Run Predictive Insights actions to score and enrich account data and gain a better understanding of customers and prospects.
Predictive Insights enables you to carry out further actions on your dataset that give you better insight and understanding of prospects. These include scoring and enrichment of your datasets.
Scoring is also known as inference or prediction, is the process in which you take a dataset and run it through a trained model in order to assign the accounts with predictive scores for prioritization.
Scores range from 1 to 100. This represents how much of a fit the prospect is, rather than the probability of it becoming a customer, when compared to your existing customer base. Higher scores are ranked as “A” while lower scores are ranked as “D”. Note that an account that does not match any attribute will score zero or rank “E”.
When Predictive Insights scores the accounts, you can tier the ranks according to your needs. For example, you may want to target the top 10% of accounts operating in a specific geography. To do this, you take the scored account and adjust the ranks in the Predictive Insights model.
Enrichment is a process that uses the attributes gathered by Predictive Insights to learn about specific companies in a dataset. You do this separately from the predictive scores that are received from running a score action against a predictive model.
Enrichment is used when you want to know facts and insights about companies, whether they are already a customer, or a prospect. In short, Enrichment takes the knowledge you already have on customers and prospects, and adds more useful information to it, such as intent attributes. You gain a better understanding of the account's needs, preferences, and behaviors. This information helps sales teams tailor their approach and messaging to the specific needs of each account, improving the chances of making a sale. Enrichment also gives sales teams improved targeting, as you can use enriched data to identify potential opportunities more effectively.