The Science of Match Scoring: Predicting Customer Engagement

Before we start interpreting the match score, let me explain how we define it. At a technical level, match scores measure the similarity or approximate similarity between two objects. In the world of artificial intelligence, objects are usually some form of text that describes how an entity communicates who or what it is.

Here’s an example of where you’d use one: Company A wants to launch a new product, so it will create a compelling message for an advertising campaign that describes its unique offering—and to maximize resources, budget, and efficiency—target only consumers. With a match (or lead) score over a certain amount. The higher this number, the higher the indication that the customer is likely to engage with the advertising campaign. Those who engage are also more likely to convert or purchase.

How can you connect potential customers to a new product? The first step is to identify two objects—we also refer to them as entities—to measure the relationship between the two. In the example above, the first object would be a product description and the second would be a user description, which could be a social media profile such as a LinkedIn profile. Note that if you have a small set of customer data, you can perform the matching manually. It may take a long time, but it is possible. Let’s say you have a user data set of one million users; There is no other way to do this than through automation (that’s where we’re going because we specialize in applying AI to very large data sets).

I’ll spare you the technical details, but once we have the two entities, we can see how closely the language around them shares similarities or similarities.

The power of a match score lies in its inferential power, or its ability to predict the likelihood of a strong match or a weak match.

For a sales or marketing team, high customer-brand match scores suggest that a brand message or description will resonate positively with high-scoring customers and less positively with low-scoring contacts. Note that I did not use the term “negative resonance”; Customers may have lower match scores because they are not as familiar with the brand, but in a growing campaign they may end up with a higher match score because they indicate more brand awareness.

Conversely, high-scoring contacts may indicate that their public personas are more knowledgeable about the brand category and therefore do not require the same level of education as their low-scoring counterparts.

The question most clients ask is, “What does a high or low score represent?” Generally, scores can range from 0 to 100, with a high score being over 60. A score above 60 usually indicates someone who is an innovator or someone who publicly expresses a higher quality of the brand or product.

Individuals with scores below 35 may be considered disinterested or unfamiliar with the content of the comparison item.

The most important thing is that the scores should be viewed in a context that includes the sample population (does your CRM match the warm approaches or from the cold list?) and industry (is your product super technical or easy to understand?), and possibly other variables. Match points can give you a statistically significant indication of who should be penalized based on the goal you are trying to achieve. Marketing and sales efforts that include match points will be much more effective because they allow for more informed targeting of advertising and information efforts than typical casting of a wide net where everyone is considered part of the same game. Valley.