In more than one high street retailer I’ve heard the same thing. “Oh, yes, we do data science stuff – it’s over there in the marketing department where we decide who to email”.
I suppose that isn’t surprising. After all, the marketing team probably generates quite a lot of the data through their loyalty programmes, and using data to fine-tune which marketing emails to send to which customers is an obvious and straightforward application.
But those retailers who leave data analytics locked in the marketing department are missing a trick – and they are also revealing something seriously wrong with their overall strategy.
Retailers who leave data analytics locked in the marketing department are missing a trick – and they are also revealing something seriously wrong with their overall strategy
The trick they are missing is that data science techniques can be used to add value to a retail business in a huge variety of ways. Clever analysis of stock in channel can reveal pockets of slow-moving lines and unlock valuable working capital.
More than one fashion retailer, for example, has built models with data by day, SKU, colour, size and store in order to forensically determine which markdowns to apply when. And yet other retailers in the same sector continue to manage pricing by instinct and by spreadsheet.
Models can also help get the right sizes and varieties of the right products into the right stores at the right time. And that kind of modelling does not have to be the preserve of giant retailers – fashion specialist River Island, for example, has used AI and predictive analytics to improve its stock management and merchandising.
Analytical techniques can be applied not just to data about customers and products, but to supply chains, delivery networks and even stores.
Imagine building a segmentation model, not of customers but of different store types in your network. One supermarket did exactly that and was able to build completely different models for stores based on the type of customer and type of shopping journey they served.
Even more profoundly, careful use of data can spot emerging consumer trends and purchasing habits that transcend any individual product.
Consumer interest in low-fat and organic meals and in environmentally responsible products were all spotted long in advance by those retailers making full use of their data, and they responded by sourcing new products and ranges before their competitors.
Too many retailers, however, have effectively walled off data and analysis into their marketing teams.
Indeed, in one retailer the language the leadership team used was a dead giveaway. Ask a trading director or retail regional manager about data and they’d proudly point to the small team working on it and refer to them as “the email factory”.
That label revealed not just a series of missed opportunities but also a real strategic problem. It revealed a wider leadership team in “data denial”.
As they pointed to the email factory they did so with real pride – they appreciated that what the team was doing was complex and valuable, but the idea that the data being analysed might not just drive emails, but could decide which stores to open and close, which products to buy and where to send them seemed to have never occurred to them.
At a cultural level, that is understandable. Retail leaders in many functions have grown up and become senior based on their deal-making skills, their product knowledge, their instincts about customers and their operational excellence. But many have done so in a business where ‘big data’ did not really exist.
Many retail leaders have grown up in a business where ‘big data’ did not really exist
In a world that has changed beyond recognition and where data science can augment and enhance many leadership functions, a bit of denial is understandable.
That’s why, in my new book The Average is Always Wrong I ended up focusing as much on the cultural challenge facing the leadership team as I did on writing case studies of how data can be used.
The journey towards real data-centricity can be a tricky one for management teams, but it is vital. All those pureplay start-up and technology businesses crowding into the retail space are data natives, after all, and established retailers need to keep up.
Time to let the analysts out of the email factory.
Retail Week subscribers can get 30% off The Average is Always Wrong until the end of October using the code AVERAGE30. Visit https://harriman-house.com/averagealwayswrong.