Most of us have experienced the power of data-driven recommendations. Maybe you found a former colleague through LinkedIn’s “People You May Know” feature or you watched a movie because Netflix suggested it to you. And it’s quite likely that you bought something that Amazon.com recommended to you under the "Frequently Bought Together" section. It’s estimated that recommendation engines power approximately 30% of Amazon’s revenue. In all of these instances, recommendation engines help narrow your choices to those that best meet your particular needs. In all of the above situations, the systems that these companies built incorporate algorithms that learn from past data. Customers benefit from a more tailored and personalized experience, and this positive experience increases the likelihood that they’ll buy more products and services and stay loyal to the particular service provider or retailer in question. For the merchant or service provider, recommendation engines increase up-sell and cross-sell rates, reduce churn, and improve customer loyalty.
Use Cases by Industry
You might ask, “Does a recommendation engine have value for my industry?” The most well-known scenario for a recommendation engine is in the context of an online retail or e-commerce operation. Lots of data is collected about the online activity of users, i.e. clickstream or mobile data, past transactions and other behavioral data. All of this data can be mined for patterns and outliers to predict what the next optimal product or service should be for that particular customer. Most industries, including financial services, manufacturing, telecommunications, and media, have a portion of their business that is driven by an online channel where a recommendation engine is a great fit.
There are certainly other scenarios outside the e-commerce use case where a recommendation engine has great applicability. Let’s think about healthcare for a moment. If you look at the history of medicine, recommendation engines are what doctors manually apply on a daily basis—they look at patient symptoms and apply training and experience to provide the best diagnosis and treatment for the specific situation. Now, big data analysis offers the promise of personalized health care by analyzing vast amounts of information regarding an individual such as patient history, electronic medical records, demographic similarities, lifestyle information, etc. to produce disease risk profiles for an individual. Using recommendation engine techniques, hospitals are able to focus on preventive care, well-being and reducing re-admission rates, thereby improving the overall quality of care and reducing costs.
For a financial services company, getting a more granular view of a customer can help augment existing fraud detection techniques. For example, combining past purchasing history with behavioral data about customers can help determine if a certain set of transactions are outside the norm and require investigation. Other uses of recommendation engines include offering new products (cross-sell and up-sell of loans, refinancing, credit cards, etc.) in real time as users browse through an online banking portal.
The key point is that recommendation engines can be utilized in almost every industry to optimize and improve customer experience. According to a recent research study by Accenture Strategy (Jan 21, 2015), titled Customer 2020: Are You Future Ready or Reliving the Past?, only 10 percent of respondents felt that companies effectively converge interactions across digital, social, mobile and traditional channels. The report also emphasizes that in order to tap into new revenue growth potential, you must adopt new, customer-centric practices. The need for a recommendation engine solution couldn’t be greater!
Our Quick Start Solution
One of our recently introduced Quick Start Solutions is focused on helping you getting started with a recommendation engine solution. The template that comes with this Quick Start Solution includes data workflows, parsers and machine learning modules to help you get started in the implementation of a recommendation engine.
There are several MapR customers who have benefited from a recommendation engine. A great example is Beats Music, a streaming service that provides a personalized music experience for each user through a unique blend of digital innovation and a library of over 20 million songs. The key to their success has been the ability to analyze the high volume of data from users and offer music recommendations that are immediately personalized to them.
Recommendation engines are at the front and center of big data initiatives. Learn how you can get started on the path to deploying a recommendation engine and improve customer experience.