Building a customer health score your team believes

A health score earns its keep when the retention desk acts on it without arguing. That takes a short list of inputs, an honest weighting, and a feedback loop from every save.

Priya Raman//7 min read/Analytics

Abstract print of a dial of concentric arcs with one needle

Every health score starts the same way. Someone builds it, the desk works it for two weeks, half the flagged accounts turn out to be fine, and the score quietly stops being opened. The fix is smaller inputs and faster feedback.

Start with four inputs

Service reliability, payment health, sentiment, and tenure. Four inputs are enough to sort a book of 12,000 accounts into a workable order, and each one is defensible in a conversation with a service manager.

InputDefinitionWeight
Service reliabilityMissed visits, skips, reservices in 180 days35%
Payment healthDeclines, days past due, autopay status30%
SentimentLatest survey score and review text20%
TenureMonths on the plan, with month 14 flagged15%
A starting weighting worth tuning after one quarter of outcomes.

A score is a queue, not a verdict. Its only job is deciding which ten accounts get a call today.

Close the loop on every save

When the desk works an account, record what happened and whether the account stayed 90 days. Without that record, the score never learns which signals matter in your market and which ones were noise.

Software that holds the account data, the risk score, and the save outcomes together closes that loop by default. The advantage over a homemade model is that outcomes get captured automatically rather than by discipline.

A score nobody revisits after a quarter is a spreadsheet with better formatting.

Review the score every quarter

  • Compare flagged accounts against accounts that actually cancelled.
  • Drop any input that adds nothing to the prediction.
  • Raise the weight on the input with the longest lead time.
  • Ask the desk which flags felt useful, since they hear the reasons.

Ship a rough score this month. A score at 70 percent accuracy directs attention far better than a perfect one that arrives in the spring.

Priya Raman spent nine years building reporting for field service operators and now consults on data work for home services owners.

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