Correlated works with leading PLG companies like Intercom, LeadIQ and Circle to help power go-to-market playbooks across their customer lifecycle. One of the thorniest problems we’ve heard customers experience is figuring out which self-serve users or accounts to target with sales, marketing or CS playbooks.
Many of our customers have invested 12-18 months and in some cases millions of dollars building out custom data science models for product qualified lead (PQL) and product qualified account (PQA) scoring methodologies. After hearing this from so many customers, we thought there had to be a better way!
Correlated’s PQL Scoring Engine
If you’re looking to implement PQLs without devoting months of engineering time and countless dollars in resources, you’re in luck! With Correlated’s new PQL Scoring Engine powered by Machine Learning, you can set up PQLs and PQAs with just a few button clicks. Rather than taking several months to implement a scoring methodology, Correlated handles all the hard work of wrangling your data, training a model, and predicting results so that you can get to a prioritized, product-qualified lead funnel out of the box.
Here’s how Correlated’s new Scoring Engine changes the game:
- Incredibly fast time-to-value to discover the best PQLs and PQAs in just a couple button clicks
- No black-box magic - understand what indicators are most correlated with conversion and expansion
- Integrated platform that automates GTM Playbooks so that you can DO something with your newfound knowledge in the tools you use everyday like Salesforce, Outreach and Slack
Why you should implement PQLs
If you’re building a product led growth (PLG) SaaS product and you haven’t implemented a PQL methodology yet, now is the time! This is particularly true if you have a self-serve product that serves as top-of-funnel for your sales and customer-facing teams.
Buyers no longer want to go through multiple demos and multiple conversations with sales teams in order to access the product. They want to self-qualify and vet the product themselves. This doesn’t mean that sales teams aren’t important. Instead, it means that sales teams can now have a better conversation with a qualified buyer who has already seen the product, knows what they want to achieve, and is looking to buy. The sales team can then focus on expanding the contract size or helping customers get across the finish line.
PQLs are a key piece of the puzzle.
No sales team has time or interest in digging through data to figure out which leads are the most active in the product, or who is exhibiting the most likely indicators that they are ready to buy. They are busy selling! That’s where PQLs can really increase the quality and effectiveness of your entire sales motion.
With a PQL methodology that qualifies leads and routes them to the right team, you’ll be able to act on the best leads, and nurture the users and accounts who aren’t quite ready to convert yet.
What you get from Correlated’s PQL Scoring Engine
Correlated’s PQL Scoring Engine uses machine learning to analyze all the data you connect to Correlated (like your CRM, product analytics, and so on) to discover the best indicators for conversion or expansion.
You’ll get a report that tells you the top indicators that a person or account will convert, discovered by our Scoring Engine. You can improve the model as needed by leveraging your own domain expertise to highlight which indicators you think are relevant (or not relevant). The model will also generate predictive scores that grade your Accounts and Users on a scale of 1-5 based on likelihood to convert or expand. Pretty cool, huh?
You probably have a bunch of questions about how this actually works.
How does my data get loaded into Correlated in the first place?
Correlated supports multiple data sources, including Segment for real-time event data, Cloud Data Warehouses like Snowflake, BigQuery and Redshift for customer and user data, and CRMs like Salesforce and Hubspot. You can connect these integrations to Correlated, configure things to map to your organization’s unique data model, and Correlated will join everything into a holistic view of all your Accounts and Users.
How does Correlated know how conversion or expansion is defined within my organization?
Correlated has an incredibly flexible data model that can be configured to match your unique organizational needs. You can specify conversion or expansion goals using our dynamic, no-code logic builder so that our models can predict indicators uniquely catered to your organization.
What if the results don’t make any sense?
Our Scoring Engine enables you to iterate easily. If your results don’t make sense to start, you can easily improve the model to test new hypotheses by removing or adding back certain data points.
Correlated’s Scoring Engine brings the power of ML to you
Correlated opens up a whole new world for GTM teams at SaaS companies. Now, you can easily implement and iterate on your GTM hypotheses, leveraging machine learning and data science to guide your work. However, it’s important to note that Correlated is not meant to replace internal data science initiatives, but rather to augment it and be a supporting extension of your team. Many of our customers still leverage internal data science models and use our functionality on top of that work.