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Likelihood to buy

Have you ever dreamed about sending your leads to Sales when they’re really active within your app? By using your in-app data, we’ll tell you exactly when your leads are engaged with your product so that you can talk to them when it’s the most relevant time.

Doing so ensures that you always talk to your leads at the right time.

Introduction

MadKudu’s likelihood to buy models learns from historical patterns to uncover specific behaviors that separate leads who were on the path to conversion from others.

MadKudu continuously scores all your active leads based on their behavior (in-app behaviors, marketing & sales interactions…) to determine which are on the verge of closing.

MadKudu labels your leads with segments simple to understand and to act upon. The possible values are:

  • Very High: Power Users
  • High: Active Users
  • Medium: Occasional, reactivated or newbie users
  • Low: Phantoms, zombies, at-risk, slipping away or one and done users

Main Use Cases

  1. Better routing: Send Leads who are ready to convert straight to sales.

  2. Prioritizing sale’s actions: Your reps prioritize their time based on who is most active in your product.

  3. Better marketing campaigns: Send different offers based on user’s engagements. Send product video to those who didn’t engage at all and send advanced content to those who really engaged.

How is it computed?

  1. We analyze your in-app data to discover key conversion events

  2. We identify usage thresholds leading to conversions (“created 4 projects”)

  3. We score leads based on their usage data

Frequently Asked Questions

Will the scores be updated over time?

The scores are constantly updated depending on the behaviour of the user. If we find that a power user with a “high” likelihood to buy changes to an occasional user with a “medium” likelihood to buy in the next month, this means that the user has stopped being active.

Scores will gradually decay over time to ensure that the scores accurately depict customer’s behaviors at that point of time. This is what we call time decay.

It can be even be interesting to reach out to people whose score are decreasing to let them know you’re here to help.

How often do you update scores?

We update scores every 4-6 hours because we need to let users play around with your product before we can aggregate the data.