Critical customer signals: the practical model

Most companies claim to be customer-led. Very few can tell you which customers are drifting, why, and what is being done about it this week.

When an account starts to slide, the evidence fragments. Usage lives in one tool. Support friction lives in another. Commercial pressure sits in someone’s head. By the time decline appears in a dashboard, the damage is already done.

The problem is not missing data. The problem is missing structure. Signals works by turning scattered evidence into shared understanding and then into disciplined action.

From early signal to action

A practical model needs three layers. Signals provide the evidence, drivers explain the evidence, and operating rhythm turns explanation into action.

1) Signals: observable change

Signals are specific, observable changes in customer behaviour. They are not opinions and they are not forecasts.

Examples include:

• usage drops sharply over a short period
• multiple escalations in a single month
• a known champion changes role or leaves
• sentiment drops materially
• renewal approaches with no active engagement

Signals matter because they show movement. Metrics describe the past. Signals highlight change before it hardens into trend.

2) Drivers: explained risk

On their own, signals are noise. Drivers group related signals and weight them by impact so risk can be explained rather than guessed.

Common drivers include:

• engagement: usage patterns, feature adoption, activity decay
• support strain: ticket volume, escalation rate, repeat issues
• sentiment: surveys, stakeholder feedback, relationship health
• commercial pressure: renewal timing, expansion activity, deal size

Each driver carries a score. Combined, they form the account health score. The score is traceable, which means you can always see what moved it and why.

3) Operating rhythm: back to green

Once risk is visible and explained, action becomes possible. A back-to-green plan is not a slide or a note. It is structured work where the team identifies the top risk drivers, assigns owners and deadlines, defines the expected impact on health, and reviews progress regularly.

This is the shift from firefighting to operating discipline.

Why this works when health scores fail

Most health scoring models collapse because they live in one tool and one team. Success sees engagement. Support sees pain. Sales sees pipeline. Leadership sees outcomes. Nobody sees the whole picture in time to act.

This model works because it is cross-functional by design, with signals coming from everywhere and risk treated as shared. It explains itself because scores are evidence-based rather than abstract. It creates accountability because actions have owners, dates, and intent.

This is not reporting. It is coordination.

Start where it matters

Do not start with every account. Start with the ten that matter most, the ones that would hurt if they churned.

Build the driver model, run the rhythm, prove it works, then scale. Signals is not about coverage. It is about control.

The outcome

When this system is in place, conversations change. You stop asking whether an account is healthy because the evidence is already visible.

• these are the accounts at risk
• these are the drivers creating that risk
• this is the work underway
• this is how we will know if it is working

That is not optimism. It is an operating system.