# How to Run a Renewal Risk Review (Scoring Model Included)

> A renewal risk review scores every account near its renewal date on five weighted factors and ends with a save plan for each account below 50.

Source: https://clarivoxx.com/articles/renewal-risk-review · Author: Abdessamad Ghanem · Clarivoxx · Customer Success · Updated 2026-09-03

Here is how I run a renewal risk review. A renewal risk review is a meeting every two weeks where you score each account in the renewal window on a few weighted factors and agree a save plan for the low scorers. The renewal risk score I use is Σ (factor weight × factor score from 1 to 5) ÷ 5 × 100: an account with a weighted sum of 2.20 out of 5 scores 44, which puts it in the high-risk tier.

This is Part 2 of The Renewal Playbook. Part 1 built a five-stage [renewal process for B2B SaaS](https://clarivoxx.com/articles/renewal-process-b2b-saas), and this part turns its first stage into a scoring model and a meeting. To see what actually renews each quarter, use the free [renewal rate calculator](https://clarivoxx.com/tools/renewal-rate-calculator).

## Why isn't a health score enough for renewals?

A health score mostly reflects usage across your whole base. A renewal risk score covers only accounts near a contract date and adds commercial factors: budget, buyer changes, price increases and competitors.

An account can look healthy and still be at risk. With one hotel partner I worked with, bookings were steady and tickets were few, so the account looked green everywhere. Then a new regional director arrived and started reviewing every distribution contract. Usage data told us nothing about that. A five-minute call with our contact would have.

If you already run a [customer health score model](https://clarivoxx.com/customer-health-score-model), use it as one input. The guide to [customer success metrics](https://clarivoxx.com/customer-success-metrics) shows where it fits.

## How do you calculate a renewal risk score?

Keep the model small enough to score an account in five minutes.

```
Renewal confidence score = Σ (Weight × Factor score) ÷ 5 × 100

Factor score  = 1 (poor) to 5 (strong) for each factor
Weight        = the factor's share of the total; weights add up to 1.00
Factors       = Usage trend 0.30, Champion and sponsor 0.25, Value realized 0.15,
                Support signal 0.15, Commercial factors 0.15
Tiers         = 75 to 100 low risk, 50 to 74 watch, below 50 high risk
```

What each factor means:

- **Usage trend:** active users over the last 90 days against the prior 90.
- **Champion and sponsor:** an engaged champion and access to the budget owner. A departed champion scores 1.
- **Value realized:** progress against the goals agreed at kickoff or the last review.
- **Support signal:** open critical issues and recent escalations.
- **Commercial factors:** budget pressure, a planned price increase or a competitor evaluation.

## What does a scored account look like?

Here is an illustrative example: a $140K ARR account whose champion left last quarter.

| Factor | Weight | Score | Weighted |
|---|---|---|---|
| Usage trend | 0.30 | 2 | 0.60 |
| Champion and sponsor | 0.25 | 1 | 0.25 |
| Value realized | 0.15 | 2 | 0.30 |
| Support signal | 0.15 | 4 | 0.60 |
| Commercial factors | 0.15 | 3 | 0.45 |
| **Total** | **1.00** | | **2.20** |

The score is 2.20 ÷ 5 × 100 = **44**, below the high-risk line of 50. This account needs a save plan now, not at day −30.

Treat the weights as a starting point and check them against real outcomes after two or three quarters.

## How do you run a renewal risk review meeting?

Plan 45 to 60 minutes every two weeks, with CSMs, account managers and a CS leader.

1. **Score before the meeting.** The meeting is for decisions, not data entry.
2. **Sort by ARR at risk,** then check accounts that dropped 10 points or more since the last review.
3. **Ask three questions per account:** what changed, why, and what is the next action, with an owner and a date.
4. **Update the forecast category** so it reflects what the room just learned.

![How to Run a Renewal Risk Review (Scoring Model Included) — A photorealistic scene in a dark premium meeting room with w](https://clarivoxx.com/article-images/identify-customer-risks-early.jpg)

## How do I read the whole renewal portfolio?

Illustrative portfolio: 38 accounts renewing in the next 120 days, worth $2.9M.

| Risk tier | Accounts | ARR | Share of ARR |
|---|---|---|---|
| Low risk (75–100) | 21 | $1.55M | 53% |
| Watch (50–74) | 11 | $0.85M | 29% |
| High risk (below 50) | 6 | $0.50M | 17% |

Shares are rounded. The high-risk tier holds $500K across only six accounts, so each one needs its own save plan. The watch tier is where next quarter's surprises come from. Check the result each quarter with the [renewal rate calculator](https://clarivoxx.com/tools/renewal-rate-calculator).

![How to Run a Renewal Risk Review (Scoring Model Included) — A photorealistic scene in a dark navy modern office with tea](https://clarivoxx.com/article-images/reactive-support-to-proactive-customer-success.jpg)

## Which save play fits each weak factor?

Match each low factor score to a standard play:

- **Low usage:** training for the teams that stopped logging in.
- **Lost champion:** map the new stakeholders within two weeks and ask to meet the budget owner.
- **Weak value:** a short outcomes review with goals reachable before the renewal date.
- **Poor support signal:** a named technical contact and a written fix plan.
- **Commercial pressure:** bring in the account manager early with options, not discounts.

Tie these plays to your [customer health score alerts](https://clarivoxx.com/articles/customer-health-score-alerts), so a drop between meetings still reaches someone.

## What I've seen in practice

- **Champion loss beats usage as a warning.** On one team, we found that most of our losses had a low champion score and a fine usage score, so we moved weight from usage to champion.
- **Green dashboards miss commercial change.** The hotel partner above is the reason commercial factors have their own line.
- **Score before you meet.** If scores are filled in live, the hour goes on data entry and nobody leaves with a save plan.
- **Automate what is objective.** Usage and support signal can come from your systems. Champion and commercial factors stay CSM judgment.

## My renewal risk dashboard

I open every review with this table, then go to the accounts. This review uses the portfolio and Account A from the examples above.

| Metric | Target | This review | Last review | Change |
|---|---|---|---|---|
| High-risk accounts | 3 | 6 | 5 | +1 |
| High-risk ARR | $0.25M | $0.50M | $0.40M | +$0.10M |
| Watch ARR | $0.60M | $0.85M | $0.80M | +$0.05M |
| ARR in watch or high risk | 30% | 47% | 43% | +4 pts |
| High-risk with a save plan | 6 | 4 | 3 | +1 |
| Account A score | 50 or more | 44 | 58 | −14 |

Account A dropped 14 points in two weeks, so it goes first on the agenda. Then I chase the save-plan row: two high-risk accounts still have no named next step.

This week, score your five largest renewals in the next 120 days and book a save-plan conversation for any that lands below 50.

## Frequently asked questions

### How often should you run a renewal risk review?

Every two weeks for accounts within 120 days of renewal. Monthly is too slow once a quote is out.

### Should the renewal risk score be automated?

Partly. Automate usage and support signal. Keep champion and commercial factors as CSM judgment, because no system sees a new director arriving.

### What is the difference between a renewal risk score and a health score?

A health score tracks the whole base, mostly from usage. A renewal risk score covers accounts near a contract date and adds buyer and budget factors.

### How many factors should the model have?

Five is enough to start. More factors make scoring slower without making it more accurate, until you have outcome data to test them.

## Sources

- [Customer success](https://en.wikipedia.org/wiki/Customer_success), Wikipedia
- [The Value of Keeping the Right Customers](https://hbr.org/2014/10/the-value-of-keeping-the-right-customers), Amy Gallo, Harvard Business Review (2014)