Customer Health Scoring Software

Health scores that don't reflect reality don't get used

The most common failure mode in customer health scoring is a score built on the wrong signals, or the right signals with the wrong weights. A CS platform's out-of-the-box health score treats all customers the same. We build health scoring engines on top of your actual data: during discovery, we analyse which signals correlate with churn versus expansion in your customer base, then build a pipeline that aggregates those signals from your product, support, CRM, and billing systems.

  • Multi-signal health score engine aggregating usage, support, NPS, billing, and engagement data

  • Configurable signal weighting tuned to what actually predicts churn in your customer base

  • At-risk account alerting and escalation routing before churn signals are obvious

  • Health score trend tracking showing whether accounts are improving or deteriorating over time

Recent outcomes

Voice AI · Research

6× deeper insights

Text-based interviews converted to automated phone calls

AI Automation · Ops

20k+ txns day one

Manual invoice OCR across 40+ gas stations

Loyalty · Retail

1,062 users in 4 weeks

SuperValu & Centra loyalty platform with receipt validation

SaaS · Logistics

2,000+ shipments yr 1

Multi-carrier shipping hub for Indonesian eCommerce

4.9
on Clutch
See our work

The problem

Sound familiar?

  • CS team finding out an account is at risk when they send a cancellation request rather than 60 days earlier when outreach could have changed the outcome?

  • Health scores in your current CS tool that CSMs don't trust because they don't reflect the signals that actually matter for your product?

Short answer

RaftLabs builds custom customer health scoring platforms for SaaS and subscription businesses. The platforms aggregate product usage, support history, NPS and CSAT responses, billing signals, and communication cadence into a single account health score. Health scores surface at-risk accounts weeks before churn signals are obvious and flag expansion candidates before they go quiet. Most projects deliver in 10-14 weeks at a fixed cost.

Key takeaways

  • Signal weights are set by correlation analysis against your actual historical churn events, not a generic default, with recency decay so old signals stop dragging down a recovered account.
  • SHAP-based explainability shows CSMs exactly which signals drove a score up or down, so the number is interrogable, not just observed.
  • Support and NPS signals are typically the earliest-leading churn indicators, often surfacing risk two to four weeks before usage metrics reflect disengagement.
  • A platform aggregating 2-4 signal sources with daily recalculation and at-risk alerting runs $15,000-$50,000; churn prediction modelling and playbook automation bring it to $50,000-$100,000.

Trusted by

Vodafone logo
Aldi logo
Nike logo
Microsoft logo
Heineken logo
Cisco logo
Calorgas logo
Energia Rewards logo

Health scoring software delivery, by the numbers

products shipped
100+
industries served
24+
cost delivery
Fixed
week delivery cycles
10-16

Health scores that don't reflect reality don't get used

A generic CS platform's health score doesn't know your power users log in three times a week while at-risk accounts log in once, or that a support ticket spike means active adoption for one segment and churn risk for another. The resulting score reflects your customer reality, which is why CSMs use it to make decisions rather than ignoring it.

Capabilities

What we build

  • 01
    Multi-signal health score engine

    A composite 0-100 score combines usage, support, NPS/CSAT, renewal proximity, champion engagement, and billing history, with configurable per-segment weighting and SHAP-based explainability behind every score.

  • 02
    Product usage data integration

    DAU/MAU ratio, feature adoption breadth and depth, and recency roll up from your analytics layer to account-level aggregates, visualised as a 90-day sparkline alongside the current figure.

    Built with
    Segment · Amplitude · Mixpanel
  • 03
    Support and CSAT signal aggregation

    Ticket volume, escalations, and reopen rate weight by recency and severity, with sentiment analysis on ticket text surfacing frustration even when volume looks average.

    Built with
    Zendesk · Intercom · Delighted
  • 04
    Segment-level health analytics

    Portfolio views by CSM, segment, and cohort show health trajectory at 30/60/90 days post-onboarding, with an XGBoost or logistic regression churn model as a secondary probability layer.

  • 05
    At-risk account alerting and escalation

    Configurable thresholds trigger CSM notifications with the SHAP breakdown and recommended playbook, plus manager-level escalation for high-value accounts, with alert-fatigue controls built in.

    Built with
    Slack · Salesforce · HubSpot
  • 06
    Health score trend tracking

    Daily-granularity score history lets CSMs see whether a decline is two weeks or two months old, with automated playbook triggers on threshold crossings and recovery-rate reporting for CS leadership.

How we work

From scope to live scoring engine

  1. Week 1
    01

    Signal and churn data scoping

    We map your available signal sources and historical churn events. You leave week 1 with a written scope document and a fixed-price quote.

  2. Weeks 2-4
    02

    Correlation analysis and weighting design

    Signal correlation analysis against your churn history sets initial weights and recency decay parameters.

  3. Weeks 5-11
    03

    Build and integrate

    Signal pipelines, scoring engine, and alerting built in parallel, tested against real account data.

  4. Final 2-3 weeks
    04

    Launch and calibration

    CS team trained on the score, with weights reviewed and refit after 60-90 days of live operation.

Why us

Why CS teams choose RaftLabs

  • 01
    Senior engineers build what they scope

    The engineers who assess your churn data also build the solution. No bait-and-switch, no offshore handoff after the contract is signed.

  • 02
    Fixed price before development starts

    We scope the work, calculate the cost, and lock it in writing before any development starts.

  • 03
    9 years and 100+ products shipped

    Clients include Vodafone, T-Mobile, Aldi, Nike, Cisco, and Lockheed Martin. Track record building SaaS retention and CS platforms.

  • 04
    Weights set from your data, not a template

    Correlation analysis against your own churn history replaces guessed default weights.

  • 05
    Explainable, not a black box

    SHAP breakdowns tell CSMs why an account moved, not just that it moved.

Have a health scoring project?

Tell us what data sources you have, how many accounts your CS team manages, and what your current early warning system looks like. We'll scope a scoring model that reflects your customer reality.

Talk about your health scoring project

Related services

Customer Health Scoring Software, scoped in one call.

Tell us what's broken. Within one business day you get a straight take on cost, timeline, and the right first step. No deck, no pressure.

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Frequently asked questions

The strongest churn predictors are declining DAU/MAU ratio over 30 days, support ticket escalations and reopen rates, missed QBRs, slow response to CSM outreach, payment delays, and non-response to renewal outreach in the 90-day window. Feature adoption narrowing is a consistently strong churn predictor six to eight weeks later. Expansion predictors include growing DAU/MAU ratio with advanced feature adoption and positive NPS with qualitative feedback.

We run correlation analysis between each signal and churn events in your historical data using logistic regression. High-correlation signals receive higher weights; sparse or noisy signals receive lower weights or are excluded. Recency decay is applied so recent signals carry full weight while older signals decay, with weights reviewed and refit after 60-90 days of live operation.

When an account drops into at-risk territory, a playbook is activated: a task is created in Salesforce or HubSpot, or an alert posts to the CSM's Slack channel with account context and recommended next action. Playbooks are configured per segment and account tier, and effectiveness is tracked by correlating interventions with subsequent score recovery.

A platform covering signal aggregation from two to four sources, a configurable weighted scoring engine with daily recalculation, and at-risk alerting typically runs $15,000 to $50,000. Adding SHAP-based explainability, a churn prediction model, and CS playbook automation typically adds $20,000 to $40,000. A full platform with all signal sources typically runs $50,000 to $100,000.

Work with us

Tell us what you need. We'll tell you what it would take.

We scope Customer Health Scoring Software in 30 minutes. You walk away with a clear cost, timeline, and approach. No commitment required.

  • Scope and cost agreed before work starts. No surprises. No obligation.
  • Working prototype within 3 weeks of kickoff.
  • Pay by milestone. You see progress before each invoice.
  • 60-day post-launch warranty. Bug fixes, UI tweaks, and deployment support. No retainer.
  • All conversations are NDA-protected.

Go deeper

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