Product-led companies often struggle to know which signups to talk to. Since salespeople can’t talk to every user, they look for the ones with the highest revenue potential. That’s why the first step in making product-led sales work is to define product qualified leads (PQLs).
But determining a PQL is tricky. People end up manually creating rules to score leads. This is limiting for three reasons:
- Rules are based on human guesswork
- Rules are brittle and don’t adapt to emerging user behavior
- Rules are slow to change and take up the data engineering team’s time
Instead of only relying on institutional knowledge and intuition, top product-led sales teams leverage artificial intelligence to be scientific about which usage metrics are critical to their business.
This doesn’t have to involve a backlog of requests for your data team. Sales teams can leverage an out-of-box solution to make sense of all the data. The advantages of using machine learning are clear:
Find patterns humans can’t
Finding real value metrics is sometimes counterintuitive. How do you identify the usage signals that strongly impact revenue? For example, it might appear Dropbox users gain value from uploading files. In reality, their greatest 'Aha moment' comes from sharing those files. Machine learning is required to know which usage metrics belong in your PQL and how much to weigh each product signal.
Update criteria based on new data
As the company grows and your product evolves, how people use your product will change too. New product features change your PQL input signals. Machine learning adapts to this new data by refreshing rules and evolving PQL criteria overtime.
Save time spent on data analysis
Machine learning automates hours of data analysis into a short period of time. It can surface insights within seconds, simulating the process of a top rep who has spent years learning about why people buy.
With ML you can arm your sales reps with actionable insights—without spending time gathering them.
Introducing AI-Powered Prospecting by Calixa
Our core vision has always been to streamline the product-led sales workflow. Machine learning further closes the gap of time and resources spent on data analysis. By uncovering deal opportunities quickly, salespeople can focus on having the right conversations.
Calixa’s ML models update PQL scores in real-time, evolving PQL criteria based on emerging user behavior. The best part is that no developer work is needed.
Each ML model is custom built and uses thousands of signals to identify unseen patterns among your current best customers. Then it applies these learned patterns to your user base in search of future best customers. Multiple models can be created to predict any desired outcome—such as free-to-paid conversion and account growth.
Sales reps will see a PQL score ranging from 1 (low potential) to 5 (high potential). With Calixa, you can filter your lead lists by these scores or directly route high scored leads to reps via Slack messages.
Our approach to machine learning
Machine learning is often used as a buzzword. But in a sea of thousands of signups, no human can find the right ones to speak with. Predictive scoring helps you proactively use data to discover how you can better serve and sell to your users. Machine learning makes sales more proactive and productive—given the right guiding principles.
Goodbye black box
We’re committed to being transparent and accessible to all teams. That’s why Calixa shows you the context behind each PQL score. Sales reps will instantly see why it was scored the way it was. These usage insights bring credibility into the sales conversation, which benefits both the sales rep and customer.
The power of machine learning is that it increases accuracy overtime. Did reaching out to the user with a PQL Score 4 then result in a deal? Every action reps take trains the signal inputs (example inputs above) and the model itself to then narrow down the margin of error. After continuous improvement from observing and testing, you’ll get a fine-tuned revenue engine.
AI-Powered Prospecting helps you leverage personalization in the right places. It’s scientific about which leads have diminishing returns, and which deserve hand-crafted messages and attention for higher conversion. You’ll see an increase in sales productivity, more opportunities created, and revenue growth.
Sales is (and always will be) a quintessentially human endeavor, because you’re solving real problems real humans have. By letting machine learning automate the science of data analysis, you can focus on the art of selling.
Get access to our beta
AI-Powered Prospecting is a game-changing way to shorten the product-led sales cycle. Instead of digging through data, sales reps can focus on building more personal connections with sales-ready users. Use ML to help you close more deals and avoid letting them slip through the cracks.
Email us at email@example.com to join our beta program.