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What set of decisions does a retailer need to get right to succeed?

We’ve already seen a turbulent start to the 2024 retail landscape. The likes of Card Factory, Superdrug and Ocado have reported strong Christmas sales, whilst Sweaty Betty and Burberry have posted a loss and lower profit guidance respectively. It’s more important than ever, then, for retailers to focus on great commercial retail decision making.

The promised land for retailers.

At its most basic, retail is all about deciding what products to sell; what price to charge; how to ensure there’s enough stock, and who to sell to and how.

But we all know it’s not that simple. To make effective decisions in these areas, retailers have to sift through reams upon reams of data. That’s why retail decision making driven by data science and AI – rather than instinct and guesswork – is the promised land. For example:

  • Retailers harnessing the power of data for personalised experiences see a sales uplift of 30-50%, according to McKinsey & Company.
  • Employing advanced techniques like AI for price optimisation provides up to 5% margin benefits, as reported by Bain & Company.
  • By using data to remove non-incremental SKUs from product assortments, retailers see up to a 28% profit increase, as per a case study by Accenture.

Retail decisions are interlinked.

As we see it, success in retail boils down to making great decisions in four key business areas:

  • Product assortment;
  • Pricing;
  • Demand forecasting;
  • Marketing, customer loyalty and media.

Critically, these areas are interlinked. For example, changing a product price or removing a product line impacts customer demand. This affects marketing decisions – products in high demand likely need less marketing investment, whereas products struggling to win demand require a boost. Put together, making great decisions in all these areas strengthens customer loyalty and increases profitability.

Retail decisions are interlinked across product assortment, pricing, demand forecasting, and marketing, customer loyalty and media.

So what does this mean in practice? Well, retailers should be considering the wider impact of commercial retail decision making. This is only possible with the support of machine learning and AI.

Generative AI is only the beginning.

Although generative AI has captured public imagination, machine learning is another branch of AI that’s highly applicable to retail. It analyses large volumes of data and identifies patterns, then makes accurate forecasts and recommendations.

Machine learning can be used to predict demand and address stock issues, whilst analysing pricing data and forecasting sales – ensuring better sales performance, inventory management and understanding of customer behaviour. Put together, this helps increase customer satisfaction.

Building foundational insight is the first step.

So where can retailers start with connected retail decision making? There’re no shortcuts, so it’s crucial to start by creating foundational insight:

  • Affinity: Do you understand the relationship between products and customers? What are your customers’ browsing and buying habits? Which products do they substitute for each other – and which do they treat as complementary by purchasing them together? Understanding affinity across your product range is crucial for connected decision making.
  • Attributes: Have you tagged each of your products with a comprehensive list of attributes? Taking the time to build attributes from metadata and review data is vital for a deep understand of customers and their behaviour.
  • Customer need states: Do you understand which clusters of products meet a particular customer need? How are consumers grouping products into missions or decision mindsets? Recognising how customers shop helps inform product assortment, pricing, demand and marketing decisions.
  • Segments: Are your customer segments based on characteristics, behaviours and preferences alone? Or have you also created macro, micro and individual segments based on product attributes and customer missions?

Put together, each layer of foundational insight drives more intelligent decision making and joins the dots between assortment, pricing, marketing and demand.

Our decision intelligence platform isn’t one-size-fits-all. It helps retailers create core foundational insight based on their data, then powers connected commercial retail decision making. Sound good? Book a demo to find out how we can help your retail business.

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