AI Singapore regularly conducts AI Clinics for organisations keen to get started on their own AI journey. These are sessions led by our AI Advisory team and the goal is to acquaint decision makers with knowledge of where AI might serve their business or organisational needs. In this article, I provide a flavour of what is typically shared in an AI Clinic. The target industry for this particular clinic is that of sales and marketing in the retail space.
For as long as there has been commerce, sellers have been keeping tabs on the buyers of their goods and services. That means data on customers, goods/services, and transactions. What separates the good sellers from the poor ones is often the mastery of this data. This is where AI promises to deliver tangible value.
Think about the relationship a retailer has over time with a particular customer, i.e. the customer life cycle. There are many angles one can take with regard to this relationship. This is illustrated in the diagram below.
For each of these angles, there already exist mature AI tools to help. We will look at two of them – customer segmentation and recommender system.
Do you know your customers? Even though every customer is unique, there are also broad categories to which every customer can be said to belong for marketing purposes. Typically, from the mass of transaction records, a customer profile table can be derived. This describes in each line the transaction history of a particular customer. The columns contain data on different features of the customer’s transactions. The more technically inclined reader can imagine this table as representing the customers as points in a multi-dimensional space. The task is to identify groupings of points and then target them with separate marketing campaigns. All this can be easily done with a machine learning model.
Let’s look at a more concrete example to make things clear.
Suppose you are a pet shop owner and, after experimenting with different features, the machine learning model segments your customers into five groups with the following summary values.
For the first segment, you notice that the customers return higher values than the others. So, you call them the “big spenders”. For the second segment, the average frequency of purchase is almost twice that of the next highest group. You label them “frequent buyers”. So on and so forth. With these segments identified, you can proceed to craft appropriate marketing campaigns to engage your customers.
Do you know what your customers want? Of course, you do know what they have bought before. But do you know with confidence what they would likely buy if you were to gently recommend to them? This is what sales promoters do. Machine learning can do this on a massive scale. Take Amazon, the behemoth of retailers. As much as 35% of its sales are generated from its recommendation system.
Just as in customer segmentation, it all starts from transaction records. There is a lot of math involved in processing the records, which lies outside the scope of this article, but it suffices to say that a recommender engine identifies people who have made similar purchases and then proceeds to recommend novel items. As the diagram below illustrates, if John and Jane have both bought dog food before and John has also bought a dog toy but Jane has not, the system will recommend the dog toy to her. The system is able to do this even though it has no semantic understanding of dog food and dog toy.
What I have just shared is a small sampling of the content that goes into an AI Clinic. Keen to know more? You can contact the AI Advisory team at firstname.lastname@example.org for further information.