Top 5 Metrics to Predict Customer Churn. Best Practices
In our article, we'll go deeper into five specific metrics to Predict Customer Churn. We will highlight the changes in the traditional approach of customer health scoring and how modern companies are actually solving these changes in innovative ways today. Also, you will see the Pros and Contras of each metric.
Why Customer Health Scoring?
Customer health scoring is a very foundation of customer success management because it actually gives a customer success manager and sales a basis for proactive actions to prevent or increase enhancement expansion rather than with accounts or customers who just screaming the loudest. In simple words, it’s state-of-the-art customer success to make sure that we know where to go.
The main advantages to measure health scoring in your company:
A Customer Health Score is actually an aggregation of multiple scores or aggregation of multiple parameters from a broader set of systems or areas of the customer journey. Therefore it's usually calculated based on multiple KPIs on multiple metrics and these metrics are aligned under one customer healthcare formula based on the weighted average for example or some more complicated formulas that gives the value to make it easy to grasp and understand for future actions.
For helping you with metrics we built a Customer Health Score template that you can download from our website. The template includes over 27 different KPIs which could be relevant to understand customer health and predict churn and expansion. But we just picked 5 top metrics which we've seen frequently in many different discussions and engagements and also based on the feedback, what people find useful and what's actually practically used in a bigger part of the health scoring formula.
1. MAU, DAU, WAU
It is one of the most basic user engagement metrics for SaaS / Online tools and is frequently used as a ratio such as DAU/MAU Ratio or WAU/MAU Ratio. Also frequently used as a ratio to the number of licenses/seats.
Pro: Simple, easy to understand; Great tooling support, e.g. in Mixpanel, Pendo, Amplitude etc.
Contra: Not universal (weekly, monthly, quarterly usage; Different roles and scenarios; W1/M1 retention may be treated differently
2. Net Promoter Score®
NPS is a metric used in customer experience programs. It is a survey question asking respondents to rate the likelihood that they would recommend a company, product, or service to a friend or colleague. Originally developed by Bain & Company, a super simple and transparent methodology.
Pro: Simple to understand and easy to do; Build-in in many SaaS tools and universally applicable without any customizations
Contra: Like any survey statistical relevance only with a high number of participants; Not always easy to apply to the 'account' level (e.g. 30% participation rate; High variance in results on non-cohesive groups
3. Activation trigger(s) / TTV
Activation triggers follow the presence and/or frequency of specific events in the customer lifecycle that indicates a generated value of the product. For example, it can be 'aha moments' in your products and services, like for us is preparing a presentation and a video about 5 Top Metrics. This metric is closely related to 'Time to Value', basic & exceeded.
Pro: One of the best predictors of churn and/or expansion; Can be collected automatically (for SaaS) or manually
Contra: Not universal - specific for each application and/or service offering; Tracking can be tricky on the account level where multiple personas are involved
4. Support KPIs
Metrics for support are present by various KPIs and can be used like the Number of Open Tickets, a number of SLA violations, Time to First Reaction, Time to Full Resolution. These indicators are used as absolute values of trends on median values over time.
Pro: Simple to understand and track values; Basic KPIs are supported by major tooling providers like out of the box
Contra: Aggregating on account may lead to lower statistical relevance hence high variance; Health scoring of absolute values might be misleading
Manual assessment of individual Customer Success Managers based on a simple sentiment scale from Very Negative to Very Positive. The de-facto basis for assessment of the quality of any customer health score model.
Pro: Simple, easy to understand; Easy to collect with the required consistency
Contra: Subjective and can be biased; Consistency can be affected by fluctuation in CSM team structure
Challenges with a traditional approach
Modern trends and approaches: Health Scoring with ML Models
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