When a huge retail group operates across a wide portfolio of B2B and B2C brands, digital data accumulates fast. Multiple brands. Multiple websites. Multiple customer journeys. All generating data that only means something if it is tracked consistently and put in front of the people making decisions with it.
For many organisations at this scale, that last part is where things break down. Every dashboard request routes through one analytics team. Numbers vary depending on who pulled them and when. Business users wait. Decisions get made on data nobody fully trusts.
This is how Keyrus helped a major retail group change that.
The starting point: scale without a shared source of truth
The challenge was not a lack of data. It was a lack of consistency in how that data was captured, structured, and distributed across a complex multi-brand environment.
Without a centralised approach to digital analytics, teams were making decisions on numbers they could not fully rely on, or waiting on a single overloaded team to produce every report. Neither situation scales. And at this level of organisational complexity, the gap between what data promises and what it actually delivers becomes a real business problem.
The retail group needed a centralised analytics architecture that could keep pace with the size and diversity of its digital operations, and put reliable, actionable data directly in the hands of the teams who needed it most.
Four objectives that shaped the engagement
Every decision in the project traced back to four clear goals:
Deliver accurate digital data across all touchpoints, consistently and reliably.
Enable self-service analytics so business users could run their own analysis without depending on a specialist for every request.
Provide clear, actionable insights that supported real decisions rather than generating reports nobody acted on.
Ensure reliable tracking of user behaviour as the digital environment around it kept changing.
The sequencing mattered. Accuracy had to come first. Self-service on unreliable data does not solve the problem. It scales it.
What Keyrus built
Keyrus consultants worked directly with the retail group's Digital Analytics team across a set of connected workstreams, each addressing a distinct part of the challenge.
Tracking across every digital channel. Customer interactions, sales, traffic, and campaign performance were tracked and analysed consistently across all digital touchpoints, replacing a fragmented channel-by-channel approach with a single, coherent picture of digital performance.
Structured data for the teams that needed it. Management, product, marketing, and UX and UI teams received data shaped for their specific decisions, not a generic export that everyone had to interpret differently.
Adobe Analytics training for business users. Rather than keeping analytical capability locked inside a specialist team, the project trained business users directly on Adobe Analytics, covering dashboard creation, deeper analysis, and the correct interpretation of data. The goal was not just tool familiarity. It was genuine analytical independence.
A tiered support model. The specialist Digital Analytics team remained available for complex use cases and deeper analytical questions, while standard reporting moved to self-service. This kept expert time focused where it was actually needed, rather than on requests that did not require it.
Continuous improvement of tracking accuracy. Through close collaboration between business, IT, and technical experts, tracking stayed reliable as the sites, campaigns, and customer journeys around it evolved.
Future-proof measurement. The engagement addressed evolving tracking requirements including server-side tracking and cookieless approaches, so measurement remained effective as the wider analytics landscape shifted.
What changed once it was implemented
The results of the engagement were felt across three dimensions.
Continuous insight. Teams moved from periodic reports to an always-on flow of data they could use to steer decisions in real time. The dependency on scheduled reporting cycles disappeared.
Business autonomy. Marketing, product, and management teams could explore data independently for the standard questions that used to require a request and a wait. The analytics team's time was freed for the work that genuinely needed specialist expertise.
Better decisions. With accurate, accessible data in place, teams stopped making assumptions and started acting on real customer behaviour. The gap between data and decision-making closed.
What this project actually demonstrates
Training business users on a tool is not the same as building a self-service analytics capability.
The training worked here because it sat on top of accurate tracking, structured data, and a support model designed to know which questions belong to business users and which still need a specialist. Remove any of those foundations and self-service does not empower teams. It just spreads unreliable numbers further and faster.
This is the kind of project Keyrus builds: the tracking architecture, the governance, and the enablement that make digital analytics something teams can genuinely run themselves. Not a one-off dashboard. Not a training session that gets forgotten in a month. A capability that compounds over time.
AI does not transform businesses. Architected intelligence does.
Still routing every analytics request through one team?
For the solution contact our expret.
Self-service analytics means business users can build their own dashboards, run their own analysis, and correctly interpret the results, without submitting a request to a specialist team for standard reporting needs. It is not just about tool access. It requires accurate underlying data, structured reporting, and the training to use both effectively.
If the underlying tracking is not accurate, giving more people access to it just spreads unreliable numbers faster. Accuracy and structured data need to come first so that self-service produces decisions people can actually trust. The tool is only as useful as the data feeding it.
A specialist team stays focused on complex use cases, deeper analysis, and evolving tracking requirements such as server-side tracking and cookieless measurement. Standard reporting moves to business users. This keeps expert time focused on the questions that genuinely require it.