Customer Retention and Cohort Analysis: Repeat Rate and Churn by Month

"Retention rate is one of the fundamental metrics in product management. We all use it regularly, yet few of us know that there are many different ways to calculate retention rate. And it is very important to know which one to use when you're making decisions based on retention data."

— Source: r/Entrepreneur (3 upvotes)

CAC (customer acquisition cost) is the metric everyone quotes. But on its own it hides the hard question: do those customers come back?

Illustrative example: say each $50 order leaves $20 of contribution after product, fulfillment, and fees. A customer acquired for $8 who buys once and never returns leaves $20 − $8 = $12. A customer acquired for $15 who buys 5 times leaves 5 × $20 − $15 = $85. The second costs nearly twice as much to acquire but is worth about 7x as much.

Cohort analysis is how you see this. It segments customers by the month they joined and tracks how many of them come back, month after month, to make repeat purchases.

The 50-word definition

Cohort analysis is the antidote to vanity metrics. Instead of looking at aggregate "repeat rate" (e.g., 22% of all customers repeat), you see the pattern: do June customers repeat at 22%? Do July customers repeat at 24%? Are you getting better at retaining customers, or worse?

Example cohort table (simplified):

Reading this table: The September cohort is retaining customers better (28% M1, 18% M2) than July (24% M1, 15% M2). This suggests improving product-market fit or better customer segmentation.

Repeat Rate (Month N)

Formula: (Customers who purchased in Month N) / (Total customers in cohort)

Example: June cohort had 1,000 customers. In July (Month 1), 240 of them purchased again. M1 repeat rate = 240 / 1,000 = 24%.

Repeat rate is month-specific. It captures the "reactivation" in that month. High M1 (short-term repeat) suggests strong product experience. High M6, M12 (long-term repeat) suggests habit-forming product or subscription-level engagement.

Cumulative Retention Rate

Formula: (Customers who purchased at least once after initial purchase) / (Total customers in cohort)

Example: June cohort, 1,000 customers. 420 have made at least one repeat purchase within the first year. Cumulative retention = 420 / 1,000 = 42%.

This answers: what percentage of customers ever come back? For DTC brands it is the "second purchase rate", one of the most useful retention numbers. Pair it with the monthly rates, which show when customers come back.

Churn Rate

For subscription products, churn is simple: cancellations in the month ÷ active subscribers at the start of the month. For non-subscription products, a customer who skips a month has not necessarily churned. Define churn as no purchase within a set window, longer than your typical repurchase cycle.

Example: if most repeat buyers reorder within 90 days, count a customer as churned after 120 days with no order. If 600 of a 1,000-customer June cohort have not ordered again within 120 days of their first order, 120-day churn is 600 / 1,000 = 60%.

Compare that same window across cohorts. If June's 120-day churn is 60% and September's is 55%, churn is improving.

Average Repeat Purchase Value (RPV)

Formula: (Total revenue from repeat purchases in Month N) / (Customers who purchased in Month N)

Example: June cohort, M1 repeat purchases = 240 customers, total revenue = $8,400. RPV = $8,400 / 240 = $35/customer.

Tracks not just who repeats, but how much they spend. A cohort with 30% M1 repeat rate but $50 RPV is better than 40% M1 repeat at $20 RPV.

Repeat Purchase Frequency

Formula: (Total purchases from cohort in Month N) / (Total customers in cohort)

Example: June cohort, M1 repeat purchases = 270 transactions (some customers purchased more than once). Frequency = 270 / 1,000 = 0.27 purchases per customer.

Compare the same month across cohorts. If the September cohort makes 0.30 purchases per customer in M1 against 0.27 for June, newer customers are buying more often.

Structure

A standard monthly cohort table:

• Rows: Customer cohorts, one per acquisition month (e.g., July 2024, August 2024). Show 12-24 months.
• Columns: Month 0 (acquisition), Month 1, Month 2, ..., Month 12 (or beyond). Represents months post-acquisition.
• Cell values: Repeat purchase rate (or count, or revenue) for that cohort in that month.

Example: DTC beauty brand cohort table (repeat rate %)

Interpreting the table

• M1 repeat rates (24-28%): About 1 in 4 customers buy again in the month after their first order.
• Trend down M1-M6: Steep drop from 28% (M1) to 10% (M6) for the September cohort. Most of the repeat activity happens early.
• Stabilization M6+: After 6 months, repeat rate stabilizes around 5-9%. These are loyal, repeat customers.
• September cohort (best M1): 28% M1 repeat rate is higher than July (24%) and August (26%). Product or marketing may have improved.

Building the table in Excel/Sheets

• Column A: Cohort names (July, August, September, etc.)
• Columns B-M: Month 0 through Month 12
• Prepare the order data first: one row per order with customer ID (column A) and order date (column B). In column C, find each customer's first order date: =MINIFS(B:B, A:A, A2). In column D, months since first order: =(YEAR(B2)-YEAR(C2))*12+MONTH(B2)-MONTH(C2).
• Build a pivot table with cohort month (from column C) as rows, months since first order (column D) as columns, and a distinct count of customer IDs as values. Divide each cell by the cohort's Month 0 count to get the repeat rate.

Consumer brands should build separate cohort tables by channel and customer segment. Repeat rates often differ dramatically:

Scenario: Apparel brand, DTC + Amazon, Jan-June 2024

• Product: Women's athleisure (leggings, jackets)
• Channels: DTC (Shopify) and Amazon
• Customer acquisition: 500-800 new customers per month
• Data: Transaction history, customer acquisition date, repeat purchases

Step 1: Identify January cohort (500 customers)

• Total customers acquired in January: 500
• Customers who purchased in February (M1): 95 = 95 / 500 = 19% repeat rate
• Customers who purchased in March (M2): 65 = 65 / 500 = 13% repeat rate
• Customers who purchased in April (M3): 48 = 48 / 500 = 9.6% repeat rate
• Customers who purchased in May (M4): 32 = 32 / 500 = 6.4% repeat rate
• Customers who purchased in June (M5): 22 = 22 / 500 = 4.4% repeat rate

Step 2: Repeat for each cohort (Feb-June)

• February cohort (600 customers): M1=22%, M2=15%, M3=11%, M4=7%, M5=5%
• March cohort (550 customers): M1=20%, M2=14%, M3=10%, M4=6%, M5=N/A (future)
• April cohort (700 customers): M1=24%, M2=16%, M3=11%, M4=N/A, M5=N/A
• May cohort (650 customers): M1=26%, M2=17%, M3=N/A, M4=N/A, M5=N/A
• June cohort (800 customers): M1=28%, M2=N/A (future data not yet available)

Step 3: Build the cohort table

Step 4: Analyze the patterns

• M1 repeat (19-28%): Improving trend. January cohort was lowest at 19%; June cohort is highest at 28%. Marketing or product quality may be improving.
• Drop-off: Steep decline M1-M3, for example 24% to 11% for the April cohort. By M3, only about 1 in 9 April customers bought that month.
• Stabilization: After month 4-5, repeat rate stabilizes around 4-7%. These are the loyal repeat customers.
• Cumulative repeat (Jan cohort): Adding the monthly rates (19% + 13% + 9.6% + 6.4% + 4.4% = 52.4%) overstates it, because the same customer can buy in several months. Count unique customers instead: if 180 of the 500 bought again at least once in M1-M5, cumulative repeat is 180 / 500 = 36%.

Step 5: Calculate repeat revenue

Add a second cohort table for repeat revenue:

Reading: repeat customer counts match the cohort table (for example, 22% of February's 600 = 132 in M1 and 15% = 90 in M2). M1 spend per repeat customer ranges from $25.00 to $30.00 with no upward trend: January was highest at $30.00 and June is $25.00. Repeat rates are improving, but spend per repeat customer is not, so there is no sign of upsell improvement.

For DTC, Amazon, and wholesale brands, cohort analysis answers critical questions:

• Are we improving retention? Track M1 repeat rate trend. Is June cohort better than May? If retention is declining, it signals product or marketing quality issues.
• Which channels retain best? Build separate cohort tables by channel, and compare repeat rates alongside each channel's contribution margin. Put retention marketing behind the channels where repeat customers are most profitable.
• Do promotions hurt long-term retention? Compare cohorts acquired during promotions vs regular pricing. Promo cohorts can repeat less (discount-seekers). Compare profitability, not just repeat rate.
• How does repeat rate vary by product? Build cohorts by product category. Replenishment products (supplements, beauty consumables) tend to repeat more than one-time purchases (apparel, home goods).
• Are new customer acquisition costs sustainable? If CAC is $20, each order leaves $20 of contribution, and customers average 1.15 orders over your planning horizon, LTV = $20 × 1.15 = $23, just $3 more than CAC. At 1.40 orders, LTV = $28 and $8 is left after CAC.

The goal: build repeatable, predictable retention. Once you understand your cohort retention by channel and segment, you can forecast LTV, optimize CAC budgets, and model annual unit economics.

Many brands watch "repeat rate" as an aggregate: "22% of all customers repeat." On its own, that hides the trend. What matters is whether retention is improving or deteriorating, and whether it varies by channel and customer segment.

Cohort analysis shows the pattern. In the worked example, January customers repeat at 19% in M1 and June customers at 28%. In the channel example, DTC customers repeat at 32% and Amazon at 18%. Those differences change what each acquisition dollar is worth.

The operational challenge: building and maintaining cohort tables for 50+ SKUs, 3 channels, and monthly cohorts is complex, and a table built once goes stale. The brands that succeed refresh it monthly, track it by channel and product, and feed it into the budget.