Client challenges

Which of these sounds like your business?

Suppliers rarely arrive with a request for "analytics." They arrive with a problem that is costing them money, time or credibility with a retailer. These are the five we see most, with what we would do about each.

Not sure which applies? Describe the situation in your own words and we will tell you where to start.

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Understand our retail performance

Sales are up at one retailer and down at another, and nobody can say with confidence why. Reports show what happened; they do not explain it.

What we do

Decompose your sales into the things that actually move them: stores selling, units per store, price and mix, promotion, and seasonality. Compare retailers on a like-for-like basis. Rank the gaps by value.

What you receive

A driver analysis by retailer and category, a ranked opportunity list with the value and evidence behind each item, and a short written readout of the three things that matter most.

Illustrative scenario

A housewares supplier's dollar sales were flat year on year. The decomposition showed units down 6% at the largest account, entirely explained by a distribution cut in 140 stores where the SKU had been in the top velocity quartile. Price increases had masked the loss. The opportunity list led with restoring those stores.

Service

Analytics, usually followed by a performance dashboard so the view stays current.

See the multi-retailer example

Bring retailer reporting together

Each retailer's portal exports something different; the hardware co-ops report through distributors on their own cadence. Someone spends days every week combining them, and the result is out of date by the time it circulates.

What we do

Map every source, agree one set of definitions, and build a consolidated data model that loads on a schedule. Put dashboards on top that are organised by the decisions each role makes, not by the files they came from.

What you receive

A documented data model, automated weekly loads with checks that flag missing or changed files, role-based dashboards, and a definitions page so "sales" means the same thing in every meeting.

Illustrative scenario

Two home center portals, a co-op distributor report, a farm retailer's weekly file and one shared spreadsheet with 31 tabs. Replaced with a scheduled load into a single model and a dashboard the sales team opens before every call. The two days a week spent pasting were redirected to working the opportunity list.

Service

Business Intelligence, with AI Services where parts of the load or the commentary can be automated.

Explore an Interactive Dashboard

Improve inventory and availability visibility

You find out about out-of-stocks when the merchant mentions them, or when the fill-rate penalty arrives. By then the lost sales are gone and the relationship has taken the hit.

What we do

Combine store-level inventory, sales and shipment data into an availability view: which stores and SKUs are out, for how long, how much it is costing, and whether the cause sits with your supply, the retailer's replenishment, or phantom inventory on the shelf.

What you receive

A store × SKU availability dashboard with estimated lost sales, weekly exception lists sized for a sales team to act on, and the evidence to take replenishment issues to the retailer with specifics rather than complaints.

Illustrative scenario

Seven farm-channel stores in one region showed a three-week in-stock collapse on a core plumbing SKU. The sales data was still showing units, which hid it from the weekly report. The availability view surfaced it in week one of the next occurrence, with the lost-sales estimate attached.

Service

Analytics and Business Intelligence together.

See the availability example

Prepare stronger merchant conversations

The line review is in three weeks. The deck is forty slides of your own data, organised around your categories, and the merchant will ask about theirs.

What we do

Rebuild the argument around the merchant's decision: category growth, their share of it, the productivity of your items against the shelf they occupy, and what you are proposing. Every number sourced, every claim one the merchant's own data will support.

What you receive

A short evidence pack with the comparison periods and definitions the retailer uses, an account-planning view you can refresh for every review, and a working session to rehearse the questions you will be asked.

Illustrative scenario

A paint-sundries supplier wanted two additional facings. The pack showed their item turning 1.8× the category average per facing while two competing items turned below 0.7×. The ask changed from "more space" to "reallocate space the category is wasting."

Service

Analytics, often becoming a standing quarterly preparation under ongoing support.

How we prepare account reviews

Put AI to work in our business

You can see that AI should be taking work off your team's plate. You do not want to hire a specialist to find out how, and you are wary of tools that promise more than they do.

What we do

Start from the work, not the technology. Identify the reporting, reconciliation and administrative tasks that follow a pattern, automate those with a person still accountable for the result, and give your team assistants grounded in your own data with clear limits on what they will and will not do.

What you receive

Working automations for specific tasks, a short list of what we recommended against and why, documentation your team can maintain, and enablement sessions so the people doing the work can extend it.

Illustrative scenario

A monthly retailer scorecard that took a commercial manager a full day now drafts itself from the data model, flags exceptions against agreed thresholds, and leaves the manager to write the two paragraphs of commentary that need judgment.

Service

AI Services, built on the data foundation from Business Intelligence.

Ask a Question about AI See the reporting automation example

Your problem may not fit neatly in one of five boxes. That is usually where the interesting work is.

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