Not all customers are equal

Consumers today demand a very individual, personal experience that is unique to them.

They want to connect meaningfully with brands, use a whole range of new retail touch points and expect retailers to deliver expertly curated, ultra-personal experiences.

The latest research report from Accenture and the Retail Industry Leaders Association’s (RILA’s) (R)Tech Centre for Innovation shows consumers want personalisation, both in store and online. And whether that’s wardrobe suggestions or interior design advice, demand for curated expert service has risen by a third in just two years, particularly among millennials: 69% expressed interest in personalised brand experiences.

To respond, retailers must track individual customer preferences and profitability, identify and cultivate high-lifetime-value customers and liberate the retail experience by enabling ‘ubiquitous shopping’ – consumers buying anything they want, anytime, anywhere. What does this look like in practice?

Breakthrough visibility – and ubiquity – leveraging analytics-driven insight…

To deliver experiences like these, retailers have to transform from “shopkeeper” to “customerkeeper”. Focusing the whole business around serving customers creates a virtuous cycle: engaged employees providing relevant products and services to happy customers – encouraging them to spend more.

Of course, giving customers those exquisite, personalised, seemingly spontaneous experiences takes some serious behind-the-scenes work. Brands and retailers will need to combine world-class technologies with a deep understanding of customers’ behaviour, empathetic communication and data mastery – all in real time.

It’s the future of retail growth, but the journey’s only just begun. Many retailers think their current internal capabilities are sufficient to get there. The reality is significant investment and a different approach. For example, retailers might see AI systems as all they need for successful analytics. But before they can extract and exploit insights, organisations need to ‘teach’ their AI with high-quality data. Low-quality data leads to results that risk harming, not helping, the business. So retailers have to rigorously vet and clean their datasets before they can use them.

…across the enterprise

Becoming truly data-driven is a significant change for most retailers. But following some proven steps can make it easier and simpler to achieve than they might expect. They need to break down and integrate functional silos, acquire data science and develop analytical skills.  Fostering a culture that welcomes change is also key, as is measuring what matters: which customers buy what, where and how. 

To generate the maximum value from their data, retailers need to apply analytics on an enterprise scale, across areas including revenue drivers, marketing and fulfilment costs and digital levers for customer behaviour. That’s vital to avoid the misalignment between what customers want and what’s available.  The digitally-enabled organization will gain a true picture of each customer’s behaviour and each product’s performance. Then they’ll be able to avoid margin-eroding, mass-market promotions, but focus instead on the highest-value, most profitable customers.

These granular customer insights can support new business models centred around specific user needs and shopping styles with customer insights woven into the whole business.  Product teams can use data-driven creativity to understand their audiences, and marketing promotions can be grounded in insights about the profitability of individual products and customers.

Key questions

To find out if you’re maximizing the value of your consumer data, ask yourself some key questions:

  • What percentage of your revenue plan will come from existing customers?
  • What’s your acquisition rate for new customers?
  • What’s your rate of repurchase from 1st to 2nd, 2nd to 3rd, 3rd to 4th, 4th to nth? And what’s the annual trend?
  • What percentage of your customer base is loss-making?
  • How much of the inventory that your customers want to buy do you have in stock?

If you have clear answers to these questions, your data use is probably in pretty good shape. Anything less, and it’s time to think about radically upgrading your data and analytics capabilities or risk a very stark insight gap that will impact your bottom line.