With the immediate crisis response of covid now easing, retailers are able to turn their attention back to innovation. They’re switching from ‘survive’ mode to ‘thrive’ mode, and finding that the lessons they learned in the pandemic are invaluable. The tools, techniques and technologies they applied to get through the crisis are just as pertinent now, as they re-focus on better customer experiences and more efficient retail store operations.

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Retail data analytics is one such retail technology. Retailers used it during the pandemic to respond to covid requests, but now realise its broader potential. Retail data analytics can help retail businesses find innovative ways to make operational efficiencies, strengthen customer relationships and loyalty and deliver an outstanding shopping experience. By capturing data from a range of operational systems – inventory, retailer POS, marketing, promotion types, loyalty, supply chain movement, consumer demand – and pulling it all together, retailers can see patterns within their business, understand consumer behaviours, spot trends early and adapt rapidly.

Any retailer who doubts the value of retailer data analytics does not have to look far to see the evidence for its use. Research shows that there is a clear gap between retailers who use retail data analytics and those who don’t. One survey, for example, looked at retailers who have ‘a common set of tools and methods across the enterprise for accessing and analysing data’, and found that 80% exceed their business goals. In those companies who had the tools and also trained their staff in how to leverage data, overperformance rises to 88% of the cohort.

Retail data analytics is at the heart of personalised marketing, and pricing management – two key strategies which drive improved results. Retailers who offer a personalised customer experience get 20% better results than those who do traditional marketing. Those who use analytics for pricing management see a 2-4% improvement in margin and 1-2% increase in sales.

An effective retail data analytics system will draw in data from a wide range of operational systems, and can therefore deliver a whole host of insights to staff, including

  • Customer profiling – who are the most loyal customers, what do they want to buy, when and how do they buy, how do they respond to promotion types, who has not purchased recentl
  • Performance against key performance indicators (KPIs) – real-time data and historical comparison views
  • Process that can be improved and streamlined
  • Top performing retail stores
  • Recruitment and/or training gaps
  • Trends – comparisons by hour, day, week, month, year
  • Inventory alerts and where to optimise supply with demand
  • Fraud identification
  • Return on investment for marketing spend – how shoppers responded and the impact on sales
  • How to personalise promotion types – based on shopper profiles

  • When prices need to be adjusted – in real time

  • Optimise supply with demand

When it comes to implementing retail data analytics, there’s a bit to think about. Here’s our top tips for planning for success:

  • Choose the right platform – retail data analytics is going to be at the very heart of your business and must empower it, not hold it back. So it’s essential to choose a platform that is open, stable, scalable and secure.

  • Enterprise retail solutions – the value of the analytics you get from your platform depends on the range and quality of the data it can access. Having an enterprise-class retail solution, with integrated, rather than ‘siloed’ data, ensures you can analyse data concurrently from multiple retail systems. So, for example, you’ll not only be able to see that sales increased over the last three days, but you’ll also be able to find out why. If the reason is a promotion type, you’ll be able to make that link, and also dig further to determine other valuable information, such as the conversion rate, which demographics drove the increased sales, and the split between online and brick and mortar transactions.

  • Online/offline integration – shoppers no longer make a distinction between shopping through ecommerce solutions and in the brick and mortar store, as seen by the growth of omnichannel retail transactions such as click and collect/BOPIS and the fact that 70% of retail store shoppers have researched online first. So analytics must cut across boundaries, and give a single, integrated view of the business.

  • Give the right staff the right tools to make the right decisions. In today’s cut and thrust retail world, data that is days, or even hours, old is of limited use. Staff need reports in their hands, wherever they are in the retail store or office, with tools that are available online, via any device.

  • Get expert help. Retail data analytics can add so much value to your business, but it’s essential to get it right. A decision is only as good as the data is based on. So call in the experts to help you, and set up a retail data analytics platform that will deliver innovation that help your business to grow.

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