2.5 KiB
2.5 KiB
Churning Deals Warning – Early Alert System
Context & Objective
The current Churn Report focuses on deals that are already churned — either through a formal cancellation or after 12 consecutive months of inactivity. However, by the time a deal is flagged here, it's often too late to take action.
To help Account Managers intervene earlier, we're introducing a new “Churning Deals Warning” section. This section will identify at-risk accounts based on behavioural patterns, enabling proactive outreach and retention efforts.
Proposed Criteria for “At-Risk” Accounts
We aim to flag accounts showing early signs of disengagement or decline. The criteria below are initial ideas and open for iteration.
1. Sharp Drop in Monthly Bookings
- Flag deals where monthly bookings have dropped by 70% or more compared to the average of the previous 3 to 6 months.
- Only apply this check to deals that had a minimum threshold of activity in the past (e.g., at least 10 bookings/month on average) to avoid noise from small or sporadic users.
2. Sustained Inactivity
- Flag deals that have had no activity (0 bookings) in the last 3 to 6 months.
- This helps catch accounts before they hit the 12-month churn threshold.
3. Step Change in Listings
- Flag deals that have seen a significant drop in the number of active listings (e.g., 50%+ drop compared to 3-month average).
- A drop in listings often precedes a drop in bookings.
4. Other possible options
- Track accounts that haven’t had any contact with their Account Manager in over 6 months.
- Track accounts that have bad CSAT score on their bookings (less than 2-3).
Purpose of These Flags
The main goal is not to automate action, but to guide Account Managers' attention to potentially declining accounts. With earlier signals, they can:
- Reach out before accounts fully disengage.
- Investigate potential issues (e.g., pricing, onboarding, product fit).
- Offer support, incentives, or solutions.
Implementation Ideas
- Integrate this section into the existing Churn Report, under a new tab or visual.
- Allow filters by:
- Account Manager
- Warning reason
- Region / Country
- Deal Size (segmentation)
- Business Scope
- Show “reason for flag” per deal (e.g., “Bookings dropped 75% in last month”).
Next Steps
- Finalize flagging criteria with input from AMs and data team.
- Build the logic and integrate into dbt / Power BI.
- Gather feedback from pilot usage.