DTC Retention Curve Benchmarks: 78K First-Time Buyers

8.8% of first-time buyers repurchase within 30 days. 13.1% within 90 days. Then the curve flattens.

Those are the DTC retention benchmarks across 78,714 first-time buyers, 12 monthly cohorts, and multiple brands over a full year. And the shape of that retention curve tells you something most retention strategies get backwards: the window isn't 90 days. It's 30. Everything after that is diminishing returns.

67% of your 90-day retention has already happened by day 30. From 90 days to 180 days, you pick up roughly 3 more percentage points. The first month does the heavy lifting. The next five months split the scraps.

What Is a Retention Survival Curve?

A retention survival curve tracks the cumulative percentage of first-time buyers who make a second purchase over time — measured at fixed intervals (7 days, 14 days, 30 days, etc.) from their first order. Unlike a static repeat purchase rate, survival curves show when retention happens, not just whether it happens. The shape of the curve tells you where your post-purchase experience is working and where it's failing.

Our repeat purchase benchmarks showed that 50.3% of repeat buyers come back within 30 days. This retention curve data adds the monthly cohort view — how that pattern holds (or breaks) across different acquisition months, seasonal shifts, and cohort quality. We also published BFCM-specific cohort retention. October's weak retention in this data corroborates that finding.

First 30 Days Capture 67% of All Retention: Two Stats That Compound

This isn't one finding. It's two — and they make each other worse.

Stat 1: 67% of 90-day retention happens in the first 30 days.

Across all cohorts, the average 30-day retention rate is 8.8%. The average 90-day retention rate is 13.1%. That means two-thirds of all the retention you'll capture in the first three months has already happened by the end of month one. The remaining third trickles in over the next 60 days.

The curve is steep early, then it bends. Hard.

Stat 2: From 90 days to 180 days, you gain roughly 3 percentage points.

The cohorts with full 180-day data average about 16% cumulative retention at the six-month mark. That's 13.1% at 90 days plus roughly 3 more points over the next 90 days. The same amount of time. A fraction of the gains.

Now put those together. You lose the majority of your retention opportunity in the first 30 days. And the opportunity that remains after 90 days is barely worth the calendar space. Two leaky periods, compounding.

This is why the framing matters. "You have 90 days to retain a customer" sounds like you have a quarter to work with. The truth is closer to: you have 30 days to capture two-thirds of your retention outcome, and the clock started the moment the order confirmed.

Why Most Post-Purchase Email Flows Miss the Retention Window

Here's what most brands' post-purchase sequences look like:

Day 0: Order confirmation. Day 2: Shipping notification. Day 7: Review request. Maybe a second review nudge at day 14. Then silence. The flow ends. The customer doesn't hear from you again until they hit a campaign segment or a winback flow triggers at day 90 or 180.

Three to five emails over two weeks. Then nothing.

Now look at the curve again. 3.6% of first-time buyers repurchase within 7 days. 5.8% within 14 days. 8.8% within 30 days. 13.1% within 90 days. The buying is still happening — at a declining rate, but happening — for months after that initial purchase. And the post-purchase flow covers only the first week or two.

Most brands invest their flow-building energy in the wrong place. They build elaborate winback sequences targeting 90-180 day lapsed customers. They A/B test subject lines on re-engagement campaigns for people who haven't opened in six months. Meanwhile, the post-purchase flow — the sequence that covers the highest-probability retention window — is three emails and a review request.

That's backwards.

The data says invest in day 1-30. That's where 67% of 90-day retention lives. The post-purchase flow should be the longest, most carefully constructed sequence in your email program. Not the shortest.

Most brands build for the winback. The curve says build for the window.

Monthly First-Time Buyer Retention Benchmarks

Here's the full retention curve, month by month — the core DTC retention benchmarks from this study. Every row is a cohort of first-time buyers acquired in that month, tracked forward at fixed intervals.

Table: Monthly First-Time Buyer Retention Survival Curve (12 Cohorts, Feb 2025 — Jan 2026)

A few things jump out.

The curve shape is remarkably consistent. Month after month, the pattern holds: steep gains from 7 to 30 days, a slower climb from 30 to 90, and a near-plateau from 90 to 180. The level shifts — some months are higher, some lower — but the shape doesn't change. This isn't one cohort's behavior. It's structural.

July was the strongest cohort for early retention. 4.3% at 7 days, 10.0% at 30 days, 18.6% at 180 days. Smaller cohort (3,841), higher AOV ($372). Fewer buyers, better buyers.

September showed the highest 90-day rate: 15.9%. Also a smaller, higher-quality cohort. The 4.8% at 7 days is the strongest early signal in the table — nearly 1 in 20 first-time buyers came back within a week.

October and November are visibly different. Cohort sizes balloon to 12,714 and 11,471 respectively. AOV drops. Retention drops. This is the holiday acquisition effect, and it deserves its own section.

BFCM Cohort Retention Benchmarks: Why Holiday Buyers Retain Worse

October 2025 is the outlier that proves the rule.

The October cohort was 3x the size of a typical month — 12,714 first-time buyers versus an average of roughly 4,500 for non-holiday months. That's the BFCM ramp: promotional spending increases, discounts go live, and new buyer acquisition surges.

But look at what came with that volume.

AOV dropped to $143. The prior six months averaged $319-$474. October's average order was less than half that. Cheaper buyers buying cheaper.

30-day retention: 6.5%. Typical months run 8.0-10.0%. October is 1.5-3.5 percentage points below normal. That's not noise. That's a fundamentally different cohort.

90-day retention: 9.6%. The worst in the table. Typical months run 11.2-15.9%. October is 1.6-6.3 points lower. The gap widens as you move further out — the quality difference compounds over time.

Here's the math that should worry you. A typical month acquires ~4,500 first-time buyers and retains ~13% of them by 90 days. That's ~585 repeat buyers. October acquires 12,714 first-time buyers and retains 9.6%. That's ~1,221 repeat buyers. You tripled the acquisition and got double the retained customers. The volume partially offsets the rate — but you paid 3x the acquisition cost for 2x the retained outcome.

Discount acquisition produces discount retention. We published a full BFCM cohort retention analysis that confirms this finding from a different angle — the BFCM cohort's 90-day repurchase rate was 39% lower than year-round averages.

November shows a similar pattern, though less extreme. Cohort size of 11,471, AOV of $218, and 30-day retention of 9.4% — better than October but still below the spring and summer months. The holiday-acquired customers who do retain tend to show up earlier (November's 14-day rate of 6.5% is solid), likely driven by gift-for-self purchasing and holiday re-engagement.

The takeaway: Promotional acquisition isn't inherently bad. But it changes the math on everything downstream. If you're planning post-purchase flows and retention budgets around a 13% 90-day retention rate, and 40% of your annual acquisition happens during a period where the rate is 9.6%, your annual retention forecast is off before it starts.

DTC Retention Benchmarks by Brand: The 4x Range

The monthly DTC retention benchmarks show variation across time. The per-brand data shows variation across businesses. And the range is enormous.

Table: First-Time Buyer Retention Ranges by Vertical

The spread from best to worst is roughly 4x at both 30-day and 90-day marks. Functional Beverage at 20-22% at 30 days versus Health & Wellness at 3-8%. Non-Alcoholic Spirits hitting 40% at 90 days versus Home Decor at 5-10%.

A caveat on sample sizes: some of these brands have monthly cohorts of 30-50 first-time buyers. At those volumes, a few extra repeat purchases can swing the rate by several percentage points. The directional patterns are consistent — Functional Beverage outperforms every month, not just in aggregate — but the precise percentages should be read as ranges, not fixed benchmarks.

Some of that gap is product category. Consumables create natural repurchase occasions. Durables don't. Someone who buys a functional beverage drinks it, runs out, and needs more. Someone who buys a piece of home decor doesn't need another one for a year. The product itself builds — or doesn't build — the repeat occasion.

But not all of it is category. Look at the consumables. Functional Beverage sits at 20-22% at 30 days. Food & Bev (Scale), also a consumable brand, sits at 9-11%. Same product type. Same consumption cycle logic. But a 2x gap in 30-day retention. That delta is larger than you'd expect from noise alone, even with small samples.

The brands at the top of this table have higher retention for some combination of product, flow design, and customer mix. We can see the outcome. Attributing it to a single cause — especially across different categories — would be overstating what the data shows. The brands at the bottom aren't doing anything wrong — they're in harder categories with longer purchase cycles. And within each tier, there's separation, but the degree to which that separation reflects execution versus other factors is an open question at these sample sizes.

Where does your curve flatten? That's the question this table should prompt. If you're a consumable brand below 15% at 30 days, there's likely room. If you're a durable brand above 10% at 30 days, you're probably outperforming. Know your category reality and then ask whether you're meeting it.

Post-Purchase Email Flow Design: Mapping Emails to the Retention Curve

Here's one way to think about mapping your post-purchase flow to the curve. This is a framework, not a formula. Your product, your customer, and your curve will determine the right touchpoints and timing. But the shape of the curve suggests natural windows where different types of messaging might earn their return.

Days 1-7 (3.6% cumulative retention): The Momentum Window.

Buyers who repurchase almost immediately. Still in buying mode. This window is about reducing friction: easy reorder links, add-on offers, "complete the set" prompts. Don't try to educate yet. They already bought. Make it easy to buy again.

Days 7-14 (5.8% cumulative): The Reinforcement Window.

The product has arrived. The customer is forming their opinion. This is where product education earns its keep — usage tips, how-to content, "getting the most out of your order" guides. A customer who uses the product is more likely to reorder than one who lets it sit in the box.

Days 14-30 (8.8% cumulative): The Decision Window.

The majority of early retention lives here. The customer has used the product and is either thinking about reordering or has moved on. Direct repurchase messaging earns its return — "running low?" for consumables, "ready for another?" for repeat-friendly products. Social proof hits hard here. Reinforce the purchase decision and give them a reason to do it again.

Days 30-60 (11.2% cumulative): The Nudge Window.

The curve has started to bend. This is where incentives start making sense — not as a discount habit, but as a strategic nudge. A small offer, a loyalty reward, early access to a new product. You're no longer riding momentum. You're creating urgency.

Days 60-90 (13.1% cumulative): The Last Call Window.

The final stretch of meaningful retention. After 90 days, gains plateau. "It's been a while," "your favorites are still here," "we saved your picks." This isn't a winback flow — the customer isn't lapsed yet. But they're at the edge.

Days 90-180 (~16% cumulative): The Long Tail.

Roughly 3 more percentage points over 90 additional days. Traditional winback territory. If your entire retention strategy starts here, you've already missed the window where the curve was steepest.

It's worth mapping your existing flows to these windows. If you have five emails that end at day 14, you've covered the first two windows and left the rest to campaigns or silence. Whether that's a problem depends on your brand and your curve — but knowing where your flows end relative to where your retention concentrates is a useful diagnostic.

What This Curve Means for How You Think About Retention

These retention curve benchmarks point to one obvious implication: the first 30 days is the highest-leverage window for retention. That's where 67% of the 90-day outcome concentrates. Most retention strategies spread their investment across the full customer lifecycle — a little post-purchase, a little mid-cycle, a lot of winback. This data suggests concentration matters more than coverage. Whether that means more flow touchpoints, better offers, or just more attention in weeks one through four depends on the brand. But the curve makes the case for front-loading.

The BFCM cohort data raises a harder question: if you acquire 3x the customers at measurably worse retention rates, is that a good trade? October brought in 12,714 first-time buyers at 9.6% 90-day retention. A typical month brings in ~4,500 at ~13%. The volume partially offsets the rate — you end up with more retained customers in absolute terms — but you paid for 3x the acquisition to get 2x the retention output. Whether that math works depends on your CAC, your margins, and your LTV model. This data gives you the inputs to make that calculation for your brand. It doesn't tell you the answer.

The per-brand variation suggests that retention is at least partly controllable — the 4x range from top to bottom isn't all explained by product category. But the sample sizes are small, and we should be honest about that. Some of these brands have monthly cohorts of 30-50 first-time buyers. At those volumes, a few extra repeat purchases can swing the rate by several percentage points. The directional patterns are consistent, but confidently attributing the differences to specific tactics versus product category versus customer mix would require more data than we have.

The aggregate 30-day retention rate of 8.8% hides a range of 6.5% to 10.0% across months, and a much wider range across brands. No single DTC retention benchmark tells the full story. If you only measure the blended number, you can't see where the problem is — or whether there is one. Monthly cohort tracking, like the table above, is the minimum resolution you need to connect retention outcomes to the acquisition and experience decisions that drive them.

Pull your own cohort data. Build the curve for your brand. See where it flattens. That's where your retention opportunity concentrates — and whether it's a 30-day window or a 60-day window will tell you more than any benchmark report can.

FAQ

What is a good 30-day first-time buyer retention rate?

A good 30-day first-time buyer retention rate is 8.8% on average, based on 78,714 first-time buyers across 12 monthly cohorts. The range is 6.5% to 10.0%, driven by cohort quality, seasonal acquisition patterns, and vertical mix. Consumable brands should target 9-12% or higher. Durable brands will naturally sit lower at 5-8%. If you're below 7% in a non-holiday cohort, investigate your post-purchase experience.

How much retention happens in the first 30 days vs 90 days?

67% of 90-day retention happens in the first 30 days. The average 30-day rate is 8.8% and the average 90-day rate is 13.1%. After 90 days, gains slow dramatically — only about 3 additional percentage points from 90 to 180 days. The first month is the highest-leverage window for retention investment.

Why is BFCM/holiday cohort retention lower?

Holiday acquisition brings in larger cohorts at lower AOVs with weaker retention. Our DTC retention benchmarks show this clearly: the October 2025 cohort was 3x the normal size (12,714 vs ~4,500), had the lowest AOV ($143 vs $237-$474), and the worst 90-day retention (9.6% vs 11-16% typical). Promotional acquisition attracts deal-seekers and gift-buyers whose intent to repurchase is structurally lower.

How long should a post-purchase email flow be?

Based on the retention curve, post-purchase flows should run at least 90 days. 67% of 90-day retention happens by day 30 and meaningful gains continue through day 90. Most brands run 3-5 emails over 7-14 days, which covers only the steepest part of the curve. The flow should map to the full retention window: education in week one, reinforcement in week two, repurchase prompts in weeks three and four, and nudges through month three.

What's the difference between a retention curve and a repeat purchase rate?

The key difference is timing: a repeat purchase rate tells you whether customers buy again, while a retention survival curve tells you when. A repeat purchase rate is a single number — the percentage of customers who buy again within a defined window (e.g., 18.8% over 365 days). A retention survival curve tracks that same behavior over time, showing cumulative retention at fixed intervals. Both metrics matter, but the curve is more actionable because it shows where the opportunity concentrates.

Methodology: How We Tracked 78K First-Time Buyer Cohorts Over 12 Months

• Data window: 12 monthly cohorts, February 2025 through January 2026
• Total cohort: 78,714 first-time buyers across multiple DTC brands
• "First-time buyer" = no prior order in the preceding 365 days
• Retention = placed a second order within the observation window (7d, 14d, 30d, 60d, 90d, or 180d from first order)
• Brands are identified by vertical only, never by name
• Verticals represented: Functional beverage, non-alcoholic spirits, food & bev, decorating supplies, health & wellness, home decor, luxury apparel, and others
• Category labels are approximate — brands are categorized by their primary product vertical. Some brands span multiple categories. If a vertical label seems imprecise, it reflects the best-fit classification, not a definitive categorization
• Aggregate averages are weighted by cohort size
• Cohorts with incomplete observation windows show dashes (e.g., January 2026 only has 7-day data)
• AOV is the average first-order value for first-time buyers in that cohort
• Per-brand ranges represent the min-max across months with sufficient data. Months with fewer than 30 first-time buyers were excluded
• This data will be updated quarterly as additional cohorts mature through the full 180-day observation window