Use of Machine Learning in Retail

Machine Learning is changing the landscape of industries by helping them capitalize on Big Data. It is no longer exclusive to just digital businesses, businesses from every industry are using this technology to improve on various aspects - processes, customer response rates, RoI, hiring and even sports.

Looking specifically in the field of retail and marketing, Machine Learning helps target customers better, improve response rates and increase the overall marketing RoI. By using Machine Learning to analyze large volume of data - spending habits, demographic info, purchase behavior - and using algorithms to identify trends and patterns. As predictions are made and tested with historical data, the algorithm learns from these results and with time gets the predictions to be increasingly accurate. It keeps learning as more data is fed into the system.

When such invaluable marketing insights are used to develop a marketing strategy, the chances of success greatly increase - thus improving the marketing RoI.

Here’s a quick look on 3 ways how Retailers can use machine learning to improve their RoI.

1. Anticipating demand and driving Sales

Retailers that have access to customer purchase history and patterns can use this data to predict demand/supply. But the use of this data doesn’t end here. The data accumulated over time also holds other important information like trends (esp fashion & styling), the user’s anticipated purchase behavior and repeat purchases.

Retailers can use this information to create individualized experiences for their consumers which builds the customer loyalty and drives more sales.

A very good example of this is the recommendation engine by Amazon. The recommended products they show are based on your purchase and browsing history. And they are uncannily accurate as their recommendation engine learns based on more incoming data.

With the competition around, retailers cannot ignore the advantages that technologies like machine learning provide.

2. Matching people with products

With the amount of information customers make available to retailers via social media, their browsing and purchase history and even searches - it becomes very easy for them to figure out a segment under which the customer falls and target them for specific products. This is by no means a trivial undertaking - it involves taking into consideration the user’s buying habits, search history, attitude, social sentiment, timing and even demographics in near-real time.

If you recall the infamous Target incident which used machine learning to sell the correct products to customers - that was an unfortunate circumstance, but the same technology can be used to target the correct customers at the right time and increase sales.

3. Cross-selling and upselling

Machine learning, along with analytics also helps simplify retailer operations at the back end and cross-sell and upsell. It does a lot of the heavy lifting and through analytics, retailers can find useful information to

• Offer best prices to customers based on past purchases
• Suggest complementary products for current purchase
• Recognize when a consumer might need a past product purchased again and provide more context-aware choices to them

This provides many cross-selling and upselling opportunities to retailers with a higher chance of conversion into actual sales.

Machine learning is the best way to predict buying habits of consumers and adapt marketing strategies as your customers evolve over time. Consumers these days have high expectations for personalization from retailers, and if your business truly understands their needs, they will remain loyal to you.

Contact us to find out how you can collect invaluable user data for your business and use it to devise a marketing strategy.