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How to Optimize a Lead Scoring Model with More Intelligence

By Scott Ingram

RelationshipOne spoke with Hannah Kranich, Director of Demand Generation at Dow, on episode 76 of the Inspired Marketing Podcast. The conversation entailed how B2B marketers can improve lead scoring by adding more intelligence to it.

The right person at the right company with the right message

Lead management involves three major steps: identifying potential leads; knowing the right time to contact them; and providing the right content to turn that prospect into a lead. From there, marketers nurture leads to hand them off to sales to make the deal. Lead scoring lets you know how good a lead is and whether they’re ready to be moved down the marketing funnel.

Kranich’s team had been working with a traditional, yet fairly advanced B2B lead scoring model. Their colleagues in sales would look at businesses that were the best fits. Sales was familiar with the types of leads coming in, which were marketing-qualified leads based upon their engagement, whom they worked for, and their titles.

However, Dow wanted to take this model one step further, and look at a prospect’s propensity to buy. They essentially wanted to answer why is this business one we should be targeting and why now?

Doing so would help them drive revenue faster by targeting the right person at the right company with the right message. Kranich emphasized that many teams aspire to do just that, and while it might sound simple, it is actually rather complicated. After all, doing so requires identifying which customers are the right fit and when they’re in the market.

Starting with clean data

The first step was to get clean data. To do so:

  • They thought through how their AI-driven data models should be aligned and organized around their product lines

  • They put two years’ worth of wins and losses into the system to build out their models with clean data and establish a strategy

  • Once Dow had an analysis and a working model, they connected their marketing automation platform and their CRM platform to share data

Dow’s next steps will involve digging into the data and staying both data-driven and data-inspired. They are working with their own internal customer data platform (CDP) and data team to create different types of reporting. They are also using cross-sell tools to identify which businesses are the right ones to cross sell to with the help of this reporting and their other data.

Adding more intelligence to lead scoring pays dividends

As a result of their efforts, Kranich’s team have a greater alignment between marketing and sales, because the teams are working from the same set of data. Targeting has become much more important to them as well as identifying what’s important to their customers. They are also seeing faster speed to lead and greater opportunity pipeline.

Dow’s lead scoring journey started with clean data and lead to stronger targeting and better alignment between marketing and sales, which garnered results.

For more information about lead scoring, please:

  • Read How to Become a Lead Scoring Superhero
  • Find out how to Do More with Lead Management
  • Check out Oracle Eloqua

Source:: http://feedproxy.google.com/~r/itsallaboutrevenue/~3/BIN9fsMlWGQ/how-to-optimize-a-lead-scoring-model-with-more-intelligence