Customer needs have evolved considerably in the times of pandemic, especially when there is lesser physical interaction and more remote communication. In such times, customers associate themselves with brands that understand their needs. Brands must ensure that they reach out to customers through right content and right channels. Connection, nevertheless, isn’t an easy feat.
With the help of Artificial Intelligence, it is possible for the companies to anticipate the needs of customers and reach out to them. With the evolution in technology, there has been a considerable change in how AI can be used to influence customer behaviour and predict trends that affect their buying perspective.
How AI facilitates understanding of customer behaviour?
Artificial Intelligence tools can be used extensively to study customer behaviour. There are different stages of a customer journey, where AI can be put to use extensively to gain insights.
- Awareness Stage: AI subset called predictive analytics can be used to identify different attributes of customers. On the basis of this, it makes recommendations of products or services.
- Consideration Stage: In this stage, AI can be used for integration of huge amounts of data about the customers. This can aid customers on the website and assists in gaining knowledge about products or services, on the basis of which they can make informed decisions.
- Purchasing Stage: AI can help understand customers’ buying patterns by analyzing data collected during their purchase. Based on this, it can provide relevant recommendations.
- Support Stage: The after-stage is called support phase, where AI can study and monitor customers’ behavior by analyzing signals like ratings and reviews,
- Lastly, AI can be used to facilitate the right customer support by engaging in two-way conversation.
Let us take a look at a detailed example of how Amazon innovates the use of AI in influencing its customers’ buying behaviour, and offering excellent CX.
Amazon’s case study: How it uses AI to understand customers?
Amazon has been continuously using AI for understanding its customers better, especially in case of search queries and why a customer is looking for a particular product. They also use this to predict a customer’s context of that search.
For example, look at this image below. I searched for “Fairy Garden Stones” on Amazon.com.
My search results do not remain confined to “stones.” Amazon AI does not just read keywords like “Fairy Garden,” only but also predicts that I might be looking for other related products, such as these -
Not only does it influences my buying behaviour, but also gives me recommendations of related search and brands associated with my search, such as these -
While a recommender system works in different ways, AI and machine learning are used to give better search results on Amazon.com, which makes the overall shopping experience better. To make this happen, Amazon adopts a process.
The first step of this process is training its system for which an internal team builds a data set. For creating the data set, the team assembles a list of multiple contexts, on the basis of categories. These categories are divided into certain numbers of activities like running, reading, cleaning, gardening and so on. Apart from this, the context is also based on various kinds of audience categories, such as professionals, men, kids, women, father, mother, and so on. This Amazon data set then correlates a customer’s query with results (of products) on the basis of affinity score, which goes from 1 to 15.
Amazon has been encouraging the use of AI in enhancing customer’s shopping experience through its internal innovation. In the evolving and dynamic tech world, a lot of firms are still struggling on silos without realizing the true potential and benefits of AI.
Companies can capitalize on huge information through AI. Valuable insights churned out from enormous data can be used to offer personalized products or services to customers in targeted areas. The companies which take first-mover advantage of AI have a power to create an impact and influence customer behaviour.