Getting a new customer to walk through your door is expensive. Getting an existing customer to come back is dramatically cheaper — and more profitable. This is the fundamental math behind every loyalty program, and it's why Square built one directly into their POS.
But tracking loyalty points and tracking customer behavior are two very different things. Square Loyalty handles the first part well. The second part — understanding who your repeat buyers actually are, what they're worth over time, and how to keep them — requires analysis that goes beyond what the loyalty program shows you.
What Square Loyalty does
Square Loyalty is a built-in rewards program available on paid Square plans. It works like this:
- Customers earn points for each purchase (you define the earning rate)
- Points accumulate toward rewards you configure (a free item, a discount, etc.)
- Customers enroll via their phone number at checkout
- The program is tied to Square's Customer Directory, which tracks purchase history
It's simple, which is its strength. There's no separate app for customers to download, no physical card to carry. The phone number is the identifier. When a customer reaches enough points, the reward shows up automatically at checkout.
For basic loyalty mechanics — incentivizing repeat visits with a tangible reward — Square Loyalty works. Where it falls short is in the analytical layer. It tells you who earned points and who redeemed rewards. It doesn't tell you much about the underlying customer behavior that actually drives retention.
Measuring repeat purchase rate
Repeat purchase rate is the percentage of your customers who have bought from you more than once within a given period. It's the most fundamental customer retention metric, and Square doesn't calculate it for you.
Here's how to think about it: pull your customer list from Square's Customer Directory for the last 12 months. Count the total number of unique customers. Count how many of those made two or more purchases. Divide.
If you had 2,000 unique customers and 600 of them purchased at least twice, your repeat purchase rate is 30%.
That number alone is informative. For most brick-and-mortar retail, a healthy repeat purchase rate is somewhere between 20% and 40%, depending heavily on your category. A coffee shop might see 60%+. A furniture store might see 10%. Context matters.
But the trend matters more than the absolute number. If your repeat purchase rate was 32% six months ago and it's 26% now, something changed. Maybe a competitor opened nearby. Maybe your product mix shifted. Maybe your staff changed. The data doesn't tell you the cause, but it tells you to look.
Calculating customer lifetime value for in-store retail
Customer lifetime value (LTV) is how much revenue (or profit) a customer generates over their entire relationship with your business. It's one of the most important metrics in retail, and one of the least tracked by small and mid-size merchants.
The simplest LTV formula for retail:
LTV = Average Order Value x Purchase Frequency x Average Customer Lifespan
Square's Customer Directory gives you the raw data to calculate this, though it won't do the math for you.
- Average Order Value (AOV): Total revenue divided by total transactions for your customer base. Square surfaces this per customer in the directory.
- Purchase Frequency: Average number of purchases per customer per period (typically per month or per quarter). Count total transactions divided by unique customers.
- Average Customer Lifespan: How long a customer remains active. This is the hardest to estimate. One approach: measure the time between a customer's first and most recent purchase, averaged across your customer base.
If your average customer spends $35 per visit, comes in 2.5 times per month, and remains active for 14 months, that customer's LTV is $35 x 2.5 x 14 = $1,225.
The power of LTV isn't the aggregate number — it's what happens when you segment it. Customers who enrolled in your Square Loyalty program might have an LTV of $1,800, while non-loyalty customers might average $400. That gap quantifies the value of your loyalty program in hard dollar terms.
Identifying loyal vs. lapsed customers
Not all customers who stop coming back are "lost." Some are seasonal. Some moved away. Some had a bad experience. But you can't address customer churn if you can't identify it.
A practical framework:
- Active customers — Purchased within the last 60 days (adjust based on your typical purchase cycle)
- At-risk customers — Last purchase was 60-120 days ago. They used to come regularly but have slowed down or stopped.
- Lapsed customers — No purchase in 120+ days. Likely lost without intervention.
Square's Customer Directory shows the last visit date for each customer, but it doesn't segment your base this way or flag at-risk customers proactively.
The at-risk segment is where your effort should focus. These are people who demonstrated interest — they've been in your store, they've bought your products — and they're drifting away. A targeted outreach (email, SMS, a personalized offer) to at-risk customers has a far higher return than any acquisition campaign.
Purchase frequency patterns
Average purchase frequency is useful, but it hides important variation. Some customers come weekly like clockwork. Others come in bursts — three visits in a week, then nothing for two months.
Mapping purchase frequency distribution reveals your customer segments more accurately than averages:
- High-frequency regulars (weekly or more) — Your core revenue base. They probably represent 10-15% of your customers and 40-50% of your revenue. Protect this group at all costs.
- Moderate regulars (bi-weekly to monthly) — Your growth opportunity. Moving these customers from monthly to bi-weekly visits doubles their value.
- Occasional visitors (quarterly or less) — Low individual value but potentially a large group. Don't over-invest here, but don't ignore them either.
Square Loyalty is designed to move customers up this frequency ladder. Points create an incentive to return. But without tracking the actual frequency distribution, you can't tell if it's working. Are your monthly visitors becoming bi-weekly visitors? Or are your weekly visitors just accumulating points faster while everyone else stays the same?
Using loyalty data to personalize service
The customer data sitting in your Square system isn't just for spreadsheets. It has practical, in-store applications.
When a customer checks in at your POS (or you look them up by phone number), Square shows their purchase history. Your staff can use this information in real time:
- A customer who always buys the same coffee order? Acknowledge it. "The usual?" goes a long way.
- A customer who previously bought a specific product? Ask how it worked out and suggest the complementary item.
- A loyalty member who's close to a reward? Mention it at checkout. "You're just two visits away from a free..."
These aren't grand gestures. They're small, data-informed interactions that make customers feel recognized. And recognized customers come back more often.
The limitation is that this depends on staff training and initiative. The data is there in Square. Whether it gets used at the point of interaction is a human problem, not a technology problem.
What Square Customer Directory tells you (and what it misses)
Square's Customer Directory is automatically built from card transactions. Every time someone pays with a card, Square creates or updates their customer profile. For loyalty members, you also get phone number and enrollment date.
What it shows:
- Total spend per customer
- Number of visits
- Last visit date
- Items purchased (transaction history)
- Loyalty points balance and reward history
What it doesn't show:
- Customer lifetime value calculated and segmented
- Repeat purchase rate trends over time
- Churn risk scoring or at-risk customer identification
- Customer cohort analysis (how do customers acquired this quarter compare to last quarter?)
- Purchase frequency distribution across your customer base
- The impact of your loyalty program on retention metrics versus non-enrolled customers
The Customer Directory is a CRM tool — it helps you look up individual customers. It's not an analytics tool that helps you understand your customer base as a whole.
How Spark provides deeper customer analytics
Spark by MishiPay connects to your Square account and analyzes your customer and transaction data together, providing the analytics layer that Square's built-in tools don't offer.
With your Square data connected, you can ask:
"What's my repeat purchase rate, and how has it trended over the last six months?"
"Which customer segment has the highest LTV, and what do they buy?"
"How many of my customers are at risk of churning based on their purchase patterns?"
"Do loyalty members actually spend more over time compared to non-members?"
Spark calculates LTV by segment, identifies at-risk customers before they lapse, and measures whether your loyalty program is genuinely driving retention or just rewarding people who would have come back anyway. These are the questions that inform strategy — not just who earned a free coffee, but whether your loyalty program is actually building the customer base you need.
For merchants who also sell online, Spark connects to Shopify, WooCommerce, and other platforms alongside Square. This means you can see the full picture: a customer who buys in-store and online isn't two separate profiles — they're one customer with a combined LTV.
Building retention, not just rewards
A loyalty program is a mechanism. Retention is the outcome. The two are related but not the same. You can have a loyalty program with high enrollment and still have poor retention if the underlying customer experience isn't working.
The merchants who retain customers best aren't necessarily the ones with the most generous rewards. They're the ones who understand their customers' behavior — who comes back, how often, what they buy, and when they stop. That understanding comes from analysis, not from a points ledger.
Start by knowing your repeat purchase rate. Then calculate LTV for your top customer segment. Then identify who's at risk. Each of these steps gives you a clearer picture of the customers who are already keeping your business alive — and what it takes to keep them.
Understand your repeat buyers, not just their points
Spark analyzes your Square customer data to calculate LTV, identify at-risk customers, and measure real retention — beyond what the loyalty dashboard shows.