Wave a magic wand and tell me where I can find more revenue. Every go-to-market leader would love to do that, but the reality isn’t quite there yet (otherwise, no one would be missing ARR goals).
However, we are closer than ever to true magic in go-to-market tech. More data exists within every organization, ready to mine for interesting insights and help teams know the previously unknowable.
Ever wondered what attributes make a customer ready to convert from a free plan to paid? Or what signals indicate a potential expansion opportunity? Getting these answers often means months-long data science projects and technical back and forth that delay actioning these critical insights.
Not anymore. With Pocus’ latest feature, Predicts AI, go-to-market teams can tune their motion without engineering or data science support.
What is Predicts AI?
Predicts AI recommends Playbooks based on the product and firmographic signals most predictive of conversion. Unlike other AI scoring tools, Predicts AI recommends Playbooks based on a specific goal. For example, if your goal is to convert free users to paid, Predicts AI will recommend Playbooks with the highest likelihood of achieving that goal, or perhaps it’s to find expansions with existing customers, you’ll get different recommendations for that goal.
Predicts AI goes beyond scoring to give you recommended Playbooks to run instead of providing a binary score about an account’s propensity to convert.
Traditional scoring models often focus on a point in time within that journey like top of funnel acquisition (MQL) or the moment a user becomes product qualified (PQL). Unlike traditional scoring tools, Predicts AI looks at the entire customer journey and recommends the Playbooks your team should run to hit specific goals at different points in that journey.
Create magic for the team
Help your teams understand how Playbooks impact conversion rate on goals without running to the data science team.
Skip the guesswork
No more blind experimentation, you can put recommended Playbooks into action and measure performance. Confidently iterate your Playbooks and overall motion based on performance data (not your best guestimate).
Know the unknown
Uncover new signals that may be predictive of conversion that you didn’t previously consider.
Why we built Predicts AI?
Sometimes you just need a little bit of magic (aka science) to balance the art of building and scaling your Product-Led Sales motion. In many cases, go-to-market teams have strong hypotheses for what product, firmographic or other intent signals are most predictive of conversion. At the core of building any go-to-market strategy is a strong understanding of your ideal customer profile and the actions those customers take before they buy and after they start using your product. This is often a good enough starting point to begin pinpointing key signals and building sales Playbooks.
As PLG companies mature and reach the stage of a Miro, Asana, or ClickUp, they may build internal data engineering and data science teams to begin doing the quantitative analysis to complement those original hypotheses. These teams can use machine learning and AI to build models that answer questions about which signals are predictive, backed by real data. However, these teams are usually quite busy and low on bandwidth for internal projects for the go-to-market team.
We built Predicts AI to speed up the process of doing this ML backed analysis so go-to-market teams, whether they are large or small, can experiment faster.
How Predicts AI compares to AI lead scoring tools
Predicts AI is a complementary tool within Pocus’ broader Revenue Data Platform. Unlike lead scoring tools which only push data to third party tools, Predicts AI recommends Playbooks that can be run in Pocus or pushed to third party tools - leaving the choice up to you.
- No black box models: AI scores that get pushed to third party tools are meaningless without additional context. Sales reps need to understand why an account is ripe for conversion, not just that it scored “excellent.” Predicts AI recommends Playbooks so you can do data-backed prioritization with full context behind the why.
- Flexible and no-code: RevOps teams can take AI recommendations and put them immediately into action without data science or engineering support.
- Support multiple goals: Pocus Predicts AI model is built to recommend Playbooks based on a specific goal not just ICP fit.
How it works
Step 1: Historical analysis of your customer data across the entire journey
Predicts AI uses your historical customer data about conversions to pinpoint signals that show higher propensity for conversion. What’s unique about Predicts is each model is trained on a different goal like free to paid conversion, expansion, upsell etc. Unlike AI scoring tools that look at signals globally without segmenting based on the type of conversion.
Step 2: Recommend Playbooks you can implement with one-click
Once we run some AI magic, our model recommends Playbooks based on patterns found in historical conversion data. Pair this with your own knowledge of the business to implement or dismiss recommendations with one click.
Step 3: Tune and iterate
Let your new AI recommended Playbooks run and measure performance in the Playbooks reporting tab. See how AI Playbookss convert compared to Playbooks built from your own hypotheses or community recommendations. With visibility into what opportunities are surfaced and how they convert, you have everything you need to continuously tune and iterate your motion.
How customers use Predicts AI to improve Playbooks performance
Customers use Predicts AI to improve their Playbooks performance continuously, outside of their typical iteration cycles. Playbooks require a consistent cadence of iteration, depending on your business model and strategy. This might mean iterating monthly, quarterly, or annually. Predicts AI Playbooks offer teams recommendations along the way. With the ability to quickly put these new Playbooks into action with a click, RevOps can spend more time monitoring performance and less time ideating, gathering data, and operationalizing.
See Predicts AI in action
Want to experiment with Playbooks faster? Curious what Playbooks your team should be running? Chat with the team to learn more.