Recommendation Engines

Learn about your customer using Machine Learning and increase your sales. Upgrade your strategy by recommending the exact items that your customer needs. Overtime you will know more about the customer than anyone. This will help Give your customer and sales teams a delightful experience. Utilise your existing data and mine diamonds by letting the Machine Learning models process your up-selling and cross-selling opportunities for you.

What is a Recommendation Engine ?

  • A concept of artificial intelligence – a ‘Perfect Marketer Tool’ which gives insights based on user’s choice or interest about a product
  • It understands user’s preference or interest and gives recommendations based on their history of researches. It predicts how much you may like a certain product and gives a list of some best products for you.
  • It processes data through 5 stages such as collection, storage, analysis, filtering, feature engineering and then passes the enriched observations to the Machine Learning model for prediction

Applications

Recommendation systems are applicable in all industries where there is high customer interaction. This helps provide a great customer experience for your users.

Products/Movie/Music Recommendations

  • On the basis of earlier purchases/browsing history recommend certain items (product/movies/songs) by the user’s taste
  • This improves the user’s experiences! And this magic trick is done by the machine learning which recommends items and predicts what the user might want to buy, watch/listen to next.
  • Eg. Based on the earlier history the system will suggest related helmets for the user who is searching for a bike.

Social Media

  • Social Media platforms like YouTube, Facebook, Instagram, messenger work use “machine learning” exhaustively to understand user behaviour.
  • Eg. Facebook continuously monitors the people you connect with, the profiles that you visit often and suggests you with the list of Facebook users whom you can invite and become friends with.

News

  • Digital news platforms can only show  the content you want and provide up-to-date information with the current trends. It will also recommend highly relevant related news based on what you’ve browsed in the  previous week.
  • Eg. In medical newsletter if you search for ” high potassium foods to avoid” then it will also recommend you to read a related coverage of “nephrotic syndrome”/ chronic kidney disease(CKD)

E-commerce

  • Recommendation system in E-commerce makes online shopping easier.
  • It suggests ideas to users and provides them with information they need in order to decide on possible purchases by modelling the users preferences and interests.
  • The most import advantage of a recommendation system is that it suggests right items to the right customers making the shopping experience really relevant and personal for the users

Benefits

Recommendation systems provides value in numerous use cases. It is one of most useful method to apply machine learning in any generic situation or Industry.

  • Improves customers engagement.
  • Increase sale and revenue.
  • Improves Customer Satisfaction.
  • Able to recommend and find users with the same taste.
  • New product discovery
  • Right item to the right customer at the right time.