VIPs Are 8% of Members but Drive 42% of Revenue — Analyzing Data by Membership Tier

"I know our VIP customers matter, but I couldn't tell you exactly what percentage of revenue they account for."

We hear this a lot when talking to CRM managers. The brand has a membership tier system and has designed VIP perks, yet it rarely checks how those tiers actually perform.

How much revenue contribution differs by tier, whether there's a real gap in AOV (average order value), when VIP customers start showing signs of churn — surprisingly few brands verify any of this with data.

How to view data by tier in Laplace

Let's start with the setup. On Laplace's Payment Statistics screen, you can add dimensions. Select member_level_name here, and every revenue and purchase metric you already track is broken down by tier.

What happens when you open up data by tier

Brand managers looking at a membership tier analysis for the first time tend to react the same way: "VIPs matter a lot more than we thought."

For example, suppose the breakdown looks like this.

By headcount, VIPs make up just 5% of customers, yet 41% of revenue comes from that 5%. In other words, if a single VIP churns, you'd need to acquire 5–6 new regular customers to fill the gap.

Without knowing this, you end up pouring your CRM budget into new customer acquisition. Once you see the data, your priorities shift — because the numbers make it clear that "preventing VIP churn" is the CRM action with the highest ROI.

There's a concept called the upgrade threshold: looking at the pattern of when customers move up to the next tier.

Regular → Silver: upgraded after 2.3 purchases on average
Silver → Gold: upgraded after 5.1 purchases on average
Gold → VIP:    upgraded after 9.0 purchases on average

Knowing these numbers lets you time CRM messages precisely. For instance, you can send regular customers who have made 1.5–2.0 purchases a message like: "Just one more purchase to reach Silver — and unlock free shipping." That's bound to convert better than blasting the same promotion to every customer.

Conversely, if an unusually small share of customers move from Silver to Gold, that's a sign the hurdle for that step is too high or the difference in perks doesn't feel meaningful. Which means you need to revisit the tier design itself.

VIP customers rarely vanish overnight. In most cases, the gap between repeat purchases gradually stretches until, at some point, they stop buying.

For example, if the average repurchase cycle for VIP customers is 28 days:

Customer A (VIP): last purchase 47 days ago → 1.7x the repurchase cycle. Churn risk signal.
Customer B (VIP): last purchase 19 days ago → within the normal range.
Customer C (VIP): last purchase 61 days ago → likely already churned.

Sending a reminder at the Customer A stage converts very differently from sending it at the Customer C stage. If you flag at-risk customers in advance based on each tier's repurchase cycle, you can run proactive CRM instead of reactive CRM.

Sometimes the tier design itself is the problem

When brands run the analysis, it's not uncommon to find that the real issue is the tier design, not revenue contribution.

Here are three typical patterns.

The VIP threshold is so low that 30% of customers are VIPs. If VIPs make up 30% of all customers, the exclusivity and sense of belonging the VIP tier should offer gets diluted. VIP status stops meaning anything — for customers and for the brand alike.

There's barely any AOV difference between Gold and VIP. If two tiers have similar purchasing patterns, there's no real reason to separate them. It's hard to differentiate perks, and customers have no motivation to move up.

Fewer than 5% of Silver members move up to Gold. If most customers stall at this step, the hurdle is too high or the perks aren't appealing enough. No matter how well you craft upgrade messages, they won't work if the tier criteria are poorly designed.

Tier-level analysis is how you check all of this.

Five things you can check right now

1. Go to Payment Statistics > Add dimension and select member_level_name.

2. Check revenue share by tier — what % of revenue do VIPs account for?

3. Check AOV differences by tier — do purchasing patterns actually differ between tiers?

4. Check the distribution of purchase counts by tier — where are the upgrade thresholds?

5. Check the average VIP repurchase cycle — set your churn-risk baseline

If you've set up membership tiers, taking one look at how they actually perform in your data can change more than you might expect.

Check your revenue by membership tier in Laplace Payment Statistics today.