Fashion retailers are increasingly turning to data analytics to keep up with the latest trends and client demands.
As well as having to meet the demands of “fast fashion” — customers wanting the latest designs from catwalk in stores the instant they appear — businesses must also price items correctly, know when to reduce them, stock enough of the right styles, colours, fabrics and sizes, and ensure that stores are well supplied and operate efficiently.
Data analytics is not new to the industry, which has long used spreadsheets and analysed sales information. However, new sources of data are now available, such as the information on mobile devices or social media sites. “The biggest change is the growth of unstructured data [data not stored in databases] — the texts, images, audio and YouTube videos,” says Keith Mercier, retail industry leader for global cognitive business solutions at IBM.
One method being deployed by retailers to discover more about what customers might want is the use of cognitive computing — programs that simulate human thought process and mimic the functions of the brain.
Cognitive computing relies on techniques such as data mining — the analysis of data from different sources — pattern recognition and natural language processing.
Mr Mercier says these types of applications mean vast new data sets can now be analysed, producing faster insights into fashion trends. “If we can give a retailer a two-week jump on trend prediction, [then] two weeks of selling time in stores is golden in this highly competitive industry,” he says.
By tracking how customers behave while shopping, data analytics can also help to improve the design and management of shops and department stores. Despite the growth of online fashion outlets, many consumers still visit stores to touch and try clothing or shoes before buying.
“The need [for companies] to know who is in the store — with [customer] permission — the moment they walk in is greater than it’s ever been,” says Brent Franson, chief executive of Euclid Analytics, a US-based company that uses location analytics to monitor consumer traffic in shops and malls.
Euclid uses WiFi signals from smartphones to track and analyse everything from the number of people entering a store to the length of time they stay and how often they come back. Customers can opt out of having data collected.
“Knowing your purchase history, and the kinds of things you buy, retailers can create a more personalised experience,” says Mr Franson.
