Digital Maturity Diagnostic
For Chief Digital Officers · Hypertrade
Is your tech stack producing decisions — or just dashboards?
Most retail digital transformations end at the reporting layer. The platform is live, the data is flowing, and the commercial team is still making decisions the same way they did before the investment was made. Score your organisation across six dimensions and find out exactly where the gap is.
~74%
of retail tech investments stall at the dashboard layer
12 hrs
saved per category manager per week at full deployment
94%
decision card actioning rate within SLA
Click or drag each track to score your organisation from 1 (data collected, decisions still manual) to 5 (commercial decisions running automatically from a validated retail knowledge layer). Your diagnosis updates instantly.
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Overall score / 5
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Digital maturity stage
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Decision velocity gap
Dimension profile
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Score all six dimensions above to generate your strategic diagnosis.
Dashboard-layer deployment
Decision-layer deployment
The dashboard paradox. Retail digital transformation investments typically produce excellent reporting infrastructure. Data is unified, dashboards are built, and the commercial team has visibility it never had before. Then the Monday morning meeting happens exactly as it always did — someone pulls the dashboard, the team discusses what it shows, and decisions are made through the same combination of experience, supplier pressure, and precedent that governed the process before the platform existed. The platform has changed the format of the briefing document. It has not changed the decision. The gap between a reporting layer and a decision layer is not a data problem and it is not a technology problem. It is a retail knowledge problem: the system knows what happened, but it does not know what it means — because no one has encoded the logic of how shoppers switch, how categories are structured, and how range and promotion decisions interact with each other. That encoded knowledge is what separates a commercial decision engine from an expensive dashboard.
Four structural phases. The data infrastructure already built is the foundation — not the destination. The retail knowledge layer is the engine. Each phase moves the investment closer to the commercial outcome it was always supposed to produce.
The honest caveat. The transition from dashboard to decision engine is not primarily a technology project. Ariane can be configured against an existing data stack in weeks. The constraint is twofold: building the retail knowledge layer takes domain expertise that cannot be replicated quickly in-house — it is the encoded product of decades of category management experience across dozens of markets. And getting commercial teams to trust and act on a recommendation they did not build takes organisational change management that runs in parallel with the technical deployment and takes longer. Plan for both simultaneously, or the decision engine will generate correct recommendations that nobody actions.
The most important thing a CDO can do after completing this diagnostic is not read a proposal. It is ask the decision engine a question it cannot currently answer — and watch it answer.
Questions your current platform cannot answer. Ariane can.
"Which three SKUs in my Beverages category are most at risk of creating a shopper need gap if we proceed with the planned delist — and what is the basket value we would lose per week?"
"Which promotions on this week's candidate list are likely to attract deal-seeking shoppers rather than our high-value basket-building segment — and what does running them anyway cost in true net ROI?"
"Across our Tops Daily format, which categories have a SKU efficiency ratio below 0.5 and a core assortment distribution rate below 70% simultaneously — and what is the combined margin consequence?"
"If we apply one-in-one-out governance to NPD launches in this category starting next cycle, which existing SKUs are the displacement candidates — and which of those have a substitution risk score that makes displacement commercially safe?"
These questions require retail logic, not just data. They require a system that knows what a basket builder is, what a shopper decision hierarchy looks like, what substitution risk means in a specific category context, and how range and promotion decisions interact with each other at store level. That knowledge layer is what Ariane brings — and it is what no amount of dashboard development will produce without it.
Option A — the right first step
See it answer on your data
We connect Ariane's decision engine to your existing EPOS and loyalty data and run it live in a 45-minute session. You bring one category question you cannot currently answer from your platform. We show you what the answer looks like when the system has retail logic behind it — scored decisions, financial value attached, reasoning visible. Not a generic walkthrough. Your data. Your category. Your question.
45 minutes · No system integration · Existing export format · One category
Book the session →Option B — architecture review
Map the knowledge layer gap
A structured conversation that maps your current data architecture against all six dimensions of this diagnostic — and identifies specifically which layer is missing between your existing platform and a commercial decision engine. Particular focus on the retail logic and knowledge structure dimension: what exists, what is missing, and what it would take to build it versus connect to it.
For CDOs and digital transformation leaders · hypertrade.ai
Request a review →What Hypertrade connects to your stack
1
A retail knowledge layer built over 30 years. Ariane does not just process your transaction data — it reasons over it using an encoded understanding of retail category logic: how shoppers make decisions within a category, how SKUs relate to each other through substitution and complementarity, how range and promotion decisions interact, and how store-level demand patterns differ from format-level averages. This layer is what makes Ariane's outputs decisions rather than correlations — and it is what cannot be replicated by an in-house data team without the domain expertise already inside it.
2
Ariane answers the question your category manager is actually asking. Not "what happened to this SKU last week" — that is a dashboard question. But "what should I do about it, why, and what is it worth if I act this week versus next month?" The decision engine produces a recommendation, a financial value, a rationale, and an urgency score. The category manager reviews and approves. The system handles the logic.
3
94% decision card actioning rate within SLA across live deployments. Cards are actioned because they arrive with a financial value, a named owner, a deadline, and a reasoning trail the category manager can interrogate. Not because the commercial team was told to use the system — because the output is specific enough and credible enough that acting on it is easier than ignoring it.
4
Deployed across 15+ markets. The integration patterns, data quality challenges, retail knowledge configuration requirements, and organisational change management involved in moving from dashboard to decision layer are not theoretical for Hypertrade. Every deployment has navigated the same sequence this diagnostic describes — and produced a commercial ROI the CDO could present to their board.
