There is no doubt that artificial intelligence (AI) and machine learning (ML) will increasingly have a greater influence in the retail ecosystem. Currently, however, there are already many retail-based systems that contain AI/ML. The proven methodologies and techniques that aim to give you more commercial leverage should be exploited to stay ahead of the curve.
We want to mention five concrete applications of AI that can instantly be applied to your retail business. They can improve revenue, customer satisfaction, operations and so much more!
Each of these AI applications will have its own extensive ML6 blogpost in the future. You can read an extensive blogpost (once it is online) by clicking on the title of one of the applications mentioned below:
Hyper-personalisation is everywhere around us, from Netflix having a very precise profile of us to your local supermarket knowing which products you like. Therefore, these companies most certainly have a profile of what kind of person you are, because you are what you consume. With every interaction with the system, a customer is leaving small breadcrumbs of information about themselves. With these breadcrumbs, you can make a useful profile of your users.
With this built personal profile you can recommend related products (recommendation engines), change the pricing of products (price optimisation) and even predict if customers are going to churn from your business (churn prediction).
Or in other words: predicting when your customers are going to leave you. But why is this useful? Always remember that your number one promoter is your existing customer. When your marketing and sales are targeting new customers, don’t forget to keep your current customers satisfied.
If you can predict when a customer is going to leave then you can act upon that by contacting them in advance. You can give them discounts and create loyalty programs for example. By being able to target churning customers, you will lose fewer customers and therefore increase your revenue.
Lost sales are sales you have not earned because you did not sell a product while the customer wants to buy it. This phenomenon is the hardest to quantify and the biggest loss for a supplier.
When you are able to predict when you are almost out of inventory you will minimise the amount of products you need to store for a longer time in your costly warehouses and minimise the number of products you cannot sell because you are out of stock. A single item not being available could even mean that the customer will never return to you.
Recommending your customers related products to the ones they bought before helps increase your revenue. This has been shown by Amazon, Netflix and lots of other companies.
Netflix wrote a paper about how to save more than $1 billion dollars per year with its recommendation system. While Amazon has 35% of its sales done by recommendation engines.
Price is one of the biggest reasons people buy a product. According to PwC, this is the case for 60% of the people. Setting your own price is therefore of the utmost importance. Because your competitors are doing the exact same thing, the nature of this problem is very hard. Having a sound pricing strategy could be a big differentiator between you and your competitors. But having the cheapest product is not per se a guarantee to gain the most revenue or profit. Game theory gives as a good start for our pricing model but has shown flaws due to its very theoretical nature. Artificial Intelligence can definitely help here overcome the gap between theory and reality.
Retailers should continue using innovative technologies to tackle the challenges they face from inventory forecasting to improving customer experiences because their competitors are doing the same. AI technologies allow retailers to compete in the 21st century economy and better serve their customers. This is what the future of retail looks like.