Customer Scoring
Customer scoring assigns a numeric value to each account based on attributes like fit, potential value, product usage, and churn risk, helping teams decide where to focus effort.
In depth
A scoring model combines firmographic fit signals with behavioral and outcome data, then weights them into a single number or tier. Unlike lead scoring, which predicts whether a prospect will buy, customer scoring looks past the sale to predict who will expand, advocate, or churn, which shifts attention from acquisition to retention and growth.
The classic pitfall is over-indexing on activity that looks like engagement but does not correlate with value, such as login counts that include admins clearing notifications. In a quiz-funnel context, the same tiering logic Pivix uses to band quiz respondents into Hot, Warm, or Cold can be extended post-sale, so a high-fit lead who scored as a Hot tier at capture is flagged for white-glove onboarding rather than a generic email sequence.
Example in practice
Frequently asked questions
How is customer scoring different from lead scoring?
Lead scoring predicts the likelihood that a prospect will buy, while customer scoring evaluates existing accounts for expansion, advocacy, or churn risk. One serves acquisition; the other serves retention and growth.
What inputs go into a customer score?
Typical inputs include firmographic fit, product usage depth, contract value, support sentiment, and renewal proximity. The right mix depends on which signals actually correlate with value in your data.
Can quiz tiers feed a customer score?
Yes. The Hot, Warm, or Cold tier a respondent earns in a Pivix quiz captures fit at the start of the relationship. That tier can carry into a post-sale score to prioritize onboarding and success efforts.