Reading Your Retention Data: Cohorts, Frequency, and Churn Signals

Why Retention Data Is Your Most Valuable Signal

Most venues focus on finding new guests. But a guest who comes back four times a year is worth roughly four one-off visits, and keeping a guest usually takes less than finding a new one. WiFi data shows who comes back, how often, and who seems to be drifting away.

Two limits to keep in mind: only guests who log in are counted, and a device stays authorised for 8 hours on a published VoqadoWiFi portal, so two visits on the same afternoon count as one.

Step 1: Understand the 7-Day, 30-Day, 90-Day Cohort View

A cohort is a group of guests who first visited in the same week or month. Tracking what share of each group comes back after 7, 30 and 90 days tells you whether first visits are turning into habits.

VoqadoWiFi does not draw a cohort table for you yet, but you can build a simple one. In CRM, click Export CSV. The file includes First visit, Last visit and Visits for every guest. In a spreadsheet, group guests by the month of their first visit, then count how many have a last visit more than 7, 30 and 90 days after the first. A healthy pattern drops after the first visit and then levels off, showing a core of regulars. A curve that keeps falling is worth investigating.

Step 2: Benchmark Your Return Rate by Venue Type

Return rates vary a lot by venue type. A neighbourhood café can expect a far higher share of returning guests than a hotel, where repeat stays are naturally rare. Published "benchmarks" rarely say how they were measured, so the useful comparison is with yourself.

Spend your first two months building a baseline: the share of each month's guests who come back within 30 days (90 days for hotels). Then compare each month with that baseline and with the same month last year. A drop tells you to look at the in-venue experience and your follow-up emails. A rise tells you something is working; find out what and keep doing it.

Step 3: Read the Peak Hours Heatmap

On the Analytics page, the peak hours heatmap shows logins by hour of day and day of week; darker cells mean more logins. Use it to check what staff believe about your busiest times, to find quiet slots worth a targeted offer ("quiet Tuesday lunches"), and to plan rotas. After a campaign, look at whether the days that followed were busier than the same days the week before.

Step 4: Identify At-Risk Guests

In Top Guest Segments on the Analytics page, At Risk means guests with two or more visits and no visit in the last 30 days, the same rule as the Guests screen and the campaign audience. That fixed window suits a venue with weekly regulars. If your guests normally come once a month, many of them will appear here without having lapsed at all, so read it alongside their usual pattern: someone with 12 visits and no login for 30 days is a very different case from a one-time visitor. Campaigns has an At-Risk audience with the same rule, ready for a win-back email once campaigns are live.

Step 5: Export Data for Mailchimp Segmentation

On the Growth plan, VoqadoWiFi adds guests recorded as opted in to marketing to your Mailchimp audience, tagged by venue; connect it under Settings, Integrations, Mailchimp. Only guests who ticked the marketing box are synced, and Auto Marketing Opt-In is off for every venue, so the audience holds only people who chose to hear from you. You can also export manually from CRM: the export gives consented guests by default, with columns including email, first name, birthday, consent details, visits, first visit, last visit and favourite venue. Filter it in a spreadsheet (for example, three or more visits in 90 days for a VIP list) and import it as a Mailchimp segment.

Step 6: Using the Retention Insight Card

The Insights page under Analytics reads your logins, returning guests and opt ins and tells you in plain sentences what changed. Make it your Monday check: five minutes reading it, and one decision about what to do this week.

Step 7: What "Good" Looks Like Over Time

In the first month your list is small and any pattern is noise. By month three you can see which days and hours are reliably busy and roughly who your regulars are. By month six you have enough history to compare cohorts, spot seasonal swings, and judge whether your follow-up emails bring people back. The aim is to know your regulars by name, notice when they stop coming, and do something about it.