Artificial Intelligence: A New Age of Personalization in Retail
From time to time, trade and commerce have evolved along with technology to benefit both the seller and the buyer. Eventually, the physical markets bustling with people are creeping into history books. All we need now is a digital device with internet connection, and what we want will be at our doorstep. In my previous blog, I have already explained how retailers enhance customer experience with effective personalization and how in the current times personalization in retail is of prime importance. With the tons of data available to the sellers, their success rate depends on how better they personalize the user experience. This is where Artificial Intelligence comes in.
Using the intelligence of the machines, a new era of marketing has begun. Virtual assistants can replace the salesperson and provide step-by-step guidance in buying a product. These virtual assistants can be more effective than humans, which will make the off-line retailers to either improve their off-line customer service or enter into the digital world and become an E-tailer.
Why is Artificial Intelligence needed for personalization in retail?
The data collected from reviews, surveys and other sources are used to recommend a list of products that the consumer might be interested in, thus achieving effective personalization in retail. The best example of such product recommendation is what we see in Amazon.
Amazon uses a technique called collaborative filtering, wherein, if you buy Product A, you might like Product B. But the use of this technology is limited to cases where we buy products in large quantities for lesser price, like household items. It does not suit well for products like TVs, vehicles and clothes, because it is complicated to determine the preference of each person.
For such cases, more advanced technology like machine learning and AI is needed.
Intelligent data-gathering for intelligent personalization in retail
The things a user does on the website turns into data – the pages viewed, viewing time, clicks and terms used for search. The bounce rate should also be analysed. When a customer leaves a website without making a purchase, it is called a ‘bounce’. Even the type of device (like mobile and PC) that is used to view the website can become a useful data, which is explained in the section below. The gathered data should be continuously updated in order to provide better service. For example, recommending a product that the consumer viewed last year will not be very effective. For enhanced personalization in retail, intelligent data gathering along with building of sophisticated framework is crucial. Data from both internal and external sources are needed to create the framework.
External data can be customer experiences from review pages of websites or social media. Even the sentiment analysis towards the product is important. The Facebook ‘likes’ and Twitter ‘trends’ are used to give personalized recommendations.
Internal data is focused on making a profile for the consumer. This data is important to understand the likes and dislikes, needs and wants of the consumer. Retailers use advanced algorithms to gather such data from online and mobile devices to gain knowledge of the consumer. These data can be as simple as the gender of the consumer to complex data concluded from their behaviour.
The personalization based on the visitor’s behaviour on website is good, but that alone is not enough. The patterns of these behaviours are needed to see if there is a repetition, so that the personalization in retail can be continuously optimised. By continuous optimisation, the right recommendation can be delivered to the consumer at the right time through an appropriate channel. This helps in providing even more personalized recommendations each time a user visits a website.
The overall data gathered will be immense, but that’s the way to provide better service and increase sales. It all depends on how well this collection of data is analyzed and used. The key is to form a network where information about each consumer across different channels is properly connected.
How does AI use this data for providing intelligent recommendations?
The toughest part is to integrate this vast amount of data that is collected. Though we all know that personalization in retail is essential but 95% of the data remains unused. All these data collected from various sources are integrated with advanced algorithms to build a correct profile for the consumer. As the data is gathered from different sources, each data needs to be linked to the correct individual. It is like solving a gigantic complex puzzle.
Around 60% of the retailers use rule-based approach to do personalization. For example, consider someone using a mobile to buy clothes through a website, say, Amazon. The geographical location from the mobile device is used to get the weather forecast for that particular location, and if it is likely to rain, the person could get a recommendation to buy an umbrella. But using an AI is more effective in case the person is likely to have other plans.
AI combines the data about the customers’ needs and the products details in order to frame a database structure as a base for recommendations. This helps in targeting the consumers more effectively, thereby increasing the sales. This also helps in better understanding of the consumers’ needs and wants, which will help in personalizing even the advertisements.
The effectiveness of using AI for personalization in retail
In January 2017, at the World Economic Forum Annual Meeting held at Davos, Switzerland, a virtual assistant called Socially Aware Robot Assistant (SARA) was introduced by the researchers from Carnegie Mellon University. SARA helped the attendees to find sessions they might be interested in and useful people to meet. SARA asked simple questions like ‘how are you feeling today’ and framed a conversation to determine the person’s mood and emotions. This gives a human touch to the services. Such technology could be a big use for the retailers.
In using AI, which is algorithm-based approach, we can focus more on behaviour of the consumer. Consider a person named Alex who is looking to buy a camera through a website. He clicked seven times on Canon cameras and three times on Nikon cameras. So it can be determined that he prefers Canon. So, showing him more options in Canon, along with few other products compatible with that brand, will be the best way of recommendation. This is an example of fine-tuned personalization in retail and a more thoughtful one. But after some years, Alex’s preferences may change. This time if he prefers Nikon, and views it more times than other brands, recommending a Nikon product will be more effective.
This is why AI is highly important. It not only helps to get a better understanding about the consumer, but also guides them along the buying process by providing the right offers at the right time.
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