, new features, and paid services before handing off to the Customer Onboarding team to continue the setup process and evaluation of the product.

The Onboarding team is responsible for working with customers after the initial 90-day evaluation period. Each customer is assigned an Account Manager who proactively engages with them to introduce features, offer assistance to improve their product usage, and guide them to reach PQL or either CORE CLF stage.

The Account Management team proactively works with “older” paying customers identified as risk (not activated, etc.) of churning to eliminate potential issues and ensure a positive customer experience.

The Impact of Activation

In conclusion, the Activation framework drives process optimization and superior outcomes across teams. By prioritizing leads based on Activation metrics, we optimize funnels and effectively target potential customers with the highest conversion potential, ultimately leading to improved conversion rates, better retention, increased revenue, and customer growth.

At Databox, we remain dedicated to refining our strategies and innovating to deliver exceptional value to our customers, with Activation being just one example of our commitment to success in today’s competitive landscape.

***

The impact of product-qualified leads or PQLs in our activation strategy is part of a series of technical articles that offer a look into the inner workings of our technology, architecture, and product & engineering processes. The authors of these articles are our product or engineering leaders, architects, and other senior members of our team who are sharing their thoughts, ideas, challenges, or other innovative approaches we’ve taken to constantly deliver more value to our customers through our products.

Tadej Kelc is a Data Analyst at Databox. He specializes in data quality and collection, providing customer insights, and optimizing business operations to drive business growth. He works with teams across the company to identify trends, detect anomalies, and unlock opportunities for enhanced performance. His main impact is helping our customers adopt our product, optimize performance, and reach goals.

Follow us for a stream of technical insights and cutting-edge thoughts as we continue to enhance our products through the power of data and AI.

--

--