Real Time Customization To Avoid Non-relevant Recommendations


Real Time Customization

How does a customer feel after seeing non-relevant recommendations / offers once she has logged in to her favourite e-commerce website. The recommendations are more or less based on her past purchases but why would one like to buy another keyboard if she has already bought one. Welcome to the world of Real Time Customization where different parameters will be used to provide a unique shopping experience to each customer. Just as the results of a search engine vary from person to person, the customized offer would vary as well.

Before providing any recommendation, Real Time Customization will consider several parameters like current trends, brand interactions, geographic location, preferences, demographics, wish-lists, interaction on social websites along with past purchases. Other parameters can be Items added to cart but abandoned, Dwell times (how long does the customer look at a particular item before going back and clicking another item) etc. Every visit of the customer would provide her a different experience as different parameters would keep changing on different visits.

An analogy is to walk in a physical store and approached by a salesman who is aware of the customer’s purchasing habits and will recommend her the items accordingly. Using all these parameters may seem intrusive to customer’s privacy but all this data should remain in the business’s servers. Making it encrypted and to ensure that this data is only used for the purpose for which it has been taken for will gain customer’s trust.

It is a win-win situation for both customers and the businesses. Customer would get relevant recommendations coupled with discounts prompting them to make a purchase and businesses would get an opportunity to maximize the sales  No doubt this would require a lot of data mining but there are softwares already working on it. For example, L’oreal Paris encouraged customers to try different shades of makeup in real time. The data collected while customers kept experimenting was used to personalize the offer given to customer.