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Machine Learning Can Help eCommerce Drive Customer Experience

Businesses today are going all out to spruce up their customer experience efforts as global studies undeniably link it to not only driving revenues and profitability but also to staying ahead of the curve and differentiating from competition. For eCommerce companies building great experiences for customers is not only about how to get them to buy what they are selling but also about how to get them to come back again and again and buy more.

The ever connected and technology savvy consumers today expect a consistent, convenient and seamlessly connected experience across – right from the time they start searching for a product, through purchases, leading all the way up to delivery and returns.

At the centre of delivering that quintessentially dynamic and compelling customer experience, leading up to customer delight, is data. Data is like fuel for eCommerce companies that can help them understand the customers, their preferences, buying behavior, shopping patterns, etc. Organizations are increasingly realizing the need to harness this massive data, understand it and analyze it in order to derive meaningful insights, which in turn, can help them create the right product mix, dynamic pricing, personalization, recommendations, search optimization and allied services.

Machine Learning could turn out to be the game changer for eCommerce businesses as they aim to become more data driven to drive better customer experiences. At almost every step of the user journey, right from the time the user logs into the online store to the time s/he receives the delivery, machine algorithms can be put to play at various junctures to create a more personalized, engaged, intuitive, safe and efficient experience.

Some use case examples of Machine Learning across the entire customer lifecycle from start to end for an eCommerce company include:

–       Intuitive search suggestions and easier product discovery with image-based search.

–       Speech recognition for conversational search

–       Hyper-personalized product recommendations that figure out what products the user is likely to buy based on his/her behaviors.

–       Dynamic pricing

–       Customer service bots that can tell when the shipment is arriving

–       Anti-fraud algorithms that determine whether the order is genuine

–       Optimizing delivery routes.

–       Predicting or forecasting the demand for products at an individual product and category level.

These are just some of the few examples. The use cases can only be restricted by one’s imagination.

Overall, a data driven approach is heralding a new phase for the eCommerce companies. Machine Learning’s potential to elevate nearly every aspect of this industry is now a reality that will soon become mainstream.

(Image Courtesy: www.isngs.com)

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