Using AI in Marketing & Sales
Propensity to buy
The "propensity to buy" is a value that indicates how likely it is that a customer will buy a particular product. Successful propensity-to-buy models provide crucial information on how to design and distribute marketing materials and how to allocate the time of sales personnel. In this way, companies can make their marketing much more efficient, resulting in higher sales without higher costs.
The Challenge
Lack of knowledge about a customer's willingness to buy leads to one-size-fits-all marketing, where time and resources are blindly allocated to all potential customers.
The opportunity for deep learning
Deep Learning is able to find patterns in large data sets with complicated data types. For example, the model could use a combination of semantic analysis of text written by the customer, demographic information, purchase history, and information about how the customer navigates the website to make a prediction about the customer's propensity to buy.
How is the AI model implemented ?
E-commerce is an example where the seller usually has directly access to their customers. Once a value for propensity to buy is determined, it would be used to redistribute discounts to customers. Customers with a high propensity to buy require lower discounts to make a purchase than customers with a low propensity to buy. This realization leads to higher sales and greater customer loyalty without increasing costs.
Another example of the benefits of a propensity to buy model is business marketing. Business customers typically have longer decision cycles, higher average order values, and greater influence over interactions with the sales team. Propensity-to-buy scoring allows sales reps to allocate their time more effectively, which in turn increases sales and revenue at no additional cost.
