Today, let’s take a look at the practical application of machine learning in everyday life. In this case: in retail.
In today’s digital age of online shopping, personalized advertising and cross-channel offers that are meant to be relevant to us are standard. Why? Modern retail is all about personalization. It’s all about the customer and their satisfaction – and since every customer is different, you still have to meet everyone’s wishes. However, there is a lot that goes into running a successful online shop: an attractive website, competitive prices and constant improvement in order to stay in the competition and not be eliminated by the competition.
Have you ever dreamed of knowing in advance what products your customers would buy? What if you could maximize your profits by calculating the highest price a customer would pay for a certain product? What if you could optimize customer service to address concerns before they become problems?
Fortunately, this is made easier for retailers: through machine learning algorithms. These algorithms can be used to their advantage by retailers to become more competitive in various aspects and improve their business. These aspects include things like price, inventory forecasting, cost reduction, etc.
Machine learning will save you a lot of programming and therefore also lead to reduced costs for your business. Instead of having to pay 10 employees to do tedious work, you could automatically monitor and implement algorithms that continuously check and optimize your e-commerce business and inventory levels. This automation gives you the freedom to focus on other aspects of your business.
As a retailer, it is important to use all communication channels these days, otherwise you will lose out. The study by the University of Potsdam entitled „Analytics as a competitive factor – determining maturity, discovering profitability potential“ found that only one in four retail companies uses their data for sales and service in order to improve themselves through the resulting analyses.
To avoid making this mistake, companies today should rely on predictive analytics. By evaluating big data, retailers can check what customers want, what their interests are and how they can attract and retain future customers for whom price and customer service play a large and decisive role.
Many retailers have not taken it too seriously to ensure that past customer experiences are included in their portfolio in order to have an overview of what their customers like or don’t like.
Unfortunately, many companies have traditional, outdated inventory management and supply chain systems with slow, inflexible planning, inventory management and pricing processes that require long-term evaluations.
Below you can see, using the example of Amazon and Netflix, how data is correctly evaluated in order to attract more customers in the future or to respond more to individual customers:
As soon as a customer buys something or looks at several things, this is documented and fed into the algorithm as data. Similar purchases by different customers are compared, their product reviews are analyzed, and the algorithm uses this to make suggestions for future purchases. If, for example, a pregnant woman looks at baby items on Amazon over a certain period of time, baby items will be suggested to her again the next time she visits the site because her data – in this case her search queries – was saved and analyzed. This makes it possible to deduce what her interests were. In addition, the system can offer her other things that are relevant to pregnancy instead of baby items in order to broaden the range of offers made to customers.
The principle is no different at Netflix. Users‘ film ratings are saved and evaluated to determine where people’s interest lies so that we can offer many films and series on these topics in the future.
Another method of “getting to know” your customer is a questionnaire, which many websites offer these days.
In the case of the Five Four Club, customers have to fill out a questionnaire by providing information about their preferences. They are questioned and later the computer evaluates this information to find out what the customer’s tastes are and what they might like based on the information they have personally filled in.
Machine learning is a process. You need training data, which in turn costs money. In order to incorporate machine learning into everyday business processes, the algorithm must be fed with a lot of sample data. The more data it contains, the more intelligent it is.
An example of this would be the constantly evolving cognitive computer system Watson from the IT and consulting company IBM.
According to IBM, 80% of all data around the world is invisible and unstructured. It is of no use because most systems cannot do anything with it. IBM Cloud Watson can detect this data and make it usable.
Watson understands different languages, is self-learning and can analyze millions of unstructured, complex data in a fraction of a second and express it in natural language. This helps to understand the amount of data available. Hypothesis are then generated and evaluated to create answers based on relevant evidence.
The company Northface makes use of Watson in its online shop.
Customers actively communicate with the system instead of choosing from a range of displayed products, as is usually the case. For example, you are asked what occasion you need a jacket for – be it a trekking trip or just to survive the winter – what you are particularly looking for, what features you would like. The system then evaluates the answers and suggests suitable products. The advantage of this is that you do not have to search laboriously for the right product, but it is suggested to you directly.
If a retailer wants to choose the best price for their product, it is not easy. Machine learning tools can help the seller: They can adapt to consumer behavior and help to understand the reasons behind individual purchasing decisions. This is particularly useful when introducing new products. But just as important as the right price is the amount of goods you have on hand, because the perfect price is of no use if you are unexpectedly sold out after a short time and have not thought of new goods. Being left with a lot of goods is not an ideal situation either. Machine learning plays an important role here too: namely, to calculate precisely these factors in advance so that these situations do not arise. The retailer has an accurate, calculated overview of his goods without having to laboriously evaluate and calculate everything himself.
Another important benefit of machine learning is that it provides security. According to Lexis Nexis, in 2015, merchants lost 1.32 percent of their revenue to online credit card fraud, compared to 0.68% in 2014. However, these numbers are trending upward. Security platforms were built to protect, but online fraudsters are becoming more sophisticated. Therefore, it is crucial to have tools that analyze fraudsters‘ behavior in order to protect against them in the future and prevent further attacks. Machine learning tools that are designed to prevent fraud and are therefore provided with enough sample data to analyze and recognize user patterns and behavior can alert the merchant when something suspicious is happening on the site.
In the future, as digitalization progresses, machine learning will be an important part of every company and will make work processes more efficient and faster. These cloud systems can be continuously developed and thus become more intelligent than they already are.
