All over the world right now, retailers are struggling to survive in the current climate, with little sense of what the future will hold. Right now, we’re seeing supermarket shelves laid bare as consumers panic buy rice, pasta and toilet paper and shift away from discretionary shopping, like fashion and beauty. Who knows what consumers will want to buy in a few months’ time and how will this impact retailers when it comes to predicting changing consumer patterns?
Enter AI. While most businesses may be on an IT systems freeze right now, it’s worth considering investing in AI when possible, especially given the significant boost it can bring to your bottom line during these uncertain times. Here are six of the biggest benefits AI can offer retailers to help drive revenue.
Throw out the book on traditional demand forecasting
The old-school way of predicting what products to purchase was by simply using historical sales data.
But through AI and machine learning, businesses can now analyse information at a very granular level. For example, a supermarket can work out how many tins of tomatoes it will need on a Saturday afternoon compared to a Wednesday morning. Then other factors will come into play – how many tins are already in stock? How many are currently on the shelves and do the tins come in packs of 12, 24 or 48? For every SKU in the store, AI can reveal the quantity needed to satisfy demand.
“I can still see retailers today using Excel spreadsheets to predict stock going forward or using manual intervention or human intuition,” observes Paul Winsor, retail general manager at Datarobot. “There’s still way too much of that going on. From a cost perspective, if you get it wrong and over-forecast on perishable goods, you’ll lose a fortune.
“But if you get it right, it could be a revenue driver.”
Create a personalised customer experience
US Supermarket giant Kroger is one of the largest retailers in the world with $121 billion in revenue, but interestingly, 96 per cent of it comes from its loyalty customers, due to its exceptional personalised offers.
With the help of AI, Kroger regularly sends 60 million of its customers MyMag, a catalogue filled with personalised offers for customers based on their shopping and lifestyle habits. So if you’re vegetarian, your MyMag will contain discounts on plant-based meats and offers on fruit and vegetables. If you’ve got a dog, it will include coupons for pet food and vitamins. It’s a win-win for both customers and the business – Kroger’s wasting its time creating generic offers and consumers are being enticed by highly relevant products.
Get the price right – and boost your bottom line
Landing the correct price for a product can be a nightmare, but AI can help predict the maximum price of an item that won’t impact the volume, which could then lead to huge cost savings, explains Winsor.
Test and launch new products
With consumers constantly looking for inspiration, many retailers churn and burn through 30 per cent of their products as they bring new products to market. But usually, those buying decisions are made on the basis of a gut instinct, as there’s no history available on how those items will sell – retailers are often taking a gamble.
However, some businesses are now using AI to look for familiar attributes in datasets of similar products like size, ingredients and quality, then building models based on them. Based on the findings from AI, you can then create forecasts and work out appropriate volumes at launch.
“You won’t disappoint customers within seven days of launch because they can’t get it anymore, or worst case, you over-forecast. Say it’s a perishable product and you’ve manufactured 10,000 of them, but you’ve only sold 7,000 and 3,000 are going out of date next Tuesday,” say Winsor.
“If you get it right, you have the opportunity to maximise your revenue by making sure you’ve got products when the customer wants it. It’s the right amount with the least amount of waste because you haven’t over-manufactured. You’ve taken the intelligence from similar products to build that model to predict that forecast as it hits the marketplace.”
Choose the best locations for your store network
French retailer Carrefour was one of the pioneers of the creation of hypermarkets, but then pivoted to building express stores to cater to the changing customer more interested in shopping on the go. Carrefour used AI to help them choose the right locations with the right foot traffic for these new stores, taking into account factors such as how close their competitors were located, the average footfall of the total population of the area, the average income of people and the proximity to key spots such as train stations and bus stops.
“If you look at the reports, Carrefour is building express stores in Italy, France, Belgium and using AI modelling to determine what their revenue would be. In the past, there was no AI used to decide where to open a new store. Now you can use that sophistication so you don’t waste your money by getting a negative return by opening an expensive physical store,” explained Winsor.
Get your rostering right
“Thirty per cent of all retail costs comes from labour and very traditionally, retailers have been quite archaic with how they schedule staff to work in their distribution centres and stores,” notes Winsor.
Typically, a store manager will tell you that you have to trade at this amount of revenue and based on that, here’s 160 hours to cover your store next week. The manager will then say ‘We’ll have two people in the morning, two in the afternoon, two in the evening – and I’ll do it seven days a week’.”
Now companies are turning to AI to observe historical sales, analyse the patterns throughout the trading day and build a model based on that to determine the best times of the day when staff are ready to pick and pack in the distribution centre or serve customers in stores.
DataRobot is the leader in enterprise AI, delivering trusted AI technology and enablement services to global enterprises competing in today’s Intelligence Revolution. Its enterprise AI platform maximizes business value by delivering AI at scale and continuously optimizing performance over time. Learn more at datarobot.com.