Fixed-price engagement

Product Data Sprint

Product data decides how well all your marketing channels perform and how far your ad spend goes. A one-off project that fixes the foundations behind it all.

Context

Does this sound familiar?

"Our customer acquisition costs keep going up, and we can't just keep adding paid spend - we need it to work harder instead."

"We want to be ready for AI shopping, but we don't know where to actually start."

"Our Shopify store feels messy and disorganised, and we don't really understand our own product data, what's there, what's missing, or why."

"We've been struggling to make real headway with SEO, and we can't quite work out why."

"We're pushing into new markets and ramping up spend, and I've got no idea whether our product data can actually keep up."

What it is

It fixes the thing most of your other marketing depends on.

Product data fuels every channel you run - Google Ads, Meta, on-site conversion, SEO and AI discovery all draw on it. Get it wrong and it drags down how each one performs and how far your ad spend goes. It isn't background housekeeping.

The Sprint builds a system for finding and fixing it, worked out and proved against real products from your own catalogue, and checked against how your actual customers describe and search for them, rather than handed over as generic advice.

5 channels

One source of truth

Google Ads, Meta, SEO, on-site and AI all draw from it

Best & worst

Not theory

Built and proved against real products from your range

2 weeks

Fixed price

Ends in a live walkthrough

The problem · in commercial terms

Three things it's costing you now.

Ad spend buys fewer impressions and clicks than it should. Products with missing or wrong attributes get rejected or limited in Google Shopping and in Meta's catalogue and dynamic product ads, on exactly the lines you're already paying to promote.
Shoppers and AI tools can't find or trust thin products. That costs conversion on-site as well as visibility off it.
Fixing it reactively costs more. One complaint or one rejection at a time, over a year, costs more than fixing it properly once.

The Sprint shows what these gaps are actually costing you, and gives you the system to fix it.

Why now

Shopify stopped being just an ecommerce platform.

For a lot of stores it's become the central point that Google Shopping, on-site search, email marketing and now AI shopping agents all pull from, rather than just a website for customers to buy from.

Merchant Center approval, what AI agents can find and trust, how well collections work, email segmentation, conversion on the page itself - it all depends on whether your data is complete and consistent.

It matters even more if scaling is anywhere on the table - already underway or just the ambition. Launching in a new market, adding a retail or marketplace channel, or ramping paid spend all multiply whatever's already wrong with your data; fixing it before volume goes up costs far less than after.

Google's product data specification

Incomplete or inaccurate product data "can cause disapprovals, limited eligibility, incorrect displays for your products, or other issues."

Why now · the evidence

AI shopping is growing fast, and structured data is what wins it.

197%

Growth in AI-referred traffic to Shopify stores over the past year

Shopify, Q2 2026 commerce data

2x

Better conversion when AI reads structured Shopify catalogue data, versus scraped or third-party feeds

Shopify, Q2 2026 commerce data

Why now · organic

It's an SEO input, not just a Shopping one.

It's what lets you scale SEO across a catalogue rather than fixing pages one at a time. Good product data gives you the taxonomy and structure to work at range.
Thin or duplicated attribute data weakens category pages. A known cause, per Google's own guidance on faceted navigation.
Complete attributes give a product something to rank for beyond its own name. Size, material, colour and use-case variants are long-tail queries thin data can't target at all.

Our approach

A greatest hits of our work at Blink.

It packages up the principles, taxonomy, product data and custom components we've built over the past decade, into something fixed-price and proven.

The model

A bounded, fixed-length engagement, in the mould of Google Ventures' Design Sprint.

The basis

Built on solving these problems for dozens of large-catalogue Shopify stores.

The philosophy

We start with how real customers describe, search for and choose the product, then build your product data structure around that.

Fit

Who it's for, and who it isn't.

A good fit

Shopify stores with a spread of products or variants - not just a single hero SKU
No real data governance process - or one that isn't working
Someone who owns the commercial outcome - founder, head of ecommerce, operations

Probably not

Smaller catalogues
Non-Shopify platforms
A team already running a live product-data process of their own

In those cases a short conversation about what to prioritise in-house is usually worth more than a paid sprint, and we'd point you there instead.

The sprint.

What we look at, what you get, and what it costs.

Scope

What we look at.

We take a sample of your best and worst performing products from across the catalogue, and use it to build and prove a system before rolling it out wider.

Your best and worst performing products, drawn from across your range, used to build and test the fix logic against real examples, checked against how real customers actually describe and search for products like yours
The attribute and metafield schema itself, reviewed once, independent of catalogue size, since this is what the system runs on
The top-level category and collection tree
A baseline snapshot taken on day one - percentage of the sample with a populated GTIN, percentage with populated category-specific attributes, and any visible Merchant Center rejections, so there's a clean before-and-after if you act on the list

Deliverables

What you get.

Delivered as a live walkthrough, not an emailed document.

A repeatable system for prioritising and fixing product data gaps, built and tested against the sample and grounded in real customer research, rather than handed over as theory
A ranked fix list against the sample, with each item tied to a named commercial surface - Shopping eligibility, organic visibility, AI discoverability, on-site search, ad efficiency
Fixes implemented wherever we can within the sample, not just flagged
A document on what applying the system across the rest of the catalogue looks like
A live walkthrough with a senior member of the team, to talk through both the system and the findings
An optional tracking and measurement check - data and tracking reliability is one of the biggest challenges Shopify stores face, so if it's needed, we'll fold a short GA4 and tracking review into the sprint

Investment

Price and timeline.

Price

£2,500, fixed

Timeline

1 to 2 weeks from data access

Scope

A system built and proved against your best and worst performing products, plus schema and category tree review, not a manual pass over the full catalogue

Commitment

Standalone. No retainer attached, no obligation to continue

Priced for what it takes to build and prove the system properly on a working sample. Everything it produces, the system included, is yours to keep regardless of what you decide afterwards.

Frequently asked questions.

Questions · fit

Is this right for us?

We're not on Shopify

No. The scope, price and review are built around Shopify's schema and metafield model. Doing it properly elsewhere means rebuilding the approach, and we'd rather say that than do a worse job of it.

We've only got a handful of SKUs

Probably not worth it. Comparing best and worst performers needs enough range to show real patterns - a shorter, unpaid conversation is usually more useful with just a handful of products.

Some data lives in a PIM

This will need some more investigation before we can scope it properly. We work from what Shopify actually serves, so we need to know what's being pushed in from elsewhere first.

We're mid-migration

Often more useful now than later. Migrations are exactly where product data problems get introduced or carried over, and catching that early costs less.

We have someone in-house

Depends what they own. Someone owning it with marketing performance in mind is great news - usually a short conversation is enough as a sense check. Ownership from a merchandising angle alone often still leaves a gap worth a second look.

Questions · price

Does it pay for itself?

How do we know?

If you don't see value in the final output, you don't pay for it. It's as simple as that.

Why cheaper than an audit?

Scoped differently, not scoped down. The lower price reflects the smaller proving ground, not a lower bar - the system tested against your best and worst performers is the one that would run against the rest of the catalogue.

What if nothing's wrong?

Then you have a genuine baseline saying your data's in reasonable shape, and you keep it either way. We'd rather tell you that than manufacture findings to justify the fee.

Questions · how it runs

How does it actually work?

Who does the work?

The same senior team who'd run a larger engagement, and a senior lead delivers the readout directly rather than handing it to someone else to present.

What access do you need?

Full Shopify admin, plus read access to GA4, Google Search Console and Google Ads. We also install a custom app that extracts product data at scale, which is what makes it possible inside two weeks. We talk through exactly what and why before any of it goes live.

Questions · what this isn't

How is this different?

Versus a free audit

A free audit is usually a sales conversation with a report attached, scoped loosely enough to always find something to sell. This is scoped tightly on purpose, so the work is judged on what's delivered.

We've paid for audits before

This doesn't end in a document that sits in a drive. Findings tie to a fix list against a defined sample, and fixes get implemented wherever we can rather than only described.

Is it just Google Shopping?

No. The same data feeds Shopping, organic, AI discovery and on-site search. A missing attribute can cause a disapproval, weaken category pages, and remove long-tail terms all at once.

Isn't this just a taxonomy or schema audit?

No. Most are compliance exercises against a schema. Ours starts from how your customers actually describe, search for and choose the product, checked against real audience research, then builds the structure around that.

We've already got a feed or paid specialist

No conflict. They optimise what's pushed into the feed or ad account, we fix it at the source in Shopify, so their optimisation has better data to work with.

If we don't continue?

Nothing. No follow-up commitment, and the system, fix list, baseline metrics and quick fixes are yours regardless of what you decide next.

Getting started

Interested in finding out more?

Fill this in and we'll come back to you with whether it's a fit, a scoped sample, and a date. If it isn't a fit, we'll say so and point you at what to do instead.

Would rather just email? sam@blinkseo.co.uk