Boost the score of personal emails that are tied to qualified accounts

As a Marketing Ops leader, there are many business questions we need to tackle every day. With MadKudu, we help make answering those questions easier.

Story

One of the business questions that we've seen many customers have is:

"How do I boost a personal email tied to a qualified account to at least a good score?"

The trigger for this is that leads with personal emails that are tied to a good account are not shown as qualified to Sales. These personal emails typically come from ads (Facebook, LinkedIn, etc) where prospects would enter their email addresses that are non-corporate.

As a Marketing Ops leader, the objective is to boost a personal email tied to a qualified account to at least a good score.

How to do this via the MadKudu platform?

Here's a step by step guide:

(1) Navigate to the Data Science Studio homepage. Duplicate the live Customer Fit model that you have in production.

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(2) Navigate to the duplicated model > Data Science Studio > Overrides. Create an override with the target segment to boost them to at least good.

The override will typically consist of:

  • is_personal = 1 (to denote that the lead is a personal email)
  • an account-level field to denote that the account is a high quality account
  • if you have a customer fit score on the account object, you can pull the account.mk_customer_fit_segment
  • if you don't but have any other account-level fields to denote quality of account, you can use it here
  • [optional] any person-level field to denote an ICP such as job title or seniority.

Here's an example:

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(3) Open up the live model and duplicated model in separate tabs. Navigate to the Validation tab for both models and compare side by side whether the performance has greatly decreased in terms of conversion rates for each segment (refer to the second section in the screenshot).

Note: typically new target segments don't perform well on historical data so we do expect the performance to decrease slightly. If the performance decrease did not drop too much, we are fine to move forward. Feel free to reach out if you'd like any advice.

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For an additional check, multi-fit deploy the duplicated model. Once the performance page has been generated, you can compare apples to apples if the recall for each model has decreased over the last 6 months. This additional check could take up to 3 days to process the new model and performance page.

Need more Technical Documentation?

Head over to our Zendesk and find information regarding integrations, scoring details, and more.

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