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Analysis / Ecommerce

Recalculating The Stock Equation In Fashion Retail

Fashion Retail

As UK retailers face an increasingly complex and competitive trading environment, many are looking to leverage the insights generated from their own businesses to drive performance. With declining footfall on High Street – with shopper traffic levels in March and April declining by 4.8% across the two months, the lowest since the depths of the recession, according to the BRC – and Visa’s latest consumer spending index suggesting 2018 is on track for its worst performance since 2012, each conversion has never been as fiercely contended.

The age-old ‘data as oil’ analogy won’t be lost on many UK businesses, with retailers understanding the benefits of using data – if properly extracted and appropriately channelled in a timely fashion – to fuel business success. Yet, with multiple data sources, collected from disparate data points across the business, many retailers are still struggling to make sense of everything. The truth is that too many businesses are swimming in data but drowning in insight, meaning they lack the understanding needed to leverage competitive advantage – and no more so than in fashion retail.

One of the main challenges that still persists for many fashion retailers is the ability to quickly shape business operations to best fit the reality of what is about to happen – whether this is fast moving fashion trends, celebrity endorsements, marketing activity or fluctuations across sales channels. And that’s not forgetting other variables, such as the weather – aggregated data gathered from Inovretail’s clients using its data science solutions shows that, in most Mediterranean climates, it can be expected there will be up to a 31% decrease in store visitors on a rainy day. And this inability to react in real-time to changing scenarios can be traced back to a single cause: inaccurate and unrealistic forecasted demands.

Historically, retailers have used an outdated ‘scattergun’ approach to the allocation and replenishment of products in stores. This was often based on what happened the previous year, without taking into account the store’s location, local shoppers’ buying patterns and other external factors, such as weather or events. But this approach is no longer fit for purpose, as the fates of several High Street retailers – such as BHS, East and Store Twenty One – attest, as they fell by the wayside due to their inability to cope with managing costs, stock and new retail models.

Predictive platforms offer a solution to overcome the challenges of real-time retail. With accurate, dynamic and multi-dimensional demand projections, aligned with forecast-driven stock replenishment calculations, retailers can operate more efficiently in line with market demands.

A data science led approach tackles the challenges faced in inventory management head on, to deliver a real-time forecast, based on hundreds of different variables, such as weather, promotions, holidays or even football matches. This allows businesses to automate allocation and replenishment decisions across products and stores. Enhanced stock recalibration enables retailers to immediately improve profit, by protecting margins through better assorted stock, driving lower inventory costs and increasing workforce productivity.

With sophisticated data science and a greater trust in systems and analytics, fashion retailers can harness the power of in-season trading, fashioning a faster return, higher margins and lower execution costs.

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Robin Coles is a director at Inovretail, a retail intelligence company.