Riparian — Retail Intelligence Riparian

Services

Five services, one commercial question: what should we do next?

Analytics finds the answer. Business Intelligence keeps it in front of the people who act on it. AI Services remove the manual work around both. And for companies that would rather own the capability than rent it, we help stand up the analytics, BI or AI function itself. Each can start on its own; together they compound.

Every engagement is scoped to your retailers, data and team. Start with a conversation, not a package.

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Analytics

Retail performance analysis, opportunity identification and commercial decision support.

Leads most engagements. The output is a decision you can defend, not a report you file. Built on the point-of-sale, category and industry data that home center, hardware, farm and B2B retailers make available to their suppliers.

The problem

Suppliers have more data than ever and less time to think about it. Portal reports describe what happened last week; they do not explain whether it was distribution, velocity, price, mix or promotion, and they do not say what is worth doing about it. Decisions default to the loudest account or the most recent complaint.

What you receive

  • Sales driver analysis by retailer, category and SKU: stores selling, units per store, price and mix, promotion, seasonality.
  • Opportunity and risk register, ranked by value, each with the evidence and the assumption behind the estimate.
  • SKU and store productivity: which items earn their space, which stores under-trade their potential, and where distribution should move.
  • Promotion evaluation: lift, post-promotion dip, margin effect and whether the mechanic is worth repeating.
  • Account review preparation: evidence packs built around the merchant's decision, in the retailer's own definitions and periods.
  • A written readout in plain commercial language: what matters, why, and the recommended action with its confidence stated.

In practice

We begin with your questions and your data as it is, including the messy parts. Analysis is iterative and reviewed with you; findings are tested against your knowledge of the accounts before they become recommendations. Where the data cannot support a confident answer, we say so and tell you what would.

Example

Illustrative scenario
A mid-size pet supplies brand sold through a national home center, a hardware co-op and a farm retailer. Total sales were growing 4%, which looked healthy. The driver analysis showed one retailer growing 19% on new distribution while the other two were losing velocity in stores they already had. The 4% was hiding a weakening base. The recommendation redirected trade spend from the growing account, which was growing anyway, to in-store execution at the two that were slipping, with a six-week test design to prove it.

Business Intelligence

Dashboards, reporting systems, data consolidation and accessible business visibility.

Turns a one-time analysis into a view your team opens every week. Built on tools you already own where possible.

The problem

Retailer data lives in portals, emailed files and a distributor's extranet, each with its own layout and definitions. Someone combines them by hand. The combined view is late, fragile, and understood by one person. When that person is on holiday, the business is flying blind.

What you receive

  • Consolidated data model across retailer POS portals, hardware co-op and distributor reporting, and your own ERP or order data, with one agreed set of definitions. Two-step distribution is handled as a first-class case, not an afterthought.
  • Automated loading on a schedule, with checks that flag missing, late or changed files before anyone reports from them.
  • Role-based dashboards: an owner's weekly view, an account manager's retailer view, an operations availability view. Each organised around the decisions that role makes.
  • Scheduled reporting that refreshes itself and distributes to the right people.
  • Documentation: a definitions page, a source map, and a runbook so the system outlasts any one person.

In practice

We work in the BI platform that fits your business and budget, including tools you may already license through Microsoft 365, and we build dashboards following a consistent design: the question first, then the evidence, then the implication, with periods, comparisons and sources visible rather than hidden in tooltips. Mobile views are designed, not left to chance, because account managers read them in car parks before meetings.

Example

Illustrative scenario
A hardware supplier with two home center portals, a co-op's distributor feed and a farm retailer's weekly file had one analyst spending two days a week on a 31-tab spreadsheet. We built a scheduled load into a single model, a weekly owner's view with five KPIs and their comparison basis, and a retailer view the sales team opens before every call. The analyst's two days moved to the opportunity register. The first month surfaced a retailer that had quietly stopped ordering two SKUs in one region.

AI Services

Practical implementation, workflow automation and team enablement.

Extends what Analytics and BI can do. We implement specific, bounded uses with a person accountable for each result.

The problem

Your team spends its week on reporting, reconciliation, portal downloads, retailer forms and chasing data. You know AI should help. You do not want to build a specialist team to find out how, and you have seen tools that produce confident nonsense.

What you receive

  • Workflow automation for reporting, reconciliation, retailer paperwork and data preparation, with checks and a human sign-off where the stakes warrant it.
  • Drafting assistants for scorecards, account summaries and merchant preparation, grounded in your data model so they cite numbers rather than invent them.
  • Question-answering over your own documents and data: trading terms, promotion calendars, planograms, with clear limits on scope.
  • A recommendation of what not to automate, and why.
  • Team enablement: working sessions with the people doing the work, so they can use and extend what we build.

In practice

We begin with a map of where your team's hours go, pick the tasks that follow a pattern, and implement in small steps you can see working. Every automated output states what it drew on. We use established platforms and keep your data under your control; nothing is trained on your data without your decision. Where a task needs judgment, the automation prepares and a person decides.

Example

Illustrative scenario
A commercial manager spent one day a month assembling a retailer scorecard: downloading six files, reconciling two, pasting charts, writing commentary. Now the data model refreshes on schedule, exceptions are flagged against thresholds the manager set, and a first draft of the commentary is produced with each claim linked to its figure. The manager spends an hour reviewing and writing the two paragraphs that need judgment. The scorecard goes out a week earlier.

Enterprise AI Programs

Standing up AI across an organization: strategy, governance, rollout and adoption.

A separate service from AI implementation. This is for leadership teams that want AI working across sales, account management, operations and finance, with the controls to trust it, and want to own the result.

The problem

Everyone in the business is experimenting with AI on their own, with their own accounts, their own prompts and no agreed rules about what data can go where. Leadership wants the productivity without the exposure, and a way to tell which uses are paying back. Pilots multiply; nothing becomes standard practice.

What you receive

  • An AI strategy tied to commercial priorities: the handful of uses worth doing first, ranked by value, risk and readiness, and the ones to leave alone.
  • Platform and tooling selection that fits what you already license, with data handling, security and cost reviewed before anything is rolled out.
  • Governance that people can follow: an acceptable-use policy, data classification, review and sign-off rules, and a named owner for each use.
  • A phased rollout plan by function, with role-specific playbooks, prompt and workflow libraries, and a champions network inside each team.
  • Training and adoption: hands-on sessions built around each team's real work, not generic demonstrations, with adoption and outcome measures reported monthly.
  • A hand-off so the program runs without us: operating cadence, documentation and the internal roles that keep it current.

In practice

This is work we have led inside an organization, not only advised on. We begin with the leadership team's priorities and a candid look at the data and tools in place, then sequence the rollout so early wins are visible and governance arrives before scale does. Every phase has a measure, and we report the ones that disappoint as plainly as the ones that land.

Example

Illustrative scenario
A multi-brand sales organization had a dozen people using consumer AI tools on retailer data with no shared rules. The program set a policy and a single approved platform, started with three uses the sales team asked for (line review preparation, scorecard commentary and retailer correspondence), trained each team on its own work, and measured hours returned per role. By the second phase, account managers were opening the tools as part of weekly preparation rather than as an experiment, and leadership had a monthly view of what was in use and what it was worth.

Capability Building

Consulting for companies that want to stand up their own analytics, BI or AI function.

You would rather own the capability than outsource it. We help you build it well the first time, then step back.

The problem

The business has decided to build an in-house analytics or BI team, or to bring AI capability inside, and nobody has done it before. The risks are familiar: the wrong first hire, a platform chosen for the demo rather than the data, dashboards nobody opens, and eighteen months spent learning what an experienced practitioner already knows.

What you receive

  • A capability roadmap: what the function should do in its first year, in what order, and what it should not attempt yet.
  • Architecture and tool selection for your data sources, retailers and budget, with the trade-offs written down so the decision survives staff changes.
  • Hiring support: role definitions, the skills that matter for supplier-side retail analytics, interview exercises, and help assessing candidates.
  • Operating model and standards: definitions, report design conventions, a request intake process and a release rhythm, so the function serves the business rather than its own backlog.
  • Working alongside your team through the first builds, so the standards are learned by doing rather than read in a document.
  • A planned exit, with an optional advisory retainer for the questions that come up after we leave.

In practice

We start by agreeing what the function is for, in the words of the people who will depend on it. Then we design the smallest version that proves its value, help you hire or assign the people to run it, and build the first deliverables with them. Our measure of success is that you no longer need us for the day-to-day work.

Example

Illustrative scenario
A growing supplier with four retail accounts decided to hire its first analyst rather than keep buying reports. Before the hire, we defined the role, set up the data model and reporting standards, and wrote the first-year roadmap. The analyst joined into a working system with a clear remit, delivered the first owner's dashboard in the first month, and six months later the company was running its own line review preparation with a short monthly advisory call as the only outside involvement.

How they fit together

Analytics answers. BI keeps the answer current. AI removes the work around both. And when you want to own the capability, we build it with you.

A typical first engagement establishes the data foundation and one or two decision views, then adds analysis and automation where they pay back fastest. The order depends on where your pain is.

The questions we help answer

Every service maps back to one of these. If your question is not here, ask it anyway.

  • What is driving our sales?
  • Where are we missing opportunities?
  • How can we combine multiple retailers' data into one useful view?
  • Where are inventory and availability limiting growth?
  • How can we prepare stronger merchant conversations?
  • What reporting and administrative work can we automate?
  • How can we implement AI without building an internal specialist team?
  • How do we roll out AI across the whole organization safely, and know what it is worth?
  • We want to build our own analytics, BI or AI function. Where do we start?

Start with a 30-minute conversation about your business.

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