By 2020, Gartner predicts that 85% of interactions between customers and retailers will occur without any interaction with a human. Most of these interactions will be machine driven or precisely data-driven. How can online retailers adapt to this demand?
For an e-commerce industry that relies on online sales, Artificial intelligence (AI) and Machine Learning (ML) are among the top technologies that can create a huge impact. Across industries, organizations have a bulk of data and are looking for ways to make their data work for them. Data science and machine learning provide them with the tools to do so.
To stay ahead in the curve, it is very important for the Retail industry to venture into customer data collection and analysis. It is very important to enhance their services and personalize the consumer shopping experience with a view of making profits. This can be done in various ways -
- Personalized product recommendations
- Product pricing strategy
- Serving customers
- Financial accounting and much more
In fact, in some instances, ML has made it possible to predict what you will be purchasing today, way before even you make up your mind. Today, customers are knowledgeable; almost every customer conducts independent research before entering the store to buy anything. Most of the time they prefer online shopping keeping in mind the offers provided and of course, timely delivery. These technologies have made personalized shopping experience possible in such a short span of time.
How ML is helping to have a personalized shopping experience?
We’re beginning to see how AI and ML can be incorporated into e-commerce, both to enhance the consumer experience, to attract users and to increase conversion rates.
After you know what the customer has browsed or purchased, it’s easy to present similar or complementary items to their potential purchase. Their history of personalized emails, notifications and ads can be a great source of attracting customer’s attention. Even the contextual data can be used to recommend products based on their location and weather. Not only products but recommendation engines can be beneficial for analyzing customer’s spending habits.
Retailers can come up with a good pricing strategy using AI by knowing the likely outcomes of different pricing thereby providing the best promotional offers. There is also a trend to keep customers engaged by delivering the most relevant content in every area be it programmes they watch or advertisements of their interest.
Data Scientists have started adopting strategies that increase the probability for users to subscribe to these. These strategies involve:
- Creation of models that help identify a set of users those are likely to contact a subscription
- Figuring out what is the best time to reach out to them
- Chatbots for customer service by recommending similar articles to which they already liked
Clearly, web-based buying is no fad; it’s a growing market a retailer must embrace if they want to survive. And a key requirement to ensure effectiveness will be tailored advertising that reaches the right individuals, at the ideal moment. No matter the tools you use in your workflow, one thing is for certain — AI and ML will continue to push innovation, communication, and interactivity to new levels in the Retail industry.
Machine learning principles have the ability to understand both shoppers and employees’ minds- by proving tools to deliver the personalization consumers expect.
Integrating AI and ML into your business strategy can feel daunting, but there are a number of advantages these technologies can provide you with as much as outcomes you’d desire.
So, it’s high time to integrate AI technologies across the entire sales cycle, from storage logistics to post-sale customer service.