Identifying Target Audience through Behavioral Analysis with Machine Learning (ML) & Artificial Intelligence (AI)

Identifying Target Audience through Behavioral Analysis with Machine Learning (ML) & Artificial Intelligence (AI)

While running a promotional campaign, the first thing that a retailer or marketer thinks of is: “Who is the target audience?”. There are many number of possible methods that a retailer might use in order to identify the target audience and reach them with a particular offer. Defining a valuable way of identifying the target audience is crucial for the business as, it leads you to the target audience that can potentially let you generate the same/higher revenue with lesser resources.
Retailers cannot afford to spend on a marketing strategy that, isn’t going to be effective in terms of boosting conversions and increasing sales and profits. That means they need to spend their marketing budgets wisely. By truly identifying the audience that is more likely to convert, retailers can drive their business forward by spending minimal resources.

The present invention relates generally to a method for predicting repeat behavior of customers of an online store, and more particularly analyzing historical behavior of each customer to accurately predict future behavior of each customer and in turn assisting retailers, identifying the targeted customers for their promotional campaigns.

In accordance with the present invention, a method is presented for identifying the target audience for retailers, before they run a promotional campaign. The main objective of this invention is to provide retailers, a more reliable and efficient way of identifying the target audience leveraging Machine Learning (ML) and Artificial Intelligence (AI) so that, they can get a higher return on investment while spending minimal resources. Another objective of this invention is to provide means of identifying the target audience based on behavioral insights of the individual customers inferred through the analysis of historical data by means of Machine Learning (ML) and Artificial Intelligence (AI). The process of analyzing customers’ interaction with different retailers produces a predictive indication of whether the customer is a repeated buyer for a particular retailer. The proposed method is supervised, as the desired output corresponding to customer and merchant pair is known as the part of the training set data. This indication can be used as is or can be combined with other business logic for the final identification of the target audience.


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