“Most retailers are overwhelmed with vast data volumes offering little or no recommendations on what it means to them.” says Predyktable co-founder and CEO Phillip Sewell
Retailers navigating a climate of perpetual change are expected to make high-stakes, business-critical decisions daily. But with so many economic, environmental, and consumer variables in play, even well-resourced businesses are often gambling on outcomes they can’t predict.
Despite billions being spent on data platforms, reports, and dashboards, too many retail professionals are left staring at the past—when what they really need is a reliable view of the future.
The Problem with Traditional Tools
For years, business intelligence and data analytics have been positioned as the answer to unlocking customer behaviour. But most of these tools rely on historic data and backward-looking insights. They demand too much manual input, fail to account for wider external influences, and leave teams guessing at what to do next.
The result? Huge volumes of siloed data. Few meaningful recommendations. No real clarity on the right forward actions to take.
And without the time or resource to sift through environmental signals—like weather patterns, local demand surges, or shifting consumer sentiment—predictions fall flat.
It’s no surprise that forward-thinking retailers are looking elsewhere.
A Forward-Looking Catalyst: Predictive Analytics
This is where predictive analytics enters the picture. It’s not just a buzzword—it’s a turning point.
Let’s recap the analytics ladder:
- Descriptive analytics – What happened?
- Diagnostic analytics – Why did it happen?
- Predictive analytics – What is likely to happen next?
Predictive analytics uses statistical models, machine learning, and AI to anticipate what’s coming—and suggest the best next move. It enables retailers to act with confidence, not just report after the fact.
Retailers like Marks & Spencer and John Lewis are already applying this kind of thinking. M&S, for example, uses predictive analytics to inform design, buying, and pricing decisions across 50+ product categories—from food to fashion.
Why It Works Better as a Managed Service
Many retailers try to adopt analytics through off-the-shelf platforms or one-off projects. But bolt-on services often lack the depth and ongoing support needed to generate lasting value.
What works better is a fully managed solution—one that combines:
- Deep data science and domain expertise
- Bespoke models tailored to specific retail goals
- Wide-reaching external data inputs
- Continuous learning and adaptation
- Clear recommendations, not just data dumps
The goal isn’t more dashboards. It’s better decisions.
At Predyktable, we build custom models that fuse internal retail data with broader external signals—like weather trends, events, consumer behaviours, and more. Then we train agentic AI to proactively surface patterns, spot risk, and recommend the next best action.
Our models improve over time, continually absorbing new data and learning from outcomes to stay relevant and valuable.
Predictive Analytics + Agentic AI: What’s Possible?
Retailers using this combined approach have seen:
- Reduced costs – Optimising labour and marketing spend with precision
- Increased profits – Better demand forecasts, smarter pricing
- Greater customer retention – Personalised offers based on predictive behaviour
- Faster, smarter decision-making – With foresight, not hindsight
Final Thoughts
Retail doesn’t wait—and neither should your forecasting tools.
Predictive analytics and agentic AI offer retailers a smarter, more resilient way to navigate uncertainty. If your current tools are still focused on what happened last month, it might be time to rethink your strategy.
Predyktable’s fully managed platform gives you insight when it matters—and shows you what to do next.
Let’s talk. We’ll show you how to make better decisions, faster—without complexity or compromise.