How Recommendation Engines are Getting More Sophisticated
In the first part of this blog, we read how using market basket analytics in a better way, small and medium retailers, too, can stay ahead of the competition by upselling or cross-selling. Today, we will look at just what goes on under the hood. One of the ways of using predictive analytics in basket analysis is by relying on “Association Rule Mining”. It means finding a relation between different objects in a set or similar patterns in a transaction database, or any such similar repository. Using Association Rule Mining applications of clustering and classification can tell you what items your customers buy frequently together, by generating a set of rules called Association Rules. So it’s kind of, if this, then that technique. Eg: If Pete has bought eggs, he will also buy margarine.
Retailers have used this for:
Changing the store layout
Analyzing customer behavior
The extracted patterns from the database are used to build data mining models, which, in turn, are used to predict performance or behavior. The model works by making a prediction about values of data, which uses known results found from different datasets. The predictive data-mining model predicts the future outcome based on history. But today, with easy access to the Internet and mobile computing devices, things have gone way beyond the scope of the older models of predictive analytics.
With the ever-increasing erosion of margins due to tight competition, it’s a major challenge to create and enhance the precision of predictive models. It may lie in the discovery of new features, inputs, or predictors. Predictive models are improved by enhancing the rules generated from Market Basket Analysis. With shopping being over in minutes and not in days as before, the huge amount of big data, structured and unstructured, collected daily from various customer touchpoints leads to the creation of extremely large data sets that need to be quickly analyzed for application, sometimes in real-time.
So, only if all that data is properly integrated, analyzed, and interpreted can the analysis offer crucial insights to cater to the customer’s needs. When Pete buys eggs, almost immediately, discount offers by margarine brands need to be put up before him, before he exits the e-commerce site.
That’s where artificial intelligence comes in. Machine learning, for example, can go deep into a retailer’s data, look at the patterns and come up with an even more precise model for the recommendation engine. Why does someone buy something at that precise point in time can never be a simple mathematical exercise?
Pete, our fictitious shopper, always buys eggs and margarine together, right? But there comes a day when he decides to buy unsalted butter with his eggs, stumping his retailer. Now, why did he do that? Therein lies the challenge. Our fictitious customer perhaps got bored of eating margarine and decided to switch to unsalted butter. Or, he went on a diet and was advised to do so by his dietician. Or, well, on this particular shopping trip, the fancy label of a new butter product caught his eye, and he thought to himself, “What the heck. Let me try it out.” Perhaps it was raining and Pete thought his eggs could do with a dash of butter in them? Couldn’t have to throw up a 25% off on the margarine offer to Pete this time, right?
Shopping is a constant, yet variable, too. People’s preferences change based on their age, diet, income, those they live or work with, even the season or the weather. If nothing else, shopping is affected by a consumer’s state of mind, a gloom-doom mindset, and the man is bound to something different on that day while shopping.
The complexity of shopping and shoppers is the reason why those like Amazon invested in data scientists and IT to work on their recommendation engines. The war for the retail customer is coming down to that – a battle between recommendation engines.
They are getting more and more sophisticated, even intelligent if that’s the right word. Here’s an example: If you still spend some time searching for what to watch even after your OTT provider’s ‘You May Enjoy’ movie recommendations, it’s not really working, is it?
Remember, recommendation engines are a filtering tool to ensure that consumers are given the data that “best fits” his taste, style, and preferences, ensuring he spends minimum time searching for the right product or service. The algorithms have supposedly “learned” from past data about the products preferred or bought by the customer.
So how can one recommendation engine best the other?
Your data, of course. Imagine the big data of Amazon versus that of your neighborhood store. It’s a no contest. Data is followed by the sophisticatedness of the algorithm. Simple association rules will no longer result in customer satisfaction.
So, the major retailers have upped the ante. Like using neural networks to increase the power of their algorithms. A neural network is a computer program that mimics the human brain cells in the process of learning. It learns on its own without human intervention how to recognize patterns, and basically decide like a human being. Although neural networks are nowhere near the duplication of a human brain, it’s a work in progress.
As of now, it may not be possible for an e-commerce site’s recommendation engine to “understand” Pete’s sudden “desire” one day to buy unsalted butter instead of margarine because of his diet plan, but that day is coming.
An Engine That Drives Customer Intelligence
Oyster is not just a customer data platform (CDP). It is the world’s first customer insights platform (CIP). Why? At its core is your customer. Oyster is a “data unifying software.”
Liked This Article?
Gain more insights, case studies, information on our product, customer data platform