Retail and D2C
Add up what every platform claims to have driven. It is more than your total revenue. That is the attribution problem we solve.
Every platform over-reports its contribution. Every report arrives too late to act on. Every stockout is discovered by a customer before it appears in your inventory view. These are not technology problems. They are data infrastructure problems. We build the foundation that fixes all three.
The problem
Where it breaks down.
Your ROAS number is wrong and everyone in the room knows it
Meta reports a ROAS. Google reports a ROAS. Your marketplace reports a ROAS. Add them up and the total attributed revenue is more than you actually generated. Budget decisions made on platform-reported numbers are budget decisions made on fiction. The accurate number exists, but only once you have a unified attribution model that does not let every platform take full credit for the same sale.
Stockouts are a customer service problem because they are not a data problem yet
By the time a stockout appears in a report, the sale is already lost. Possibly the customer too. Inventory managed from last week’s sell-through data is inventory managed reactively. The forward-looking signal exists in your sales patterns, your seasonality data, and your promotional calendar. It just has not been connected into a forecast yet.
You know your acquisition cost. You do not know your retention curve.
Day-30 retention is where most retail and D2C businesses leak the most value. But the cohort data is either missing, inconsistent across sources, or arrives monthly when you need it weekly. Acquisition costs keep rising and repeat purchase rates stay flat because the problem is not visible in time to act on it.
What we build
How we fix it.
Unified marketing attribution
A single attribution model connecting every marketing channel through consistent logic that finance and marketing both agree on before it is built. True customer acquisition cost by channel, campaign, and cohort. Not what Meta claims. Not what Google claims. What the data shows when it is all in one place with one set of rules.
Outcome
Businesses implementing unified attribution reallocate marketing budget away from channels that appeared to perform well in platform reporting and toward channels that the unified data shows are actually driving revenue.
Inventory intelligence and demand forecasting
SKU-level demand forecasting using your historical sales data, seasonality patterns, promotional calendars, and channel signals. Automated stockout risk alerts that surface weeks before the stockout would occur. Reorder point calculations that update as demand patterns change rather than sitting as a fixed number in a spreadsheet.
Outcome
Brands implementing inventory intelligence reduce stockout frequency and free up the capital tied up in overstock. The information to manage inventory better was always there. It just was not organised into a forecast.
Cohort and retention analytics
Day-7, Day-30, Day-60, and Day-90 retention cohorts by acquisition channel, product category, first purchase SKU, and geography. Built on a consistent customer identity that does not fragment across devices and sessions. See exactly where customers are dropping off, what the common characteristics of the customers who stay are, and what interventions work at each stage of the retention curve.
Outcome
Brands acting on cohort data improve 30-day retention. The improvement comes from being able to identify the specific drop-off point and target it precisely rather than running broad re-engagement campaigns with no diagnostic basis.
Revenue and margin intelligence
A daily view of revenue and contribution margin by channel, category, and SKU, including fulfilment cost, returns, marketplace fees, and advertising spend allocated to that SKU. The difference between gross revenue and true contribution margin is where most retail businesses are surprised when they finally see it.
Outcome
Businesses identify SKUs that appear profitable in gross revenue terms but generate negative contribution once true costs are allocated. The reallocation of ranging, marketing, and fulfilment decisions that follows improves overall portfolio margin.
Customer service and order intelligence automation
AI that handles order tracking queries, return status updates, delivery timeline questions, and product information requests automatically, without a human agent in the loop. The routine customer contacts that follow a predictable pattern, resolved instantly. The remaining contacts reach a human with full order context already assembled.
Outcome
Brands reduce customer service cost per contact and improve response time for routine queries from hours to seconds. Agent capacity is redirected to the contacts that actually require human judgment.
Most data engagements start with a conversation about a specific problem.
Tell us what yours is. We will tell you honestly whether we are the right team to solve it.
We respond within 24 hours.