Human vs. Machine

A Competitive Advantage in an Increasingly Competitive Industry

By simply observing our surroundings, it can be deduced that shoppers are swiftly shifting from offline to online, specifically using mobile devices as the main digital platform. The e-commerce fashion industry alone has seen a 12% increase in worldwide revenue since 2018, and is predicted to maintain a steep growth rate. Picking up on these trends, one would assume that retailers are rushing to be present and active on the prevalent platforms, ready to compete in providing users with a seamless online shopping experience. Surprisingly, it is found that less than 18% of retail companies have actually applied at least one AI solution to their current structures.

Why the Hesitation?

Most popular retail corporations have reached their current status by continuing to implement traditional pre-established methods. Considering that a systematic change can be a hefty transition for global, multichannel companies, most retailers question the necessity of AI integration, given that what they have accomplished so far is a result of traditional techniques. Internal departments have been applying the same practices for many years, and do not expect that machines have the ability to miraculously step in and “handle it from there”.

Even though sticking to a functional data strategy could be a consistent method, it can also act as a major limitation to a retailer´s potential growth and evolution. To better visualise this, let us put it into realistic context. One of the main current problems retailers experience is managing inventory to avoid being overstocked or left with excess wastes. This problem can be linked to the lack of data collected. With no concrete knowledge on upcoming trends, demand forecasting, and buyer behaviour, it is difficult to quantify the needed inventory. This issue also stands in the way of promotions, with retailers unable to measure the impact of the promotion on demand and supply.

Enter: AI Solutions

Once machines are integrated to retailer engines, they can work backwards to learn from collected data and generate solutions, as oppose to presuming a strategy and learning from mistakes. AI solutions are able to monitor consumers, studying their behaviour based on how often they shop, what items and styles they usually search for, and what items they mostly avoid. By calculating all these variables, tech systems can generate accurate demand forecasts and help retailers manage inventory down to the last item. In some cases, should the retailer experience shortage or abundance in stock, AI can offer different solutions to sidetrack the issues by suggesting similar items to shy the shopper´s attention away from the lacking item, or offer matching styles to a selected item to encourage more sales. Most importantly, AI offers a visual search tool, labeled Search by Image at Wide Eyes, which cannot be replaced by human control. This added feature is purely a fortunate technology that benefits both consumers and retailers, in that the shopping experience is easier for consumers, and an additional channel attracting clients benefits the latter.

Digital fashion buying power is increasing drastically, and the number of potential buyers is increasing to more than 1.2 billion by 2020. Since most retailers seem to be hesitant on making the drastic switch to machine learning, it is encouraged for them to consider compromise and mesh both tradition and technology, allowing them to allocate the strategic benefits while still maintaining human control. Times are changing, shoppers are evolving, and retailers should be sure to keep up.

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