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 model learns from historical patterns of specific behaviors to uncover which individual leads are on the path to conversion compared to others.
MadKudu continuously scores all your active leads based on their behavior (in-app behaviors, marketing & sales interactions, etc.) to determine which are on the verge of closing.
MadKudu labels your leads with segments that are 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
- Better routing: Send Leads who are ready to convert straight to sales.
- Prioritizing sale’s actions: Your reps prioritize their time based on who is most active in your product.
- 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?
- We analyze your behavioral data to discover key conversion events
- We score leads based on the sum of all their activities in the last 3 months
- We identify usage thresholds leading to conversions (“created 4 projects”)
Learn more about how the Likelihood to Buy score calculation.
F.A.Q.
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 a customer’s behavior at that point of time. This is what we call time decay.
It can 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 4-12 hours because we need to let users play around with your product before we can aggregate the data.
How does the account level behavior impact lead level scores?
If an account is highly active (lots of new leads being captured), this will impact the likelihood to buy of all leads rolling up to that account. The most recent and most active leads will still have a higher score but all leads from the account will be bumped up
