Are you using MadKudu to score your leads? If so, you're already ahead of the game! But did you know that involving your sales team in the process can make your scoring even more effective? Here are some tips to get your sales team on board and improve your scoring models.

Understanding Your Sales Team's Perspective on Scoring

First things first: it's important to understand your sales team's perspective on scoring. You can do this by asking them the right questions. During your next Marketing & Sales meeting, try asking questions like:

• What do you think of the recent scores you've seen?
• How are you using the scores in your outreach and sales process?
• Are there any challenges or pain points you face when using the scores?
• Do you feel that the scores accurately reflect the quality of leads and accounts?

By gathering their thoughts, you'll get a better sense of how your sales team is using the scores and identify any issues they're experiencing.

Building Trust into the Scores

Don't stop there! Once you've gathered their feedback, it's time to take action. Here are some actionable questions to deep dive into specific qualified or disqualified leads and start optimizing your MadKudu models. The best here would be to have the sales team prepare a list of leads that they agree or disagree with, thanks to the feedback loop you implemented with them.

Take a lead example with the sales team and ask if they understand why this lead was qualified or disqualified by MadKudu.

You'll find very useful links below to help you act upon your sales team's feedback, but you're welcome to open a support ticket to get more assistance in this process!

If the sales team thinks there is too little information to understand the score of this lead

• Typical questions: Which fit or behavioral signals would have clarified the score for you?
• How to answer and take action:
  • The sales team may have seen additional data points in your CRM, data warehouse, product data platform, and marketing automation platform that are currently not part of the model
  • Include those data points in the model to ensure a full alignment between what the sales see in the CRM and what is integrated into the model

If the sales team thinks the score should be higher or lower

• Typical questions to ask: Should the score of this lead be higher or lower? What should be the segment of this lead?
• How to answer and take action:
  • Explain the score of the lead through the Score Lookup tool, or through the Insight pages of your Data Studio. For instance, you can explain how a particular industry is correlated to conversion, even if it’s counter-intuitive for your sales team
  • If you need help explaining the score, feel free to open a ticket
  • Review if any scoring rules are currently strongly impacting the score of your lead and revisit them

If the sales team noticed the score changed in a way that they do not understand

• Typical questions to ask: Which specific score evolution do you not understand?
• How to answer and take action:
  • Enable your Sales team to understand the difference between the real-time score and the batch score
  • The real-time score is the first quick estimation of a lead's customer score based on the information MadKudu natively has. Then, it’s completed within 4 to 12 hours with the full scope of enrichment and is scored again in batch, with a more accurate score.

If the sales team does not understand the signals

• Typical questions to ask:
  • Which element in the signal is not clear? The wording? The priority? The content? The usefulness?
  • Deep dive into their misunderstanding through the following questions:
    • Are the Customer Fit signals not explaining the Fit score properly?
    • Are the Likelihood To Buy signals unclear?
    • Do you see contradictory information in MadKudu signals vs in your CRM? Example: a lead is marked as working in the media industry in MadKudu signals, but in your CRM the 'Industry' field says 'Software'
    • Is a lead scored with a high Likelihood To Buy score showing no Likelihood To Buy signals at all?
• How to answer and take action:
  • Make sure to reword the fit signals properly in your customer fit model. They’re fully customizable.
  • Likelihood to Buy signals are based on the latest activities performed by a lead. If there’s too much noise, feel free to remove some of the activities in the Data Studio. On the contrary, if you see a lead scored very active showing no Likelihood To Buy signals, it’s likely because you removed a large part of the signals.
  • If you see contradictory information in MadKudu signals vs in your CRM, make sure to include all the relevant enrichment coming from your CRM in your signals

What's next?

Involving your sales team in the lead-scoring process can greatly improve the effectiveness of your MadKudu models. By understanding their perspective and building trust in the system, you can optimize your scoring and generate higher-quality leads. So, don't hesitate to ask your sales team for feedback and work together to achieve your goals!