ASBIS already owns the two things this initiative needs: decades of its own commercial data, and a working intelligence platform that is already in production. What it does not yet have is the layer that turns those into forward decisions. This brief asks for one thing — a bounded pilot to test whether it does.
The platform is not a concept. It is operational today, ingesting a full market panel automatically and surfacing signals that currently take analysts days to assemble. Continuing does not start something new — it points an existing, working capability at ASBIS's own decisions.
The cost and time to build and validate this kind of capability has fallen sharply — what required several teams and a year can now be tested in weeks. A working prototype already exists. Every distributor sees similar market data; advantage goes to whoever converts it into decisions first, while testing is still cheap.
It is additive. It sits on top of IT4Profit and Power BI, changes no operational system, and is proven first in a 60-day pilot with a capped budget and an explicit stop point. If the pilot fails its criteria, it ends — the only cost is the pilot.
The decisions in scope — how much to buy, which brands and categories to back, where margin leaks — are the decisions that move inventory, margin and growth. On high-value categories, even a modest improvement in decision quality is material.
Approve a 60-day Customer Opportunity Intelligence pilot — one customer segment, one sales team — with success criteria agreed in advance. Judge it on evidence at day 60 before any larger commitment.
This initiative is past the idea stage. The following components exist and work today.
| Component | What it is | Status |
|---|---|---|
| Market-intelligence platform | Automatically ingests a full market panel — ~1.2M SKUs, ~30K brands, 50 categories, 18 months of history — and turns it into self-serve views. Work that took an analyst days now runs in under a minute. | Live |
| Normalized data layer | A single structured model — brand · SKU · category · channel · time — that any new source plugs into. The hard, reusable part, already done. | Live |
| Opportunity-discovery concepts | Working analyses that flag channel shifts, pricing / premiumization trends and brand concentration — concrete examples of signals surfaced automatically. | Working |
| Attribute-intelligence research | Early work reading the market by product characteristics (e.g. specification adoption), not just by brand — the basis for understanding why markets move. | Research |
| Executive briefing portal | decision.d1s.app — the access-controlled, bilingual briefing you reviewed. | Live |
In plain terms: the engine runs. Today it runs on external market data; connecting it to ASBIS's own internal data is exactly what the pilot does.
Not a technology problem. These are the recurring commercial questions ASBIS answers every quarter — today mostly on experience and backward-looking reports:
The constraint is no longer collecting information or reporting it. It is converting what we already know into the next decision — reliably, and earlier than competitors.
One framework answers most of the overlap concern. The business has three layers; ASBIS is strong in the first two and missing the third.
Power BI reports what happened. This layer recommends what to do next and ranks opportunities — combining ASBIS internal data with external market signal that reporting tools do not hold. Different job, sitting one level up.
Success is measured in better decisions, not delivered reports. A reporting project ends when the dashboard ships; this is judged only by whether a buyer or category manager made a better call.
It reads permissioned, read-only copies of data. It writes nothing back, changes no operational process, and introduces no new system of record. IT4Profit remains the operational backbone, untouched.
Deliberately small and measurable: prove the principle with one customer segment and one sales team, end to end, in 60 days. The first output of the Opportunity Engine — Customer Opportunity Intelligence: what else we can profitably sell the customers we already have.
| Pilot | Customer Opportunity Intelligence |
| Country | One country — chosen for the cleanest customer data and a willing sales director |
| Customer segment | One segment — e.g. mid-tier resellers, where whitespace is largest and reps have capacity to act |
| Sales team | One team (~6–12 reps), under one sales director |
| Duration | 60 days |
| Focus | Three play types only: cross-sell, customer whitespace versus comparable accounts, and reactivation of lapsing accounts. |
| Goal | Validate whether opportunity discovery — on ASBIS's own customer and sales data — produces better commercial decisions, measured in incremental gross profit, than current practice. |
| Scope | Use existing internal sales, customer and product data to produce ranked per-rep opportunity lists and a back-test of recent decisions. Internal data only — no external market panel required for the pilot. |
| Method | Test versus control: a matched set of reps/accounts receives recommendations, a comparable set does not, so the lift is cleanly attributable. |
| Resources | One initiative lead (owner); part-time, controlled data-access support from IT; an executive sponsor; read-only permissioned data; modest tooling and compute. |
| Cost | A defined, capped envelope — a fraction of a traditional BI engagement — to be finalised with Finance. Components: lead time, limited data engineering, tooling. |
If the criteria are not met at day 60, the pilot ends. The cost is bounded and there is no further commitment — by design.
The risks are real and worth stating plainly. None of them require accepting them unmanaged.
| Area | Position & mitigation |
|---|---|
| Data ownership | ASBIS owns all data. The layer reads permissioned, read-only copies and writes nothing back. No new system of record is created. |
| Security | Data remains within an ASBIS-controlled environment; external market data is used under its licence terms; everything aligns to ASBIS's existing data policy. |
| Access control | Role-based, least-privilege access. Pilot data is scoped to the one segment and one team only — nothing wider is touched. |
| Continuity & key-person risk | The real risk. Speed has come from a small, focused build, which concentrates knowledge in few hands. Mitigation: document the data model and method; pair a second person during the pilot; keep all artefacts and IP in ASBIS-owned repositories; make an explicit ownership-handover plan a condition of scaling. We do not pretend this risk is zero — we contain it. |
This brief deliberately contains no invented revenue projections. At pilot stage the honest position is that we measure decision quality and opportunity size, and build a bankable business case on that evidence — not on a forecast.
| Value lever | How it would be measured |
|---|---|
| Cross-sell & customer expansion | Incremental gross profit from selling more to existing customers; euro-sized share-of-wallet gaps closed. |
| Reactivation | Gross profit recovered from lapsing or declining accounts brought back into regular purchasing. |
| Better portfolio decisions | Brand/category onboarding and exit hit-rate; reduction in dead or slow-moving SKUs. |
| Inventory risk avoided | Overstock and stockout exposure — before versus with system-informed decisions. |
| Faster validation | Time and cost to test a new commercial initiative, before versus after. |
Value = the improvement in each decision × the money at stake in that decision, summed across the decisions in scope. The pilot establishes those improvements on a small, measurable scope — so a full business case rests on evidence, not on projection.
Because the cost and time to build and test this has collapsed, a working prototype already exists, and the underlying asset — our data — is already paid for. The advantage is moving while testing is cheap.
Because it has been built by someone who understands the distribution business and can also execute — a combination that is rare. The pilot deliberately adds a second person and full documentation to reduce dependence on any individual.
Nothing operational. Because it is additive, IT4Profit and Power BI are untouched; stopping costs only the capped pilot spend. There is no migration to unwind.
By adding data sources and categories onto the same normalized model — each addition compounds value without rebuilding. Scaling is gated on pilot evidence and a continuity / ownership plan, not assumed.
It sits above them and consumes their outputs. It is the Opportunities layer — it replaces neither the operations system nor the reporting standard.
The day-60 read-out: a back-test with quantified deltas, a sized opportunity list, and at least one real decision changed. No larger investment is requested — or warranted — without it.
This is a narrow, low-risk request: approve the pilot, and judge it on evidence in 60 days.