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Why smart retailers are checking out prescriptive analytics

Predictive Analytics

For many retailers trying to navigate a climate of perpetual change, making correct business-critical calls on complex environmental, economic and consumer future outcomes is an expensive gamble. This is because current business intelligence and data analytics approaches that support retailers’ decision-making, no longer cut it.  

Traditionally, business intelligence and data analytics have helped retailers understand and influence their customers’ buying habits. But despite billions of pounds spent globally on data platforms, data repositories and a whole stack of tools, most retail professionals still lack the support they need to turn data into forward actions that maximise profits. 

Most retailers are overwhelmed with vast data volumes offering little or no recommendations on what it means to them. Many also use solutions heavily reliant on historic data and insights that aren’t tailored for their specific forward-thinking needs. There’s not enough focus on identifying and understanding wider external data sources. This manual time-consuming research isn’t being done, so the data quality and depth aren’t there to support accurate predictions.  

These approaches aren’t enough to help retailers form a clear future view and know what to do about it.  

To solve these issues, there’s an increasingly sophisticated capability that’s taking data analytics way beyond explanations and predictions. Welcome to ‘prescriptive analytics’, which is widely considered the fourth stage of data analytics’ evolution. Here’s where it sits:  

  1. Descriptive analytics – what happened?
  2. Diagnostic analytics – why did it happen?
  3. Predictive analytics – what might happen in the future?
  4. Prescriptive analytics – what should we do next?

Prescriptive analytics aims to look into the future and then recommend the best course of action.  

Marks & Spencer and John Lewis are among a growing number of retailers using prescriptive analytics to ‘look into the future’ and pre-empt trading conditions in the weeks, months and years ahead. For example, M&S uses this approach to guide its design, buying and pricing decisions across thousands of product lines in 50 categories, including apparel, lingerie, footwear, accessories, food, home and beauty. 

When it comes to outsourcing this capability, many retailers miss out by working with conventional data providers offering prescriptive analytics as a bolt-on, one-off piece of work – with minimal support. I believe that achieving valuable results with prescriptive analytics isn’t possible with off the shelf or piecemeal solutions that treat retailers as commodities.

It’s better opting for a partner offering prescriptive analytics as a fully managed service, backed by retail sector experience. They must focus on understanding a retailer’s business and specific challenges – as these are crucial factors underpinning success. Retailers must also be supported every step of the way, so they keep solving new challenges facing their business. 

The best prescriptive analytics services blend descriptive, diagnostic and predictive insights, with cutting-edge artificial intelligence, machine learning, automation, genuine data science and in-sector consultancy expertise. Everything should be custom built, with each step creating prescription models precisely choreographed to meet retailers’ individual needs.  

This means enhancing internal data, with much wider external insights including global & local trends: weather, travel, localised demand spikes, and more.  Using this high-quality data, data scientists build and optimise prescription models which identify previously elusive, connected, patterns to deliver the most accurate foresight fuelled prescriptions.  

Expect data scientists to continually find new insights to keep models relevant, while learning from the data so they keep delivering value. By uniquely aggregating data from a wider range of external sector sources, models are further enriched to provide greater accuracy and depth to foresight, so the prescription models keep getting better and retailers keep making the most profitable business decisions. 

Here’s an example of how retailers can better gauge brand sentiment through the voice of the customer with prescriptive analytics.  

Current analytics tools offer limited views on what’s being said about brand, as they mainly focus on social media analysis and sample surveys. They don’t show how retailers are perceived through all online and offline touchpoints. By not involving sentiment in predictions, means less accurate, decision-making.  

A better approach is to create machine learning models connected to everywhere that customers are talking about the brand. This means covering online and offline channels, social media platforms, rating & review sites, search engines, contact centre logs, chat bots, blog posts, and more.   

Natural language processing is then used to contextualise each interaction. This means establishing if it’s voiced as a positive, neutral, or negative opinion, if this opinion is shared by anyone else, and if so, what’s the commonality between them?    

Sentiment and activity hotspots are gauged across customer segments, location, and channels. These insights are enhanced with domain models that track behaviours at a national and regional level. This means determining if brand sentiment is part of a wider opinion shift, or if it’s unique to customers – because of a retailer’s actions.   

All this activity generates rich foresight that fuels recommendations on which new products to launch or territories to explore. By also dynamically forecasting demand, enables retailers to optimise the cost of entering new customer segments.   

There’s huge value and so many positive outcomes to be gained with prescriptive analytics as a service, some of these include:  

  • Know which areas to reduce cost: including marketing spend, labour optimisation and demand forecasting.  
  • Understand exactly where to make more money within the most profitable customer segments. 
  • Identify which customers are most likely to convert, then win them over with a hyper-personalised and engaging shopping experiences. 
  • Retain high-value customers by recommending products and services that complement customers’ existing purchase history, interests and lifestyle.  
  • Better optimise pricing at a regional level to maximise the profit opportunities. 

Whatever your size, Predyktable delivers prescriptive analytics as a fully managed service to generate actionable foresight faster, without complexity and compromise. To discuss how we can help your organisation make more profitable decisions, please drop us a line, we’d love a chat.