Retailers have never had more data, or less clarity on what to do with it. Every retailer knows the feeling, endless dashboards, data streams, and weekly reports that don’t make decisions any easier. Despite the millions being spent on analytics, retail growth remains stubbornly flat across most markets.
In the UK, retail footfall is down by over 10% since 2019, yet spend per visit is up nearly 30%. Customers are visiting fewer stores, but spending more when they do, a paradox that reveals how traditional retail metrics no longer tell the full story.
It’s a perfect storm. Leaders are under pressure to grow in saturated markets while proving every investment to the board. But the more data they collect, the harder it becomes to separate what matters from what’s just noise. This widening disconnect between the data retailers have and the actionable insights they need is what we call the Retail Growth Gap.
Retailers aren’t short on data. They’re short on direction.
The challenge isn’t just visibility, it’s velocity. The market moves faster than quarterly reviews or annual network plans can keep up. Relying on reactive analytics to describe what’s already happened doesn’t equip growth teams to predict what’s coming next.
To close that gap, retailers need proactive analytics, models that reveal what’s next, not just what was. They need tools that can interpret complex market signals in real time, pinpoint profitable opportunities, and make those insights usable at board level. That’s exactly where new retail technologies like Ikano Insight’s Area Prioritisation Engine are making a measurable difference.
What retail leaders are really looking for
Retail growth, strategy, and property teams are chasing precision. Whether you’re a Head of Insights, an Omnichannel Director, or a Store Network Planner, your reality is the same, decisions need to be faster, smarter, and watertight.
From recent studies into retail analytics trends, five common goals have emerged among market leaders:
- Predictive confidence: The ability to forecast which locations, categories, or channels will perform before others do. This means more than hindsight, it’s about using predictive retail analytics and AI retail insights to stay one step ahead.
- An integrated omni-view: Connecting physical, digital, and local context through omnichannel analytics. Whether analysing a city centre flagship or a suburban click-and-collect point, retailers need consistent visibility across channels.
- Agility: Responding in days, not quarters, to shifts in customer demand, mobility, and spend. As one industry report observed, the best retailers “fight on a daily basis to maintain their advantage.”
- ROI clarity: Being able to explain decisions to boards and investors through scenario modelling and store network optimisation.
- Deeper customer context: Understanding not just who shops where, but why. By linking customer segmentation in retail with behaviour and mission data, retailers can shape more relevant experiences, from local product mixes to hyper-targeted promotions.
These aren’t wish-list items, they’re business necessities. But the truth is, most traditional data platforms aren’t designed to deliver them.
Where current providers fall short
Most legacy analytics tools still rely on historical and demographic data to define catchment areas and rank retail centres. While valuable for long-term trend tracking, they struggle to reflect the nuance of modern omnichannel retail, where physical and digital sales influence one another daily.
The issue isn’t the data itself. It’s that it stops short of foresight. Reports are static snapshots that need manual interpretation, forcing growth teams to bridge the gap between analysis and action. This reactive approach simply doesn’t match today’s pace of change. Customer behaviour evolves faster than reporting cycles, while footfall, sales, and loyalty are shaped by external factors like weather, work patterns, and even local events.
Retailers need analytics that help them plan tomorrow, not explain yesterday. They need data that tells them where to invest marketing spend, where to open or close stores, and which channels to double down on, with confidence backed by hard evidence.
That’s where tools like Ikano Insight’s Area Prioritisation Engine and Location Analytics solution stand apart. These platforms use retail location intelligence and AI-powered modelling to show exactly where the next growth opportunities lie, down to specific postcodes or store clusters, enabling faster, better-informed decisions.
Demand data and supply chain optimisation
Beyond the shop floor, demand data has an important role to play in supply chain optimisation. Retailers continually balance the trade-off between overstocking cost and stockout risk. Both are tricky, empty shelves infuriate shoppers and lose sales, while excess stock ties up money and threatens waste.
By placing product placement in conjunction with local demand signals, stores can more accurately forecast demand and take action to adjust the amount of stock based on that. As an example, a drugstore chain may find that cough syrup sells more rapidly in some markets and in some months, and vitamins sell better in other markets. By feeding those inputs into the supply chain, inventory can be redirected to the most critical markets.
This not only supports availability but also reduces waste and supports sustainability goals. Ikano Insight’s value modelling allows retailers to quantify these impacts, illustrating how smarter shelf-level decisions cascade into the entire supply chain.
The five unmet needs in modern retail growth
Despite vast investment in analytics, five key gaps still hold many retailers back. Bridging these gaps isn’t about adding more data, it’s about making it work harder.
Retrospective > predictive analytics
Traditional reports tell you what happened. Predictive analytics tell you what’s next. By blending AI retail insights and mobility data, retailers can forecast high-potential zones and anticipate churn before it impacts performance. This helps prevent underperforming locations from quietly eroding profit.
Unified omnichannel view
Most analytics platforms still treat in-store and online as separate worlds. But consumers don’t. Unified omnichannel retail analytics reveals how each channel influences the other, whether a social ad boosts in-store traffic, or an online search leads to an offline purchase. Integrating ecommerce integrations with store data makes this possible.
ROI-ready storytelling
Executives don’t need another spreadsheet. They need a narrative they can trust. Modern analytics tools simplify scenario analysis, allowing leaders to visualise outcomes before committing capital. This transforms complex datasets into stories that boards understand and back.
Real-time local intelligence
Footfall and spending aren’t static, they shift daily, even hourly. Real-time mobility and payment data offer an up-to-date picture of how people interact with a place. This isn’t just about knowing where shoppers are, it’s about knowing why they’re there. With tools like Ikano’s Value Modelling, you can measure how changes in competition, pricing, or demographics affect turnover in real time.
Dynamic segmentation
Static personas no longer cut it. Dynamic, AI-driven segmentation evolves with customer behaviour, helping marketers and merchandisers pivot their messaging instantly. This enables recommendation analysis that feels personalised, and relevant, across every channel.
These gaps explain why so many growth teams find themselves working harder for less certainty. To stay competitive, analytics needs to evolve from insight delivery to insight activation.
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Closing the gap: What next-gen retail analytics looks like
So, what does the future of analytics actually look like? It’s fast, integrated, and predictive, combining machine learning, behavioural data, and human decision-making into one adaptive ecosystem.
At Ikano Insight, we call this approach connected intelligence. It blends:
- Predictive modelling that runs continuous “what if?” tests on new locations, store formats, and marketing campaigns.
- Real-time mobility and spend data integration, pulling in insights from multiple sources to reveal true catchment behaviour.
- Unified dashboards that align store, online, and regional data, removing silos and offering full transparency across departments.
- Automated insight-to-action workflows, which reduce manual reporting and let teams focus on strategy, not spreadsheets.
- Scenario analysis tools for agile planning, so if market conditions change, so can your strategy.
The result? Faster, smarter decisions and a measurable uplift in ROI. By embedding retail location intelligence into your decision framework, you’re not just tracking performance, you’re shaping it. This shift from passive data analysis to active data direction is the difference between reacting to the market and leading it.
A smarter, more agile alternative
For too long, data has been treated as the end product rather than the starting point. But growth happens when insights are translated into decisive, confident action. Retailers need partners who understand that data alone doesn’t drive transformation, it’s how you apply it.
Ikano Insight’s analytics ecosystem, powered by tools like the Area Prioritisation Engine, Profiling and Segmentation, and Product Recommendation Engines, turns complex market signals into clear direction. By blending behavioural, transactional, and geographic data, they help leaders align strategy, store design, and marketing around what will drive true value next.
Rethinking the role of data
Retailers have reached a tipping point. They don’t need more dashboards, they need decisions. They don’t need another report, they need a reason to act.
The real growth gap isn’t in the data, it’s in the distance between insight and action. Closing it requires a mindset shift, from reactive measurement to predictive movement.
If your analytics still explain yesterday’s sales, it’s time to start forecasting tomorrow’s growth.
Explore the Area Prioritisation Engine and see how Ikano Insight can help you close the retail growth gap with precision, speed, and confidence.
Written by Matt Craddock
Global Head of Data & Analytics
Matt is a data science leader with expertise in heading up global teams that deliver game-changing solutions. He’s passionate about solving real-world problems with data-driven decisions, and combines hands-on technical skill with commercial insight to help businesses translate complex data into impactful outcomes.
