How can predictive analytics help retailers weather the economic storm?

people in rush

In this blog post I’m going to suggest  some ways in which predictive analytics can help retailers weather the economic storm they’re currently facing. The UK’s battered retail sector has been enduring an ongoing perfect storm following the financial crisis of 2007 / 2008.  The government would like us to believe that things are much better now.  The truth is that economic recovery has been slow and patchy at best. Think of Tesco’s recent problems, declining sales from all of the UK’s major supermarkets and repeated profit warning from the likes of Asos to name but a few. We’d be naive to ignore the evidence…it’s still tough out here.

And let’s not forget how important the UK retail sector is to our wider economy.  Retail is the largest private sector employer, providing jobs for over 3 million people.  Annual retails sales are in excess of £300 billion (with approximately 9% of that as online sales). Seven retailers (between them worth around £76 billion) are in the FTSE 100, and many pension and investment funds have significant retail holdings.  Retail is crucial to broader economic health.

So what has been going on? There are a number of factors that have created such challenging operating conditions.

  • A reduction in disposable incomes – wage growth has been weak or non-existent and, crucially, lagging behind inflation.  Levels of consumer debt remain high.  This means people have less spare money.
  • Changing consumer expectations – consumers now have multiple options open to them in the way they shop (in store, online, from a mobile device and now directly through social media applications) and can compare prices quickly and easily. Their expectations are high, they’re often very price sensitive and their attitudes change quickly.  Retailers have to ensure that they truly understand their customers, can meet their expectations and can make effective use of all communication channels.
  • High rates of VAT – VAT is at a historically high rate of 20% which retailers have generally tried to absorb rather than pass onto consumers.
  • Major growth in retail capacity – a relentless growth of retail floor space over the last twenty plus years, a removal of restrictions on opening hours and the dawn of ‘omni-channel retailing’ all adds to a highly competitive environment.
  • Increased commodity costs coupled with a dependence on international sourcing – declining availability of resources, increased domestic consumption in emerging economies, and a scaling back of production in the sectors hardest hit by the recession has meant that there has been significant increase in the cost of raw materials.
  • Devaluation of Sterling – Sterling depreciation since 2009 has conspired with real increases in commodity pricing to make input costs for UK retailers much higher.

With very tricky conditions looking set to continue, whilst also creating long term structural change in the market, how can UK retail fight back? Despite all of the challenges I believe that there are real and significant opportunities for retailers to seize upon, and at least part of the answer lies in the intelligent use of advanced predictive analytics technology.

Many retailers, especially those with online channels or loyalty programmes, have an incredibly rich store of data on customer behaviour, buying habits and preferences often including highly contextual feedback as a part of their interactions. This rich data resource can be used to better understand both customers and suppliers in order to pre-empt and predict preferences and requirements. Predictive analytics can be used to great effect to address the following challenges:

  • Acquisition – improved customer insight can help reduce the cost of and increase the efficacy of new customer acquisition by enabling the tailoring messages to specific audience groups, delivered through the preferred channel at an optimum time to maximise conversion.
  • Retention and lifetime value – data-driven customer insight can be used to encourage additional spend because customer management is better suited to the individual’s needs and expectations.  The same analytical approach can also be used to extend the length of a profitable customer relationship and to avoid undesirable relationships with clients that present a likelihood of fraud or other type of risk.
  • Sourcing, product lifecycle management and pricing optimisation – consistent and intelligent application of predictive analytics is a crucial contributor to proactive sourcing strategies and the active management of costs.  In current conditions this capability is more critical than ever.  Knowing when and where to source optimally will quickly become a significant differentiator as will close analysis of pricing and promotional strategies.
  • Store and channel segmentation – for retailers with extensive ‘bricks and mortar’ estates or with multi-channel operations, the ability to analyse patterns of sales and demand and to determine how these vary regionally enables costs to be removed from the supply chain and for the more efficient management of inventory.
  • Internal and supply chain fraud – innovative application of advanced analytical and modelling techniques will quickly identify anomalous patterns of activity that can be prioritised for investigation.  Fraud is an area where behaviours adapt and mutate quickly. Consistent analysis and monitoring will ensure that retailers can keep pace, reduce wastage, save money and protect their bottom line.

Let’s be clear. Predictive analytics is not a panacea to all the current challenges. However what it does represent is a set of skills and tools that can (and should) be developed internally and that can be applied in many areas of a retail business. If implemented correctly, it can deliver substantial gains on both the demand and the supply side of the business. These many areas of application, each delivering small yet meaningful percentage improvements from current performance, add up to a measurable and significant improvement simultaneously reducing costs and maximising revenues.

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