Use A.I. to ensure complete personalization
Focus on the audience
Include user segment targeting
Make use of content similarity suggestions
There are many different methods to achieve greater personalization in eCommerce. Some online stores reach for AI, or artificial intelligence, programs that can handle a good amount of personalization.
Others use different means to determine and set up what they want their customers to experience. It all comes down to which type of technology they prefer to use. Here are the four most popular.
Use A.I. to Ensure Complete Personalization
Complete personalization is tricky because it requires the program (in this case, AI software) to create a profile for each user.
This profile consists of different things, such as:
Email address
User data
Purchase history
For this method to work, the store must require every shopper to create an online account containing everything needed to put together their profile.
Focus on the Audience
This tactic requires a bit of market research and reporting. Who makes up the store's target market? What do those people want to buy?
This information and other things, like ages, general salary amounts, geographical locations, and more, are all used to put together a picture of the store's audience.
A personalized focus, including suggested products, can be assembled to recommend things to them that they may like.
Include User Segment Targeting
For a program to use segmented targeting, they put together a series of profiles based on audience focus, but with a slight twist towards complete personalization. They break down the segments based on several factors, putting them into groups.
The personalized recommendations are then made to people who fall within each group.
For example, people who buy sweaters and are between the ages of 35 and 40 receive a specific set of product recommendations, while those who are between the ages of 20 and 34 but by that same sweater, receive different recommendations.
Make Use of Content Similarity Suggestions and Search Recommendations
Content similarity involves suggesting things to shoppers based on what they looked at previously and placed in their carts. For example, if they purchased a cardigan sweater, the site can suggest similar sweaters that they might like.
It can also take a slightly different tactic and suggest items that can be worn with their chosen sweater, such as jeans, a dress, or even a pair of shoes.
It all depends on what the store has in stock and what the AI intuits that the shopper might like.
Further, you should personalize content and recommendations based on the searches a user is actively conducting on your site. For example, if someone searches for "men's sweaters," it stands to reason that they should see related content, such as more men's clothing, as related links or products they might also like. (Click here to learn more about how implementing AI-powered, natural language search on your website can help you deliver a more personalized experience.)