Cast Analytics: Repeat Nomination Rate × Churn Rate — Service Quality You Can See in Numbers

Many owners want to "make cast evaluation objective" and "decide training and assignments with numbers." But in the nightlife industry — hostess clubs, host clubs, and men's wellness spas — the only widely used metric is the repeat nomination rate, and on its own it cannot correctly evaluate service quality.

As an industry-focused SaaS, tasteck ships with an analytics feature that evaluates cast on 2 axes: repeat nomination rate and churn rate. This article explains how to turn "service quality" — invisible with the repeat nomination rate alone — into a number.

Why You Should Not Evaluate on Repeat Nomination Rate Alone

What the repeat nomination rate means

Repeat nomination rate = customers who chose that cast member by repeat nomination ÷ total customers that cast member served

A high repeat nomination rate means the cast member is building repeat customers. That is an important indicator.

The pitfall of the repeat nomination rate

However, looking only at the repeat nomination rate misses something.

Example: Cast member A has a 50% repeat nomination rate (half of their customers nominate them again). At first glance, excellent. But what if the other 50% of customers left the venue entirely?

From the venue's point of view:

• Repeat nomination customers: became loyal fans of the venue ◯
• Churned customers: acquired with paid advertising, then left after one visit ×

Cast member A shows a pattern: good at building loyal fans, but weak at handling customers who are not a natural fit. From an operations perspective, this tells you what kind of customers you should assign to this cast member.

tasteck's Unique Metric: Churn Rate

Definition

Churn rate = the probability that, after being served by that cast member, the customer stopped visiting for a set period

tasteck uses its own algorithm to estimate "the probability that a customer left the venue because of that cast member's service."

How it is calculated

• Trace each customer's visit history by cast member
• Calculate how often visits stopped after a specific cast member's service
• Extract "that cast member's contribution" as the difference from the all-cast average

It is a metric only an industry-focused SaaS can provide, derived from the combination of customer × cast × time-series data.

The 2-Axis Cast Evaluation Matrix

Looking at repeat nomination rate × churn rate, cast can be classified into 4 types.

Type	Repeat nomination rate	Churn rate	Evaluation	Action
Ace	High	Low	Core of the venue	Improve treatment, prioritize retention
Repeat-focused	High	High	Builds fans but loses mismatched customers	Improve customer-cast matching when assigning
All-rounder	Medium	Low	Handles any customer steadily	Assign to first-time customers
Needs improvement	Low	High	Service quality issue	Strengthen training and feedback

Once you have this classification, decisions like "who to reward," "who to train on what," and "which cast member to suggest to which customer" can be made with numbers instead of gut feeling.

How It Looks on the Actual Screen

On tasteck's cast analytics screen, you can see:

• Cast rankings (by repeat nomination rate / by churn rate)
• A 2-axis scatter plot showing the distribution of all cast
• Monthly trends (growing or declining)
• Breakdowns by course and time slot
• The list of customers each cast member served (with individual review of churned customers)

The ability to review churned customers individually is especially useful in one-on-one feedback meetings — you can have concrete conversations like "This customer has not visited since your last session. Do you remember what happened?"

The Benefits of Running Operations on Numbers

When cast evaluation runs on gut feeling, problems like these tend to appear:

• Cast assignments are decided by the manager's personal preferences
• Meetings become discussions of impressions, not facts
• Training direction stays vague
• Cast members feel "why me?" about decisions

With the 2 axes of repeat nomination rate × churn rate, you can reduce this interpersonal friction with objective data. You can also show cast members clearly that "if this number improves, your treatment improves," which makes motivation design easier.

Summary

• The repeat nomination rate alone cannot correctly show service quality
• Pair it with the churn rate = the probability a customer left because of that cast member
• The 2-axis matrix classifies cast into 4 types, each with a suitable action
• An evaluation culture based on numbers moves operations forward without damaging trust with cast

Moving from gut-feeling operations to numbers-based operations is a small step with a large payoff.

→ Try tasteck's cast analytics free for 30 days ｜ Feature list ｜ For men's wellness spas