If Amazon killed the high street, can AI revive it?

Artificial intelligence

We all know how valuable (and admittedly sometimes frustrating) it can be having suggested items thrown in our faces online. The ‘We think you might like…’ and ‘Because you bought…’ sections have become a staple feature of eCommerce sites and streaming platforms, with an algorithm pre-empting what we might be interested in next based on our buying, browsing, viewing or listening behaviour. And that’s before the relentless re-targeting across every platform you subsequently visit begins…

This is par for the course online and consumers have come to expect, even crave, this level of personalisation, despite the fact that it can sometimes feel like overkill. So what if you could replicate this in the real-world, for brick and mortar stores? Imagine if you could proactively encourage customers to enter your store in real-time, tempting them with offers and deals personalised to them? And once they’d made it into the store, if you could control where their eyes fell, and also maximise the space your store could offer? With the far-reaching and powerful potential Artificial Intelligence (AI) has, it could just be the saving grace of any struggling brick and mortar retailers.

Demand and space management

For some people, AI-enabled retail still feels like a Mad Max-inspired dystopia. But in fact, many savvy retailers have already turned to AI to deliver insights they would previously have struggled to access.

French retailer Intermarché, for example, has been using artificial intelligence to anticipate demand through categorising large volumes of data quickly. The retailer set out attempting to automate forecasting, initially based on three years’ worth of historical data from both its smallest warehouse, and its largest. As a result, Intermarché has seen a forecasting reliability rate of 95 per cent – 15 per cent more accurate than the methods it was previously using. This ensures the shelves remain stocked and keeps customers happy.

And UK retailer Pets at Home has also been using a new solution to meet shifting customer demands. Having grappled with the growing number of Stock Keeping Units (SKUs) within its 450 nationwide stores, the retailer was facing a shortage of space. Keen to explore how it could dedicate up to 20% of each store’s floor space to new in-store services, including a veterinary clinic, and other more experiential pet-based services, new software was needed. With the chain’s largest store six times larger than its smallest, store planners needed a solution that could accurately determine which products could be delisted in certain stores without impacting on profit margins.

With the help of a Store Planning and Optimisation solution, the retailer has seen profits increase by 13%, along with using the software to maximise store space in their new stores.

The chocolate bar effect

It’s not just optimising floor space that can reap big rewards. Understanding your customers’ psyche also has massive potential.

The idea of placing related products next to each other in shops, or strategically positioning products, is hardly revolutionary. It’s no secret that supermarkets have historically put the most tantalising chocolate snacks right next to the checkout because they have your attention for a couple of seconds, to be teased by the sinful offering as you’re waiting to pay. And grouping related products together that complement one another can obviously save consumers’ time and encourage more sales. But retailers have historically been manually reviewing customer data to make category management decisions themselves, often a laborious and potentially inaccurate process. Which is where artificial intelligence makes all the difference.

For retailers to gain an in-depth understanding of how and why shoppers choose the products they purchase, artificial intelligence can interpret customer behaviours. With this insight, assortments and shelf plans can then be used to steer the customer decision process. For example, retailers can determine if chocolate bars and salty snacks should be grouped to match shopper preferences on price, brand or flavour, which may be completely different from how they organise health drinks and sodas.

Maximising loyalty

Ultimately, retailers know how valuable customer loyalty is to their bottom line. But they’re also painfully aware that customers will be fickle if their needs change or, more likely, if they have a bad experience.

Retailers now have such a wealth of customer data available to them (from loyalty cards, in-store wifi tracking and opted-in customer email addresses) that how this data is managed presents a real opportunity.

Machine learning can interpret this data to present a 360 degree view of the customer which can aid in forecasting demand for individual stores, and also revolutionise marketing comms to customers. This data can be used to personalise the customer experience through targeted communications, across channels. Location-based messages, for example, can be sent to your customers’ phones when they’re in the vicinity of your store, tempting them with selected offers or deals. This takes ‘suggested items’ away from eCommerce and firmly into the realms of brick and mortar, with true omnichannel marketing now a reality.

Staying ahead of the curve

With customer behaviour more turbulent than ever, and expectations higher, the tools now available to retailers to enhance their store offering and give their customers what they want has massive potential.

From revolutionising personalised marketing communications when customers are most likely to buy, to optimising their experiences in store, AI is surely the ammunition retailers need in the fight against the eCommerce giants.

And with early adopters seeing big financial return already from investing in AI, now is the time to take action for maximum impact.

By Kevin Sterneckert, Chief Marketing Officer, Symphony RetailAI.