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Recommendation Engines

Prospective customers have finite attention spans — making attention the most costly resource faced by any sales system.

How can you maximize the utility of this attention span? You could direct the customer's attention towards an offer of their liking. But how?

Data science can help. Using statistical and A.I. techniques, we can build models of customer needs and interests. Using these models, within the customer's finite attention span, we can increase the likelihood of suggesting a product that they may be interested in.

Oneirix Labs has written multiple such recommendation engines. Our engines provide accurate, fast suggestions of possible purchases to customers. The engines also help educate the customer about new products, based on subtle customer need discovery; a hallmark of good recommendation engines.

No two businesses are alike. Consequentially, though based on similar mathematical principles, no two recommendation engines are alike.

Understanding business fundamentals allows us to build more tailored customer models, thus improving recommendations.

Understanding business goals allows us to orient the recommendation engine to target diverse outcomes. For example, short term satisfaction of customer's needs may not be the only goal. Sensitizing the customer towards various possibilities that he/she is not aware of, but may be interested in in the future, can also be a goal.

Finally, the recommendation engines may be deployed in various ways: directly as autonomous algorithms on e-commerce websites, or with humans in the loop.