Today’s sales teams have more information at their fingertips than ever before. But how do you know what data offers the most insights into buying decisions, and how can you determine the likelihood that someone will become a customer? Thanks to machine learning (ML) models, it’s possible to pinpoint answers, though those answers are heavily informed by training methods.
If you’re in sales, pay attention. We’ll unpack which data signals matter most for sales ML models, and help you develop a strategy that works for your needs.
Understanding the Value of a Sales Propensity Model
Sales teams need to know if a potential customer, or prospect, is likely to move forward with their product. Otherwise, they’ll be wasting their time with unlikely leads. With a sales propensity model, teams can gain the precise insights they need.
A sales propensity model relies on past data to make predictions about whether an account takes action, like booking an estimate. It assigns a score to each account, so you’ll know which ones are the best prospects.
In short, you won’t be hoping for your guesses to be right when it comes to outreach efforts. Instead, your sales team will gain actionable information to help you pinpoint the most promising leads.
Looking at First-Party Signals
Think of first-party data as your most direct information to inform sales tactics. That’s because it comes from customers, meaning that it’s often the most useful and accurate indicator of engagement with your product. Mapping these data signals to a clear sales pipeline example helps teams visualize exactly where each prospect sits, which stage needs attention, and when to trigger the next action rather than relying on gut feel. As a result, first-party signals are an integral part of a strong sales ML model.
First-party signals can cover how many clicks your website gets, or how long people are spending on given pages. You can look at transaction data, like subscriptions and purchases. And signals can also include adoption rates, newsletter sign-ups, and engagement with support.
Maybe someone is on their iPhone looking at your products on your website, for instance, and even adds items to their cart. Someone else, by contrast, might simply be clicking open emails. The prospect spending lots of time on your site is probably the better lead.
Considering Second-Party and Third-Party Data
Beyond first-party data, second-party and third-party data can offer relevant insights that enhance customer profiles. Your sales team can access second-party data from a trusted partnership with another company. In essence, the other company is providing their first-party data to your team.
Third-party data comes in bulk from other anonymous parties. You might get data related to industry hiring trends, product research details, or company growth.
Third-party data won’t be as precise as your internal data, but it can speak to broader trends. Comparing intent data providers to view data source opportunities can help your sales team find the right data solutions and understand signal quality.
While some buyers may make a decision after briefly engaging with your product, others will focus on doing research first. Data signals related to the frequency of site visits and product viewings can be important indicators of promising prospects, too.
Protecting Data
ML initiatives can help you target the right buyers, but they can also be prone to data leakage. Data leakage means that your model is relying on data that wouldn’t realistically be ready when making a prediction. Consequently, data leakage can skew results and lead to misplaced confidence.
Data leakage might mean using revenue details down the road to make a prediction. Or it could mean using support ticket information to inform purchase-related predictions.
Teams should watch for leakage, like premature processing or the use of future data points. Ultimately, it’s best to question any information that does not seem relevant or possible at the time of the prediction.
Monitoring model behavior and looking for overly promising performances are key steps to ensure quality data. Similarly, sales teams should maintain documentation related to data sources and model updates as part of a routine auditing process.
Turning to Meaningful Signals
Building strong sales ML models can help your sales team make the most of its resources and time. Look at first-party data, which reflects the most accurate insights. Carefully evaluate second-party and third-party data, too.
Look for signs of leakage and regularly audit model behavior to prevent inaccuracies. With a diligent approach, you can create effective sales ML models that help you jump on the best leads.
