> For the complete documentation index, see [llms.txt](https://docs.new-vue.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.new-vue.com/visual-guides/overvue/customer-retention-analysis.md).

# Customer Retention Analysis

### Overview

This page focuses on customer loyalty and repeat visits. It tags each customer's bookings as either "New" or "Repeat" and shows ticket-volume trends. This setup is designed for anyone tracking customer loyalty or repeat business, like marketing or membership teams. It's important to know that the New/Repeat tags only apply to bookings linked to a customer record. Walk-up or anonymous bookings without a customer attached are not included. Since these make up most bookings, the breakdowns here represent about one in ten bookings, so they do not show the full picture.

{% hint style="info" icon="alt" %}
Calculations Used In This Report

* Average # of bookings per customer visits
* Avg Days Between Visits
* Avg Days to Second Visit
* Bookings - Value
* Items sold - count
* Items Sold - whole of previous year
* Items Sold - year to date
* Items Sold - year to date (Cards)
* Items Sold - year to date previous year (Cards)
* Items Sold - YTD YoY %
  {% endhint %}

### Filters

Site, Season, and Product Category (with product name available underneath it).

<table data-view="cards"><thead><tr><th></th></tr></thead><tbody><tr><td>Site</td></tr><tr><td>Season</td></tr><tr><td>Product Category</td></tr><tr><td>Product Name</td></tr></tbody></table>

### Tickets sold this season

At the top of the page, four cards display key numbers: tickets sold so far this season, tickets sold at the same point last season, total tickets sold last season, and year-over-year growth in ticket sales. These headline numbers help the page owner quickly see if sales are ahead of or behind last year, and how this year's pace compares to last year's final results, before looking at the trend charts below.

### Tickets by month and year

Each season has its own bar for every month, making it easy to compare the same month across different years. This is the main tool on the page for spotting seasonality in ticket volume. You can see which months are usually busy or slow, and whether this season's monthly pattern matches or differs from previous years.

### Tickets sold to date, by season

Rather than showing a single number, this chart shows how ticket sales have built up over the year for each season. It helps you see if a strong year-to-date total came from steady sales or a late surge, and lets you compare this pattern across seasons at a glance.

### Tickets by month and season (table)

This table is the numbers-grid version of the chart above. Use it when you need the exact ticket count for a specific season and month, especially for planning inventory or staffing at certain times of the year, and when the chart's visual comparison is not precise enough.

### Revenue by customer type

A column chart and a matching pie chart both show revenue by customer type, comparing revenue from new and repeat customers. The column chart tracks this split over time, while the pie chart shows the overall proportion. Keep in mind, this split only includes bookings linked to a customer record, so it reflects a smaller group, not the whole business. Still, retention teams can use it to see whether new-customer acquisition or repeat-visit spending is driving revenue growth.

### Bookings by customer type

While the revenue charts above show dollars by customer type, this chart shows booking counts instead. Comparing the two helps you see if repeat customers, who might be fewer, are especially valuable, or if the revenue and booking splits are about the same.

### Loyalty and visit cadence

Three cards complete the page's retention overview: average bookings per customer (for those with a record), average days to a customer's second visit, and average days between visits for anyone with more than one booking. These cards help answer questions about timing and frequency. If the average bookings per customer is rising, customers are coming back more often. If it is flat or falling, you may have a retention issue to investigate. The days-to-second-visit number helps plan follow-up marketing, since knowing when customers usually return can guide when to send a re-engagement email. The average days between visits shows the general visit rhythm for repeat customers, which helps set the timing of loyalty or re-engagement campaigns.