Machine Learning and Artificial Intelligence are not just hot topics on various IT and data-blogs. It is now an actual tool that enables you to offer your customers personalized, relevant deals and ultimately increase sales and customer satisfaction.
Data about your customers and their behavior is an indispensable resource for Sales. Without this insight, it is difficult to know how to generate additional sales or increase customer loyalty. Machine Learning helps you and your sales staff make use of the available data and with quite a high reliability, it can help predict the customer’s next purchase.
This is where Machine Learning makes the big difference
In an article about Machine Learning, the industry and news site insideBIGDATA explains why the traditional way of analyzing customer behavior is outdated.
Obviously, it makes sense to display diapers, baby oil and other relevant baby products for a new mother. However, with the traditional use of purchase behavior, the new mother will stay exposed to the same type of offers within the same categories until she changes her choice of products or her shopping behavior.
With the use of Machine Learning, the intelligence grows along with the baby, and it can help predict which products the retailer should show the mother in order to be ahead of her and her baby’s needs.
Purchase behavior and geography is no longer enough
According to insideBIGDATA, marketing has traditionally been using the concept of RFM Score to identify the customer’s purchase history and on that basis present the customer for new campaigns and products:
- Recency; when was the last time the customer bought something?
- Frequency; how often does the customer shop?
- Monetary Value; how much does the customer spend?
With a RFM Score as well as purchase history, age, gender and geography, you get an idea of the customer behavior. However, it is a limited scope, because it only gives you a retrospect view of the situation. And this is not sufficient, as customers often make uncharacteristic purchases and their behavior evolve over time.
Customers, who usually buy cheap products in your shop, can easily change buying patterns. And if you only show them cheap products, you can miss out on a sale or completely lose them as customers if they are in the market to buy a more expensive product, such as a gift, or if their financial situation changes. You will not pick up on these nuances with traditional purchase history, but you will with Machine Learning.
100 percent increase in click rate
With the right usage, Machine Learning can give you very specific results. Inspired by Netflix and Spotify, the French eBook service Youboox wanted to turn up the user activity and the influx of new subscribers by giving users better recommendations. By using Microsoft Azure Machine Learning, Youboox has succeeded in substantially increasing the number of new users and activities in their online bookshop.
Predict customer needs
Tweets, pictures, status updates, cookies and e-mails are all sources of data that can provide detailed insight into customer types and customer behavior. It is not something new. What is new is that bigger and better computers have made it possible to analyze larger amounts of data than previously. A number of sophisticated algorithms find patterns in the data and provide a precise estimate of which content or which of your products, the customer will be interested in.
As mentioned earlier, there are many retailers today, who look at statistics and demography to find the right customer segment and target the sale. Now an algorithm allows you to get insight into a customer’s individual characteristics, so you can make informed decisions about which recommendations to give the customer. Machine Learning not only analyzes the customer’s behavior, it also looks into trends and tendencies and gives you the sum of it all to help you predict the customer’s next purchase.
Machine Learning will a part of the POS-system NP Retail
In the spring of 2017, we will launch new features in NP Retail. One of the many new features is the integration with Microsofts Recommendations. This integration enables the POS to come up with three types of product recommendations based on Machine Learning: associated products, other-customers-also-liked-recommendations, and personalized recommendations.
Get a trial of our POS system
Choose between the web browser version for pcs and tablets or the app for smartphones. You get access to NP Retail and Business Central for 30 days. As a bonus, you also get access to the webshop system, NP Ecommerce.