Retail
Big Data Analytics
Use Cases


 

Recommendation


This kind of analytics algorithm compare the past searches and purchases of the user with other customers with the same likes. Then it can suggest new items based on the products bought by similar users

It can be applied even when we don't have futher information about user previous behaviour, the so called "non personalized recommendation".

The typical approach suggest new product to our users, but we can look at the reverse path. For example, we want to promote a product, the algorithm can select the users with the higher conversion rates for a personalized marketing campaign

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Customer segmentation


Different users have different tastes and likes. A one size fit all marketing strategy will underperform and deliver a poor ROI

This kind of analytics algorithms has the ability to segment your customers in different groups that shares the same demographics characteristics or buying behavior. In order to create a personalized marketing campaign that will deliver the right message to the right user at the right time via the right channel.

 

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Propensity to buy


This algorithm can be used to estimate the probability of a potential customer to buy an accommodation or travel.

If a customer has a lower probability to buy, a discount could be added to increase the conversion rate

 

It could be applied also to cross-selling scenarios, for example offer different extras depending on the customer