What’s the Difference Between Persona Scoring and Predictive Scoring?

June 26, 2017 Noam Horenczyk

In the age of big data, B2B marketing and sales need a clear, effective way to make sense of the enormous volumes of data in their Marketing Automation and CRM platforms.

One particularly effective way to turn that indigestible mass of raw information into actionable intelligence is via lead scoring. It just isn’t humanly possible to quickly, efficiently and consistently know which leads or accounts to prioritize without an effective scoring system.

The two most common ways to score your database are via persona and/or predictive score.

Related: 5 Crucial Considerations When Choosing a B2B Predictive Scoring Vendor

In this short post we’ll be explaining the difference between these two modes of scoring as we see them here at Leadspace,  when implementing them into our customers’ flows. It’s worth noting at the outset that these scoring models aren’t mutually exclusive; they can — and in our opinion, ideally should — be used together to provide the most complete and informed picture possible of your potential customers. An example of how to combine the two is provided at the end of this post.

But first, it’s crucial to understand the main difference between a predictive model and a persona model: the actual meaning of the score.

Predictive Scoring

A predictive model predicts the propensity for a certain action to happen. That can be lead-to-SQL, account to closed-won, or any other action in the funnel.

Using Leadspace’s Audience Modeling as an example: we feed the model with positive and negative examples relative to the use case. The 0-100 score represents the propensity of that action to happen — the higher the score, the more likely this action is to occur.

Persona Scoring

A persona model, by contrast, measures the resemblance or “fit” to a defined persona (for example, “demand gen marketer”, “CMO”, “CFO”, “sales ops”, etc).

Leadspace feeds the persona model with 3 inputs:

  1. Top-down approach (the customer definition of what they see as a “demand gen marketer” for example)
  2. Bottom-up data (examples of people who bought the product)
  3. Leadspace analyst research

All three inputs are used to create a robust network of semantic keyword connections that capture the essence of any given persona.

Like predictive scores, the output here is a numeric score as well; e.g. “how closely does this lead fit the definition of a demand gen marketer?” The higher the score, the more confident we are that this person matches the definition of the persona. This confidence is a result of different social keywords that are being captured, clustered, tagged and weighed by Leadspace for each record.

In conclusion: predictive scores provide a likelihood for an action to happen, while persona scores provide a numerically quantifiable answer to the question “does this person resemble my definition of persona X?”.

Here’s a fictional case study (based on common scenarios our customers present us with) to illustrate:

  • You have a predictive model which predicts the “propensity of a company to close a deal of $30K or above”
  • You have persona models for ‘Demand Generation’, ‘Marketing Operations’ and ‘Sales Operations’
  • An inbound lead gets the following scores:

Leadspace Predictive and Persona Scoring for Demand Generation

The meaning of the above is:

  • John Doe works for a company that has a high propensity to buy your product for $30K or more
  • John Doe shows the strongest signals for being in a demand generation position, with less/weaker signals for marketing ops (potentially past role) and even less/weaker signals for sales ops (potentially peripheral aspects of his experience)

Recommended action:

  • Invest resources in reaching out to John since his company is worth it
  • Use content that aligns with demand generation pain-points and solutions in order to get his attention

As you can see, when used together these two scoring models provide a highly useful guide for both sales and marketing to prioritize and segment their campaigns. In fact, in many ways they complement each other, combining data science/machine learning (predictive scoring), with the subjective/human input from your sales and marketing team (persona scoring).

Watch the video below to see how Leadspace combines both Predictive and Persona scoring to provide razor-sharp insights into your customers and prospects:

Contact us to schedule your free demo today.

Picture credit: iStock

The post What’s the Difference Between Persona Scoring and Predictive Scoring? appeared first on Leadspace.

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