Why the Second Carton Never Got Bought: How an Oat Milk Brand Used IDIs and Purchase Analytics to Crack a Trial-Versus-Repeat Mystery

Executive Snapshot

Client

Oat Milk Brand, United Kingdom

Situation/Challenge

First-purchase trial for the client's oat milk landed strong after its retail launch, but repeat purchase over the following two months fell well short of the brand's internal target. Marketing pinned it on taste and started planning a reformulation, while the retail team suspected pricing, yet neither theory had been checked against the people who actually tried it once and never came back.

Objective

Run structured IDIs with trial buyers who did not return, paired with purchase pattern analytics across retail partners, to nail down the real cause of the repeat purchase shortfall before committing to a reformulation.

Constancy Researchers Solution

Primary Research & VoC through In-Depth Interviews (IDIs) combined with Data Analytics & Business Intelligence, 32 IDIs with non-repeating trial buyers, paired with analytics examining purchase timing, basket composition, and retail availability patterns.

Impact

The interviews showed taste was almost never the reason buyers skipped a second purchase, most pointed instead to a specific in-store availability problem. Analytics backed this up, showing repeat purchase rates ran far higher in stores that kept the product consistently stocked, pointing to a distribution issue rather than a product one.

Client Outcome

The client shelved its planned reformulation and instead worked with retail partners to fix shelf gaps at the exact stores analytics had flagged, recovering a large share of the repeat purchase shortfall within two quarters.

The Situation / Challenge

Alternative dairy brands often land strong initial trial through sampling, retail promotions, and curiosity-driven first buys, but turning that trial into a habitual repeat purchase is a separate and considerably tougher commercial problem. A weak repeat rate can stem from any number of causes, taste, price, availability, or simply slipping off a shopper’s routine list, and each cause calls for a completely different fix.

The client’s oat milk had performed well at retail trial, but repeat purchase within two months landed well below target. Internal teams jumped to competing explanations without testing either, marketing assumed the taste was not habit-forming enough and started scoping a reformulation, while retail suspected the price sat too high against rival oat milk brands. Neither team had actually spoken with the customers who tried it once and never returned.

Committing to an expensive reformulation on an untested hunch risked solving entirely the wrong problem, especially if the real cause of the repeat shortfall had nothing to do with the product itself.

Key Challenges

  • No direct customer research establishing why trial buyers were not coming back to repurchase.
  • Two competing internal theories, taste from marketing and price from retail, neither checked against actual customer experience.
  • No purchase pattern analytics checking whether repeat purchase varied by store or region rather than by product perception.
  • A real risk of committing to a costly reformulation aimed at the wrong underlying problem.
  • No structured way to separate a genuine product issue from a distribution or availability issue dragging down repeat purchase.
  • Pressure from commercial leadership to lift repeat purchase quickly, creating a temptation to act before the real cause was understood.

Weak repeat purchase in consumer products gets blamed on the product itself far too often, when the real cause frequently sits in distribution, availability, or simple forgetting. Direct customer research paired with purchase pattern analytics is usually the fastest way to tell a genuine product problem apart from an operational one.

Constancy Researchers Solution

Constancy Researchers combined direct interviews with non-repeating trial buyers and purchase pattern analytics across the client’s retail partners, testing both internal theories against actual customer evidence before touching the product.

In-Depth Interviews (IDIs) with Non-Repeating Trial Purchasers
  • Ran 32 structured IDIs with customers who bought the product once but had not repurchased within the following two months, exploring their actual experience and their reasons for not returning.
  • Found taste was rarely cited as the reason for skipping a repurchase, by a clear margin the most common reason was simply not finding the product on the shelf during a later shopping trip.
Purchase Pattern & Retail Availability Analytics
  • Analysed purchase timing and repeat rates across the client’s full retail partner network, comparing repeat purchase rates against each store’s shelf stock consistency.
  • Confirmed repeat purchase ran significantly higher in stores with consistent shelf availability and significantly lower in stores with frequent stockouts, directly backing the availability story the interviews had surfaced.
Retail Partner Inventory Pattern Investigation
  • Dug into inventory practices at the stores showing the weakest repeat rates, finding a recurring restocking delay hitting the client’s product harder than comparable competing products.
  • Traced the delay to a minimum order quantity threshold the client’s product had not yet reached at those particular stores, a distribution issue with nothing to do with taste or price.
Distribution Resolution & Retail Partner Engagement
  • Delivered a resolution plan engaging the affected retail partners directly, fixing the minimum order quantity issue and setting up more consistent restocking schedules at the flagged stores.
  • Negotiated a temporary order quantity exception for the flagged stores to close the stocking gap immediately while the longer-term threshold adjustment took effect.
Repeat Purchase Recovery Tracking
  • Set up a weekly repeat purchase rate tracker segmented by store, allowing the commercial team to confirm the distribution fix was actually translating into recovered sales.
  • Defined a recovery benchmark against the pre-shortfall repeat purchase baseline, giving leadership a clear target for judging when the fix had fully taken hold.

The work headed off a costly reformulation aimed at the wrong target, redirecting the company’s resources instead toward the distribution issue interviews and analytics had jointly identified as the actual cause.

Impact

  • Interviews identified shelf availability, not taste, as the primary reason trial buyers skipped a second purchase.
  • Purchase analytics confirmed repeat rates tracked directly with store-level shelf stock consistency.
  • The investigation traced a specific minimum order quantity threshold causing recurring restocking delays.
  • The planned reformulation was shelved once the real cause was confirmed as distribution rather than taste.
  • Retail partner engagement fixed the restocking delay at the exact stores analytics had flagged.
  • Shelf availability improved at the affected stores following the resolution plan.
  • A large share of the repeat purchase shortfall was recovered within two quarters.
  • The client avoided the cost and delay of a reformulation that would not have fixed the real problem.

Client Outcome

Reformulation Avoided

A costly reformulation was shelved once research confirmed taste was not behind the repeat purchase shortfall.

Repeat Purchase Recovery

A large share of the repeat purchase shortfall was recovered within two quarters following the distribution fix.

Distribution Fix

A specific minimum order quantity issue causing restocking delays was identified and fixed with retail partners.

Diagnosis Accuracy

Interviews and purchase analytics independently pointed to the same root cause, giving leadership confidence before acting.

Resource Efficiency

Commercial resources shifted from product development toward the distribution issue actually responsible.

Retail Partnership Strengthened

Direct engagement with affected retail partners improved restocking consistency well beyond the immediate fix.

Internal Alignment

Marketing and retail aligned around one evidence-based explanation instead of continuing to argue competing theories.

Customer Insight Capability

The client built a direct line of customer feedback for diagnosing future repeat purchase issues.

Market Positioning

The brand was repositioned as a data-disciplined operator diagnosing commercial problems with evidence rather than reflexively blaming the product.

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