3 Real World Case Studies on Machine Learning

Machine learning is evidently the ‘next big thing’ and businesses all across the globe and pertaining to all walks of life are feeling its might.

Machine learning has found its way into our daily lives because of its characteristic traits of effortless and quick information retrieval, and seamless database consistency.

What exactly is it about machine learning that makes it so useful?

The clear answer is its capacity to predict future outcomes for us. It uses statistics to find similar and repetitive patterns in vast amounts of data, something like finding a needle in a haystack but with a lot of efficiency.

With this in mind, let’s shed some light on three real world case studies on machine learning.

Machine Learning Case Studies

Zomato

Zomato is a food centric company that was founded in 2008 and some of its most distinguishable services include but not limited to restaurant discovery, online payment while dining, home delivery of food and seeking useful reviews about your potential favorite restaurants.

Zomato has only been on the rise ever since hitting the right nerve with the target consumer base, food is something that most people are passionate about. Zomato currently has around 2 lakh+ merchants and restaurant partners and has successfully built its empire in over 23 countries.

Zomato simply uses machine learning by applying the ‘model training’ and ‘model prediction techniques’. Remote APIs are used to make predictions which leads to a more robust system of experimentation and better models that impact the consumer base more positively.

It has 4 major elements that make up its Machine Learning profile - feature stores, feature compute engines, model store and API gateway. For example: Zomato increased its maximum feature store output to 18 million requests per minute which paved the way for 3x performance and latency improvement compared to last year.

Hellofresh

Hellofresh is a company that offers mouth-watering meals on a weekly payable basis with an added option of stopping whenever the customer feels like. While this may seem like an enticing prospect to garner more traffic, it also is a direct threat to customer lifetime value.

To address this very issue Hellofresh came up with Morpheus, an algorithm that combines weekly data to provide customer predictions with the help of machine learning.

Hellofresh has categorized its customer profitability in the form of Customer Campaign Value, as opposed to Customer Lifetime Value. CCV corresponds to the profit that each customer generates through each life cycle i.e., activations and reactivations. The sum of these CCVs impact the CLV in the end.

Morpheus uses each unique customer segment to make the consequent predictions. For example - A pre-existing customer’s information is available at the time of activation to further predict a future reactivation and this in turn is very different from the information regarding a new customer.

This level of personalisation is what sets Hellofresh apart from a lot of its counterparts in the market.

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