Machine learning is not replacing the humans in retail but it is making the overall retail experience better. Most of the retailers are afraid of machine learning and in making crucial marketing decisions. Machine learning is surely a game changer which doesn’t involve with the alteration of business communication with customers. For empowering more effective and smarter efforts, machine learning just informs better marketing decisions.
Without actionable insights, data will be nothing. In many ways, retailers can use machine learning for driving sales and making better business decisions.
Making individual shoppers take action with personalised promotions:
Before the invention of machine learning, the target audience was chosen based on the gender, age and purchase history by the retailers. A complete picture of an individual can not be determined with this data and it leads to the development of inaccurate strategies.By relying on these basic insights, retailers are wasting a lot of money. Machine learning will give you an accurate picture of what customers are expecting to buy. This helps to make the purchase again.
Pricing and Promotional strategies should be adopted by retailers constantly. Sometime they may miss because they make decisions which are not supported by the solid data analysis. With machine learning, retailers can go further and take into account other shopper behaviours, such as the general price range, preferred brand loyalty or style, to assess motivation more accurately and target consumers correctly.
For example, a customer recently purchased an expensive handbag from a retail store. The retailer could target the customer based on his gender and age for more handbag options. High-end handbags may not be purchased by every customer and other kinds of additional promotions may even annoy the customer. Retailers must consider that the buyer may have bought the handbag as a gift, and will not buy it.
Right decisions can be made possible with AI-based analytics and offer the right price and at the right time, for the right customer. For instance, the customer is interested to buy the luxury handbags only at a sale price but not with the full price. Machine learning can not only determine what promotions are optimal for shoppers to make more purchases but also when promotions must be offered. Instead of promoting the handbags immediately after the purchase, other accessories such as shoes, dresses are to be promoted which fits the customer style and range. Machine learning technology is used to pick up these patterns.
Data backed product launching strategies:
When launching a new private product or private label, promotional and pricing strategies are more complicated. Right metrics are needed for the guide decisions of a difficult process. When launching the products, predictive models are to be identified by Machine learning which also includes pricing and promotional strategies.The same retailers launching new boot lines can base pricing strategies on competitor data, but this is often a misguided effort that does not take into account how customers behave differently based on brand or product.
The main risk for retailers is setting the prices too low and losing profits, or too high and losing sales. The ideal price point of the new product can be determined by Machine learning analysis by eliminating human decision errors. This powerful tool gives utmost faith to the retailer and without that, it is almost scarier to take marketing decisions. Without the AI-supported data analysis, it is highly impossible for the customers to communicate with retailers. While machine learning platforms can be expensive upfront, they quickly pay for themselves through greater business efficiency that follows such as reduced speed to market. Because retailers ultimately have to join machine learning to stay competitive, it’s better to do it faster than later.
The machine learning platform has the ability to gain a deeper understanding of customers from time to time. This platform can analyze customer data along with the latest market trends to predict buying behavior, optimize prices, prevent customer churn, estimate demand, etc. Finally, machine learning automation makes it the most effective way to offer personalized promotions.
Automatic machine learning algorithms study the entire purchase history and recognize customer buying behavior. To motivate consumers to make more purchases, the machine learning platform then recommends retailers to promote lower, cheaper products and offer unique promotions through customer choice channels to drive a higher price range.
Machine learning is the best way for retailers to predict customer buying habits and adjust marketing strategies. Consumers have high expectations for personalization and will remain loyal to retailers who truly understand their needs.
Future of Machine learning in Retail Industry:
Artificial Intelligence will explain why certain products may become new trendsetters next year basically replacing human judgment to make qualitative judgments based on their own learning about how people perceive products. And AI will be more effective in doing it than any human. Tomorrow, AI will help make classic business decisions how much to produce, where to distribute, how to determine prices, how to promote, who will be targeted, and so on. The next border for in-depth learning moves from individual algorithms that are trained to perform certain tasks on a data set to a multipurpose algorithm answering various challenges at once.
