Why smart retail marketers are switching from business intelligence to prescriptive analytics

Prescriptive Analytics

“It’s time to ditch the rear-view-mirror approach for retail marketing and harness the future of business analytics” says Predyktable co-founder and CPO Andrew Kohter

There’s been no shortage of investment in marketing data and technology solutions in recent years as retailers struggle to understand and influence their customers’ buying habits.

But despite billions of pounds spent globally on data platforms, data repositories and a whole stack of business intelligence (BI) tools, most retail marketers still lack the support they need to turn data into action and maximise sales.

Marketers are being asked to be data scientists, which they didn’t sign up for. They’re not being given the time or tools to transform insight into action and they are often overwhelmed with a deluge of data with little or no recommendations on what it all means to them.

To make the situation worse, the vast majority of marketing teams have been looking in their rear-view mirror for too long – using BI solutions heavily reliant on historic data to help them make critical marketing decisions.

The problem with business intelligence

In the good old days, when the annual rhythm of retail was constant and one year’s trading looked remarkably similar to the next, this retrospective BI approach was fit for purpose.

In the digital age, however, when hot new retail trends shine bright and burn out within weeks, last year’s sales figures are quickly beyond their sell-by date.

Add to this fluid landscape an uptick in ‘black swan’ events which are radically altering consumer behaviour – the pandemic, the war in Ukraine, the cost-of-living crisis and the climate emergency – and it’s clear that backwards-looking BI is falling short.

What is prescriptive analytics?

Retail giants such as Marks & Spencer and John Lewis have known there’s a problem with BI for some time. Rather than relying on historic data to inform their business decisions they 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.

M&S for example has already been using this approach for more than four years 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.

Prescriptive analytics has been called “the future of data analytics” by Forbes magazine, and for good reason. This type of analysis goes beyond explanations and predictions to recommend the best course of action. It is particularly useful driving data-based business decisions and it is also widely considered the fourth stage in the data analytics process:

  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 looks into the future and identifies the best course of action by harnessing technology – such as artificial intelligence (AI), machine learning (ML) and data mining – to reveal consumer behaviour patterns within huge volumes of structured and unstructured data. AI can project these behaviour patterns forward in time to predict outcomes and prescriptive analytics can then make recommendations on the best course of action.

Armed with these recommendations retail marketers can be confident they have the data-driven insights needed to make the best possible business decision.

How can prescriptive analytics help retail marketers?

During tough economic times it’s essential that every penny of marketing spend is optimised. Thanks to prescriptive analytics, marketing teams can make better decisions on media planning and buying and avoid wasting their precious budgets. Teams can gain a clearer understanding of which campaigns are working and what sorts of marketing activities will boost sales conversions in future. Prescriptive analytics can also help retail marketing teams:

  • Identify which prospects are most likely to convert: Prescriptive analytics can play a prominent role in sales through lead scoring, also called lead ranking. By automatically counting customer page views, email interactions, site searches and content engagement, prescriptive analytics can then recommend which prospects to target and what channels will prove most effective.
  • Automate email marketing: Marketers use email automation tools to sort leads into categories based on their motivations, mindsets and intentions. Prescriptive analytics can then be used to identify which sets of email messages are most likely to nurture each group of prospects towards a sale.
  • Retain high-value customers: Prescriptive analytics can recommend products and services that complement customers’ existing purchase history, interests and lifestyle. It can also reveal valuable cross-selling and up-selling opportunities and when best to pursue these opportunities.

This is just a sample of the many ways in which prescriptive analytics can support retail marketers.

What is prescriptive analytics as a service?

Rolling out a new in-house prescriptive analytics solution is likely to be a huge challenge for even the biggest of retailers. For starters, a big talent shortage in the IT sector means that sourcing and recruiting the required data scientists would be prohibitively expensive, and that’s before you even begin platform development work and systems integration.

When it comes to outsourcing, many firms work with conventional data providers which offer prescriptive analytics as a bolt-on, one-off piece of work with minimal support.

Working with a partner which delivers a full-service solution is often a faster, easier to implement, more accurate and far more cost-effective option for retail marketing teams.

Market-leading PAaaS providers do more than provide a powerful prescriptive analytics platform and data, they provide rich insights, recommend when these insights should be put into action and they provide ongoing support for the full duration of the partnership.

PAaaS providers are essentially democratizing consultancy, data science and prescriptive analytics which makes powerful business tools once only available to retail giants such as M&S and John Lewis accessible to retailers of all shapes and sizes.

The Predyktable team has combined two decades of experience in retail and hospitality to create a full-service solution which prescribes the best business action with a greater degree of confidence, providing our clients with the knowledge they need to stay competitive. For more information about our full-service solution drop us a line, we’d love a chat!