5 Advantages Recommendation Engines can Offer to Businesses
In our previous blogs we had explained what are recommendation engines and the working of them. The main aim of implementing a recommendation engine is for the customer to buy more products. And if it doesn’t then it defeats the purpose of having a recommendation engine.
Now since a product recommendation engine mainly runs on data. Your company may not have the storage capacity to store this enormous amount of data from visitors on your site. You can use online frameworks like Hadoop, Spark which allows you to store data in multiple devices to reduce dependability on one machine. Hadoop uses HDFS to split files into large blocks and distributes them across nodes in a cluster. This allows the dataset to be processed faster and more efficiently than it would be in a more conventional supercomputer architecture that relies on a parallel file system where computation and data are distributed via high-speed networking.
Finally, we process big data sets using the MapReduce programming model. With this, we can run the algorithm in the distributed file system at the same time and choose the most similar cluster. Thus any organization can develop its own recommendation engine architecture using open source tools and we can help them in implementing the engine using our technical expertise.
Benefits of a product recommendation engine
You do not need a market research to find out whether a customer is willing to purchase at a shop where they’re getting maximum help in scouting the right product. They’re also much more likely to return to such a shop in the future. To get an idea about the business value of recommender systems: A few months ago, Netflix estimated, that its recommendation engine is worth a yearly $1billion.
Here are the 5 benefits that businesses can achieve using recommendation engines:
Revenue — With years of research, experiments and execution primarily driven by Amazon, not only is there less of a learning curve for online customers today. Many different algorithms have also been explored, executed, and proven to drive high conversion rate vs. non-personalized product recommendations.
Customer Satisfaction — Many a time, customers tend to look at their product recommendation from their last browsing. Mainly because they think they will find better opportunities for good products. When they leave the site and come back later; it would help if their browsing data from the previous session was available. This could further help and guide their e-Commerce activities, similar to experienced assistants at Brick and Mortar stores. This type of customer satisfaction leads to customer retention.
Personalization — We often take recommendations from friends and family because we trust their opinion. They know what we like better than anyone else. This is the sole reason they are good at recommending things and is what recommendation systems try to model. You can use the data accumulated indirectly to improve your website’s overall services and ensure that they are suitable according to a user’s preferences. In return, the user will be placed in a better mood to purchase your products or services.
Discovery — For example, the “Genius Recommendations” feature of iTunes, “Frequently Bought Together” of Amazon.com makes surprising recommendations which are similar to what we already like. People generally like to be recommended things which they would like, and when they use a site which can relate to his/her choices extremely perfectly then he/she is bound to visit that site again.
Provide Reports — Is an integral part of a personalization system. Giving the client accurate and up to the minute, reporting allows him to make solid decisions about his site and the direction of a campaign. Based on these reports clients can generate offers for slow moving products in order to create a drive in sales.
Sure, making an online sale is satisfying, but what if you were able to do a little more? An e-commerce organization can use the different types of filtering (Collaborative, content-based, and hybrid) to make an effective recommendation engine. It’s obvious that Amazon is successful at this principle. Whenever you buy an action figure, you will be recommended more things based on the content itself. For example, the DVD’s based on the action figure you just bought.
The first step to having great product recommendations for your customers is really just having the courage to dive into better conversions. And remember — the only way to truly engage with customers is to communicate with each as an individual.
There is more advanced and non-traditional method to power your recommendation process. These techniques namely deep learning, social learning, and tensor factorization are based on machine learning and neural networks. Such cognitive computing methods can take the quality of your recommenders to the next level. Also, with the recent rise in adoption of chatbots, it can be coupled with recommendation engines to provide follow-ups on products. It’s safe to say that product recommendation engines will improve with the use of machine learning. And create a much better process for customer satisfaction and retention.
