Know your customers.
Understand who returns, what brings them back, and which first purchases lead to lasting customer value.
RETAIL INTELLIGENCE, WITH INTENT
Your next move.
Hidden in
your data.
Your store has the answers. We’re building the science to find them — so you know which customers, products, and opportunities deserve your attention.
Built for ambitious ecommerce brands.
behind the purchases.
Explore an example data insight
FROM DATA TO DIRECTION EXAMPLE
Customer value over time
First purchase: Starter Kit
Starter Kit customers Store average
One product. A different customer story.↗
OPPORTUNITY FOUNDBetter customers start here.
42% higher customer value over 12 months.
Less noise.
More “now I know.”
Illustrative data. Real decisions are the goal.More “now I know.”
A CLEARER PICTURE OF YOUR BUSINESS
Customer behaviour Product performance Purchase patterns
Your reports tell you what happened.We help you understand what to do next.
01 / THE SCIENCE
Three lenses.
A whole new perspective.
Your everyday transaction data holds more than sales totals. Find the patterns that turn into better commercial decisions.
Find your real heroes.
Look beyond bestsellers. Discover the products that attract valuable customers, gain momentum, or need attention.
Connect the purchases.
See what sells together, what customers buy next, and when they’re likely to be ready for a reorder.
THE “AHA” MOMENT
From an interesting
number to a
useful next step.
The goal is simple: surface something you wouldn’t spot in a standard sales report, explain why it matters, and give you a direction to explore.
CUSTOMER SCIENCEILLUSTRATIVE INSIGHT
Your best acquisition product might surprise you.
Customers whose first order includes the Starter Kit come back more often — and spend more over time.
+42%12-month customer value
A NEXT STEP TO TEST
Investigate the Starter Kit as an acquisition offer.
02 / REAL RETAIL QUESTIONS
Start with a decision.
Work back to the evidence.
Six ways we’re designing Retail Science Co to help. These are illustrative use cases, not customer results or promises of a return.
THE HUMAN SIDE OF THE NUMBERS
A first purchase.
A familiar face.
A reason to come back.
Those moments build a retail business. Your transaction history can help you understand which products start a relationship, which purchases follow, and when customers return.
Better analysis gives you a clearer place to start — so your next offer feels more relevant to the person receiving it.
Illustrative retail scene.Choose a better first offer.
A beauty brand’s bestselling trial product brings in plenty of new customers. But are they coming back?
WHAT THE DATA CAN REVEAL
Compare repeat rates and 90-, 180-, and 365-day customer value for every product in a customer’s first order.
THE DECISION TO EXPLORE
Test acquisition offers around products associated with stronger long-term customer value, rather than choosing by first-order revenue alone.
Get the timing right.
A coffee or skincare store sends the same reminder to every customer, regardless of when they usually replenish.
WHAT THE DATA CAN REVEAL
Measure actual repurchase intervals by product, then identify pseudonymous customer groups approaching their typical reorder window.
THE DECISION TO EXPLORE
Plan a replenishment campaign around observed buying rhythms. Early access identifies the opportunity; it does not send messages.
Find the natural next purchase.
A homewares brand wants a relevant follow-up offer after a first purchase, instead of another blanket discount.
WHAT THE DATA CAN REVEAL
Look for purchases that follow a particular product within 30, 60, 90, or 180 days, excluding items bought in the same order.
THE DECISION TO EXPLORE
Test a follow-up offer for the next product customers already tend to buy, with timing grounded in their purchase histories.
Build bundles with evidence.
An accessories store has hundreds of possible bundles. Which combinations have a genuine relationship?
WHAT THE DATA CAN REVEAL
Measure how often pairs appear together, their directional purchase rates, and lift relative to each product’s usual popularity.
THE DECISION TO EXPLORE
Shortlist bundle and product-page recommendations supported by sufficient order volume, then test whether they increase basket value.
Catch a quiet decline.
Store revenue is holding steady, but a once-reliable product is slowing down beneath the headline total.
WHAT THE DATA CAN REVEAL
Compare recent sales velocity with a previous baseline and, where history permits, the same period last year.
THE DECISION TO EXPLORE
Investigate stock availability, merchandising, pricing, or seasonality before deciding whether to change the offer. A decline flags a question, not its cause.
Check whether growth lasts.
A promotion brings in a wave of first-time buyers. The immediate sales look good; the longer-term picture is unclear.
WHAT THE DATA CAN REVEAL
Compare acquisition cohorts at the same follow-up age and track their repeat purchases and customer value.
THE DECISION TO EXPLORE
Use retention evidence to assess the quality of new customers. Campaign attribution and ad profitability require additional data and are outside the initial scope.
For founders, ecommerce leads, and the teams making the next commercial call.Bring your question to early access
03 / HOW IT WILL WORK
Your data.
A little more direction.
We’re starting with the data you already have. No new tracking. No black-box chatbot. Just focused retail science.
Start with your store.
Early access starts with historical WooCommerce transaction data, using read-only access.
Let the patterns emerge.
Customer, product, and basket analysis uncover relationships and changes worth investigating.
Make your next move.
Get a focused report with ranked insights, supporting evidence, and ideas you can put to the test.
Built with privacy in mind.The planned analysis uses pseudonymous customer keys. Customer names, emails, and addresses stay out of the analytical dataset.
READ-ONLY BY DESIGN04 / EVIDENCE BEFORE CONFIDENCE
An insight should
earn your attention.
We’re developing a repeatable analytical pipeline with explicit checks and visible supporting metrics. Here’s what that means in practice.
Check the starting point.
The planned pipeline checks storage mode, order counts, statuses, and revenue totals against the source. Refunds are represented separately, so customer value isn’t inflated by treating every order as a completed sale.
Give small samples less weight.
Initial configurable thresholds include 30 customers for acquisition-product comparisons, 20 orders for product analysis, and 5 shared orders for a product pair. Thresholds are a first filter, not proof of statistical significance.
Account for time to return.
A new customer hasn’t had a year to demonstrate 12-month value. Fixed-window comparisons need sufficient follow-up; partial histories should be identified rather than presented as equivalent to mature cohorts.
Show the reason, not just a score.
The planned report includes the period, sample size, comparison baseline, and supporting metrics. Insights are ranked by impact, confidence, urgency, and actionability, using deterministic calculations rather than an LLM.
A pattern is a starting point for a test.
Transaction data can reveal associations. It cannot, by itself, prove that a product causes loyalty or that a particular action will improve sales. Your store context and a measured experiment complete the picture.
EARLY ACCESS / COMING SOON
Good decisions
start with
better questions.
Be among the first to put Retail Science Co to work. Join the waitlist for launch updates and an invitation when early access opens.
STARTING WITHWooCommerceShopify on the roadmap
A first look. A head start.
Tell us a little about your store.
THE RETAIL SCIENCE FIELD GUIDE
Better questions.
A stronger starting point.
Explore the measures behind customer retention, value, product performance, and purchase patterns.
CUSTOMER SCIENCE
Browse all retail analytics guides WooCommerce Customer Retention Analysis
Are new customers becoming repeat customers?
CUSTOMER VALUEWooCommerce Customer Lifetime Value Analysis
Which customers and first purchases lead to lasting value?
PRODUCT SCIENCEWooCommerce Product Analytics Beyond Bestsellers
Which products deserve more attention — and why?
BASKET SCIENCEMarket Basket Analysis for Ecommerce
What belongs together — and what comes next?
REORDER SCIENCEWooCommerce Reorder and Repurchase Analysis
When might a customer be ready to buy again?
A FEW GOOD QUESTIONS
Before you
take the next step.
What is Retail Science Co?+
Retail Science Co is an early-stage retail intelligence product. We’re building a way to turn ordinary transaction data into specific decisions about customer retention, product performance, cross-sell, and reordering.
Which ecommerce platforms will you support?+
We’re starting with WooCommerce. Shopify is on the roadmap. You can join the waitlist with either platform — or another one — so we can understand demand and keep you updated.
Is the product available yet?+
Not yet. We’re developing the first version and validating the analysis. Joining the waitlist means you’ll receive launch updates and an invitation when early access opens. There is no published launch date or pricing yet.
How will customer data be handled?+
The planned analytical pipeline reads source data without changing your store. It replaces customer identities with pseudonymous keys and excludes direct identifiers such as names, raw emails, phone numbers, and addresses from the analytics database. The email you submit to this waitlist is stored separately for early-access communication.
What will an early-access report include?+
The planned report brings together customer retention and value, product performance, purchase relationships, and reorder opportunities. Its focus is a short ranked list of observations with supporting metrics and a practical question or action to investigate.
How much historical data do I need?+
There is no single minimum that makes every analysis useful. You need enough transactions to meet the relevant sample threshold and enough history for the comparison period. A 365-day customer-value analysis needs 365 days of follow-up for the customers included. Results with insufficient history or volume should be withheld or clearly identified.
Will this tell me profit or advertising return?+
The initial scope uses transaction revenue and purchase behaviour. It does not include reliable product costs, margins, ad spend, or campaign attribution. Customer value here means observed revenue to date or within a stated window, not predicted profit or guaranteed future lifetime value.
Does joining the waitlist cost anything?+
No. Joining is free and doesn’t commit you to purchasing anything. Pricing and early-access details will be shared before any purchase decision.