Data is the ‘oil’ that fuels identification, segregation, classification, and even prediction of fresh output based on inputs through machine learning techniques. For recommendation systems, consumer data is extracted from social media, websites, eCommerce portals, apps, and other channels to train machine learning models.

Machine Learning models employ different kinds of innovative algorithms to solve personalizing problems while scaling results for an ever-growing online audience.

Recommendation systems with machine learning use users’ behavioral, historical purchase, interest, and activity data to predict preferable items to buy. As a business, personalized recommendations can achieve greater customer engagement and consumption rates while boosting ROI significantly. As McKinsey reports, personalizing leaders such as Amazon, Netflix, etc. are generating a 15 to 35% increase in revenues through product recommendations.

In addition to higher revenues, here are some other long-term advantages of powering your business offerings with recommendation engines-

a) Attracts more traffic

b) Engages shoppers through diversity

c) Increases customer satisfaction and retention

d) Boosts conversion rates

e) Augments upselling and cross-selling efforts

f) Raises average order value and purchases, and more.