Hardly a week goes by without some story or other about how brands are not using, misusing – or are simply unable to use effectively – their hard-won consumer data.
Such angst is commonplace and reinforces the view inherent among retailers that there is great value hidden within their existing data. Most, however, are stuck for where to start in terms of creating value from that data. It is surely no coincidence that, despite 96% of retailers believing personalisation makes good business sense, only 6% currently have a strategy in place to make it a reality.
Unique customer and behavioural data are vital elements of personalisation, and comprise a healthy proportion of retailers’ data mountains. The uncertainty of how to structure, manage, access and use that data is only a partial explanation for the lack meaningful progress in data-driven personalisation. Yet one of the most practical and achievable applications of big data for apparel retailers today is personalisation – providing they focus on the right data.
First, let’s look at demand. Some 60% of shoppers indicate that they want retailers to present fashion that is relevant to them; 69% find it annoying to have to search through lots of clothes to find the right thing. Correspondingly, research increasingly supports the supposition that shoppers are generally happy to share their body measurements and fit preferences in order to facilitate a more curated and relevant shopping experience.
The message is obvious; customers are telling retailers loud and clear what they want. Retailers need to start listening and more important doing something to plug the personalisation gap – otherwise they risk letting their customers take their custom to those retailers which have started to tap into the potential opportunities this represents.
Second, let’s consider retailers’ needs. In the face of rapidly rising consumer expectations – every outstanding customer experience only raises the bar for every other subsequent customer experience – traditional consumer segmentation models no longer qualify as personalisation. Retailers need to work out how to employ their customer data to deliver personal, individualised brand experiences at every customer touchpoint, to bridge their data gaps to improve the customer experience.
Third: what data is needed? Fit preferences are highly personal data which, when combined with other data assets, provide a rich and insightful picture well beyond the near-meaningless garment “size”.
The concept of fit preferences encapsulate the truism that two otherwise identical people are highly likely, for reasons only they know, to prefer to wear different size garments. Such preferences change according to the type of garments being considered, and the context in which the shopper expects to wear them.
For clothing retailers, this data is truly gold, is relatively easily obtained (with full customer participation and permission), is well understood, and is straightforward to repurpose for personalisation– ie, this is the right data, not just big data.
In fact, in conjunction with shopper and garment measurements, and other data such as purchase history and location, fit preference data creates an opportunity for retailers not only to curate each and every customer experience at each and every touchpoint, but to fundamentally change the way shoppers search for, discover and buy clothes. Ultimately, fit preferences – encompassing all of the above – will enable the concept of “size” to be retired by apparel retailers and shoppers alike.
There is no quicker way to cure angst than to remove the doubt on which it is predicated. While big data might well be over-hyped currently, there is magnificent value in data that is accessible here and now and helps retailer achieve two of their most pressing goals: enriching the consumer experience and boosting the bottom line.
James Gambrell is chief executive of Fits.me
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