What can be achieved?
A model like this one can classify potential customers by their likelihood to purchase a particular product. This can be integrated into to marketing and sales strategies.
A lack of knowledge about a customer’s propensity to buy leads to one-size-fits-all marketing, where time and resources are blindly allocated to all potential customers.
Opportunity for deep learning
Deep learning has the ability to find patterns in large datasets with complicated data-types. The model could for example use a combination of semantic analysis of text written by the customer, demographic information, purchase history as well as information about how they navigate the website to make a prediction for that customer’s propensity to buy.
How is the AI model implemented?
E-commerce is an example where the vendor typically has easy access to information about their customers. Once a propensity to buy score is established, this could be used to redistribute how discounts are handed out to customers. Customers with high propensity to buy require smaller discounts in order to make the purchase than customers with a low propensity to buy. This insight leads to higher sales and retention without any increased costs.
Another example of the benefit of a propensity to buy model is in business marketing. Business sales typically have longer decision cycles, higher average order values and larger influence of interactions with a sales team. Propensity to buy scoring will allow salespeople to allocate their time more effectively, again increasing sales and revenue without any additional costs.
A model like this would need historical data of demographics and pre-purchase behavior of customers linked to if a purchase was made.