How to Increase AOV for DTC Brands: The Cohort-Led Playbook (With Ben Sharf, Platter)

Why AOV is the most pressured metric in DTC right now

If you've spent any time on DTC LinkedIn or Twitter recently, you'll have noticed how much oxygen the AOV conversation is taking up. There's a reason for that.

CAC isn't going down. Tariffs are compressing margins. Blended ROAS is harder to defend every quarter. So everyone is racing to squeeze more profit out of the first order. And yet, the more I speak to brands and the more I see in the accounts we audit, the more I'm convinced that most of them are squeezing the wrong number entirely.

That's the conversation I wanted to have with Ben Sharf, co-founder of Platter — a Shopify Platinum partner that's worked with over 200 stores in the last four years. What makes Ben's perspective particularly sharp is that Platter is unusual in being both an agency and a tech provider — they've built their own Shopify theme and Shopify app, which means they sit on a layer of on-site behavioural data most agencies don't have access to. He's not theorising about AOV. He's looking at what hundreds of thousands of real shoppers are actually adding to carts.

What came out of the conversation was a list of mistakes that mirror almost exactly the mistakes I see retention teams making every day, just from a different angle. Here's the full breakdown — Ben's tactical insights from the on-site side, and how I'd map each one back to retention strategy.

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What is the biggest mistake DTC brands make with AOV?

According to Ben, most brands are forcing AOV onto a customer instead of earning the right to drive it.

There are three connected versions of this mistake that he sees over and over.

1. They don't actually understand their existing AOV before designing incentives.

If your AOV is $70 and you set a free shipping threshold at $75, you're not lifting anything. You're just discounting customers who were already going to spend close to that. As Ben put it: "It actually just turns into a margin hit." You need to know what your AOV is — and more importantly, what it's hiding — before you can use it as a lever.

2. They throw generic upsells at the customer and hope for the best.

A carousel with 10 products on the cart page doesn't drive AOV. It causes decision paralysis. When customers are given too many options with no clear pairing logic, they don't pick more — they pick none, or pick less than they would have.

3. They have poor product discovery.

If your catalog is structured so customers can't easily find the products they'd happily spend more money on, you've capped your AOV before they ever reach the cart. Most brands aren't actually thinking about how their catalog architecture is guiding the customer — they're just listing inventory.

I asked Ben to walk through each one in turn. Below is the full breakdown.

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How to fix poor product discovery on your Shopify store

Ben's view is that this is one of the most under-diagnosed problems in DTC.

Most brands, he argues, don't actually understand the impact of poor product discovery — so they're not architecting their catalog to drive the consumer anywhere. They're just listing it.

The fix depends entirely on catalog size:

• If you have 4 SKUs, a flat menu with each SKU as a standalone card is fine. There's nothing to organise.
• If you have 1,000 SKUs, you need to think hard about category and subcategory structure, and about how that hierarchy guides the customer. What are the layers of discovery? Where do you want a buyer with intent on Product A to land next? How are complementary products surfaced without burying them?

Ben's framing was simple: "Ultimately, you're thinking about how do we guide the customer where we want them to go."

This maps almost exactly onto what we obsess over on the retention side. If you don't know which products in your catalog yield disproportionate LTV, you can't position them in the post-purchase journey. If your customer can't find Product B easily on-site, they won't find it any easier through an email link either. Discoverability is a cross-channel concern, not just an on-site one.

The play: Audit your catalog from a first-time buyer's perspective. Map the path from your top three traffic-driving product pages to the next-best complementary SKU. If it's more than two clicks away, restructure. Then apply the same logic to your post-purchase email flows — ensure the products that drive disproportionate LTV are surfaced consistently across both surfaces.

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How to use cart upsells without killing AOV

This was where Ben dropped the most counterintuitive insight of the whole conversation, and honestly, it's reshaping how I think about cross-sell strategy on the retention side too.

Most brands' cart upsells and "frequently bought together" modules are essentially guesses. There's no real data set behind them. The recommendations are whatever the merchandiser thought paired well, or whatever the platform's default logic spat out.

Ben's team at Platter ran a data study that genuinely surprised him.

They took 90 days of historical order data from a handful of supplement clients and decoupled, for every product purchased, what the most popular second product was in the same order.

The result wasn't what anyone expected.

"What we actually found was the most frequently purchased product as the add-on was the same SKU in a different flavour," Ben told me. "Imagine you're buying like an electrolyte product and you just buy the lemon-lime. A lot of brands would pair that with a shaker bottle. But what we found is that consumers were going out of their way to add the berry flavour as the second product they were buying, irrespective of any sort of discounts or incentive to do so. People wanted the same product, but more variations of it."

Now — Ben was careful to caveat that no two brands are identical. The takeaway isn't "go pair flavours of the same SKU". The takeaway is far more important than that:

Stop guessing what your customers want to pair, and look at what they're actually doing.

When Platter replaced generic frequently-bought-together logic with data-driven pairing logic across their clients' stores, they saw substantial AOV uplifts. No new SKUs. No discount sacrifice. No creative production. Just better pairing logic.

What's fascinating to me is how directly this mirrors something we've been seeing on the retention side for years.

For a long time, we tried to engineer the perfect post-purchase journey for our clients. Customer buys protein powder → cross-sell them creatine, because that's the logical pairing. We've now seen, repeatedly, that the highest repeat purchase rates often come from just reselling the original item the customer bought, not introducing a new one. They liked it. They want more of it. Sometimes in a different flavour.

We were so busy trying to predict consumer behaviour and engineer a magical customer journey that we were ignoring what the data was already telling us.

Ben's seeing the same pattern on-site that we're seeing in the inbox.

The play: Pull 90 days of order data. For each of your top 20 SKUs, identify the single most common co-purchased SKU. Make that the upsell on the product page and in the cart — not whatever the platform default suggests. Re-audit every 90 days as your catalog and customer mix shifts. Then mirror the same pairs in your post-purchase email and SMS flows.

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Should DTC brands offer free shipping?

This is one of those debates that won't die on LinkedIn.

Some people swear free shipping is a margin-killer that should be removed entirely. Others treat the threshold as untouchable. Over the last couple of years I've actually seen the pendulum swing back the other way — more brands quietly reintroducing it as an AOV mechanism after years of giving it away.

Ben's view is more measured, and I think it's right.

"We don't treat free shipping as a growth lever," he told me. "We see it as table stakes. So many brands are doing it that if a consumer's shopping and the first five sites they saw had a free shipping threshold, and then you don't, they're like, 'wait, why are you different?' We do believe it's a table-stakes thing."

The point is that free shipping isn't the lever to lift AOV. It's the floor — the cost of being in the consideration set. Removing it might lift margin on a per-order basis, but the conversion rate damage is rarely worth it for most categories.

If you're working on AOV explicitly, there are far more effective mechanisms to reach for first:

• Free gift with purchase (GWP). Creates a sense of desire and a clear "if I add $X, I unlock Y" mental model. Lifts perceived value without eroding the per-order discount profile.
• Build-your-own-bundle (BYOB). Increases perceived value while reducing decision fatigue, because the customer is the one curating the basket.
• Contextually paired cross-sells. The data-driven version we just covered.

These three work because they layer perceived value on top of the order. Free shipping just removes a friction. Friction-removers and value-adders are not the same thing — and most brands conflate them.

The play: Treat free shipping as a deliverability problem (the cost of competing), not a growth lever. Set the threshold at or just above your median basket size — never below it. For real AOV lift, deploy GWP, BYOB, or paired cross-sells.

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Why your AOV strategy is failing: you're optimising for an average

This is the point where the conversation shifted from tactics to something deeper, and it's the part that's stuck with me most.

I asked Ben whether there's a threshold at which AOV "hacking" goes too far — where you erode the customer experience and damage repeat purchase rates by selling people things they don't need.

His answer reframed the whole question.

"A lot of brands actually just straight up analyse AOV in the wrong way," he said. "A brand that sells a mix of $20 baskets and $80 baskets can have the same AOV as a brand that sells $50 baskets."

Stop and read that again.

Two brands. Same headline AOV. Completely different businesses, completely different customer cohorts, and completely different optimisation paths. Trying to get the customer who spends $20 to spend $27 is a fundamentally different mechanism than trying to get the $80 customer to spend $87 — and you almost certainly need different products in your catalog to drive each one.

"AOV stands for average," Ben said. "But the average is not the thing you want to look at when you're trying to drive incremental dollars. You need to segment it out and understand the different cohorts."

This is the most important point in the entire conversation, and it's the one most brands ignore.

We do exactly the same thing on the retention side. We slice by product purchased, by lifetime value, by repeat purchase window, by acquisition source. Aggregating across the whole customer base — especially for brands with broad catalogs — is one of the fastest ways to land on the wrong strategy.

Almost every retention audit we run uncovers the same thing: there are golden nuggets hiding in the cohorts that the headline averages completely obscure. A product the brand isn't promoting that's quietly driving disproportionate LTV. A buyer segment with a wildly different repurchase window than the average. A SKU that should be repositioned at the top of the funnel but is buried three clicks deep on the site.

If your AOV is one number on a dashboard, you're not analysing it. You're avoiding it.

The play: Before you design a single AOV incentive, segment your purchase data into at least three cohorts by basket size — bottom, middle, top. Look at the distribution, not the mean. Then build separate strategies for each cohort: different incentives, different cross-sell logic, different thresholds. This is how you stop applying blanket strategies to vastly different customer behaviour.

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The single biggest AOV change a Shopify brand can make this month

I closed the conversation by asking Ben what one change a founder could make in the next 30 days for the biggest AOV impact.

His answer was emphatic.

"I'm going to die on this hill — fix the pairing logic of your upsell products."

For every customer landing on a product page or in your cart, the question your store should be answering is: based on what's already in this basket, what is the next-best thing this specific customer is most likely to add?

Generic carousels can't answer that question. Default Shopify recommendations can't answer that question. "Frequently bought together" modules with no real data behind them can't answer that question.

Data-driven pairing logic can.

If you're a big enough store with enough order history to draw signal from, and you haven't done this — that is absolutely the first place to start. The AOV uplift is usually substantial, the margin sacrifice is zero, and there's no creative production required.

This carries directly over to subscription brands, by the way. Ben pointed out that the same logic applies to buy-box optimisation — the take rate on a subscription offer is just an AOV decision dressed up as a subscription one. If your two highest-density buyer cohorts are the $20 and $80 spenders, you should be architecting two different first offers and two different upsell paths, not one homogenised flow.

The play: This week, pull your order data and identify the top co-purchase pairs for your top 20 SKUs. Next week, swap out your default frequently-bought-together modules with the actual winners. Mirror the same pairs in your post-purchase email and SMS flows so the cross-sell story is consistent across surfaces. Measure AOV at 30, 60, and 90 days post-change.

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How AOV optimisation connects to retention and LTV

Here's why all of this matters beyond the first order.

AOV strategy and retention strategy are the same problem seen from two different surfaces. The pairing logic that lifts AOV on-site is the same logic that should be driving your post-purchase email and SMS cross-sells. The cohort segmentation that should determine your incentive design is the same segmentation that should determine your campaign sending strategy and your replenishment timings.

Brands that align these two sides — using the same data-backed product pairs across on-site and CRM — compound the lift across both first and repeat orders. Brands that don't, end up with retention teams cross-selling one product while the website cross-sells another, and customers receiving inconsistent messaging across channels.

We already know that recency is the single strongest predictor of repeat purchase. We already know that the first 30 days post-purchase is the highest-leverage window for driving the second order. If your on-site upsell is offering the customer the wrong product to begin with, you're setting your retention flows up to fail before they trigger.

The fix is unsexy. Pull the data. Segment the cohorts. Find the real pairs. Apply them everywhere — site, cart, email, SMS, subscription buy-box, post-purchase flow.

It's not a clever tactic. It's just doing the basics with actual evidence instead of vibes.

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Frequently asked questions about AOV optimisation

What is the most common AOV mistake DTC brands make?

Setting incentives — especially free shipping thresholds — based on what they wish their AOV was, rather than what it actually is. If your threshold sits below your existing AOV, you're discounting purchases that would have happened anyway. The fix is to segment customer cohorts by basket size and design separate incentives for each group.

How do I diagnose poor product discovery on my Shopify store?

Walk your catalog from a first-time buyer's perspective. Map the path from your top three traffic-driving product pages to the next-best complementary SKU. If it takes more than two clicks to find a product the customer would logically pair with what they're viewing, your discovery architecture is costing you AOV. The bigger the catalog, the more intentional your category and subcategory hierarchy needs to be.

Should DTC brands offer free shipping?

Treat free shipping as table stakes, not a growth lever. Most customers expect it, and not offering it can create negative sentiment that costs you the conversion before any AOV strategy can kick in. But don't expect free shipping to lift AOV on its own. For real AOV growth, use free gift with purchase (GWP), build-your-own-bundle (BYOB), and contextual cross-sells instead.

How can I improve my "frequently bought together" upsells?

Pull 90 days of order history. For each of your top 20 SKUs, identify the most common second product purchased in the same order. Replace your platform's default recommendations with these data-driven pairs. Re-audit every 90 days as your catalog and customer mix changes. The biggest miss in most brands' frequently-bought-together logic is that they never tested what customers actually pair — they just guessed.

Why are generic upsell carousels bad for AOV?

They cause decision paralysis. When customers are shown 10 unrelated products at the cart, they're more likely to pick none than to pick more. Limit upsells to 1–3 contextually paired products that actually correlate with what's already in the basket. Quality of pairing matters more than quantity of options.

What's the single biggest AOV change a Shopify brand can make this month?

Fix the pairing logic on your upsell products using historical purchase data. It requires no new SKUs, no margin sacrifice, and no creative work — just analysis. For most brands with sufficient order volume, this single change drives a substantial AOV uplift within 30–90 days. It also compounds when mirrored across post-purchase email and SMS flows.

How does AOV strategy connect to retention and LTV?

Both depend on cohort thinking, not averages. The pairing logic that lifts AOV on-site is the same logic that lifts repeat purchase rate in post-purchase email and SMS flows. Brands that align on-site cross-sells with email-driven cross-sells — using the same data-backed pairs — compound the lift across both first and repeat orders. Brands that don't, end up undermining their own retention efforts at the cart.

What's a realistic AOV uplift target?

This depends entirely on your starting point and your category, but a 10–20% AOV lift from pairing logic alone is achievable for most brands with sufficient order data. The more important number is incremental margin contribution per customer — measured against the cohort, not the aggregate.

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Follow Ben: LinkedIn · Platter · Ben's YouTube channel — genuinely some of the most authentic founder content out there. Highly recommend.

Related reads:

• How to Scale LTV From Your Existing Subscribers: A 4-Play Upsell Playbook for DTC Brands
• Why the First 30 Days Matters the Most for Your Retention Marketing
• The Retention Marketing Strategy Bible for DTC Brands
• 6 Powerful Emails To Engage Your DTC Subscribers & Boost Retention