Recommending products a user might be interested in is a cornerstone of increasing ecommerce sales.
Brick and mortar stores rely on attractive visual displays to encourage impulse purchases but an ecommerce store has something better: the personalized storefront. That’s an extremely indispensable part of your sales cycle.
The average ecommerce customer is spoiled for choice. There are so many options that navigating through them is itself a chore.
This tends to lead to choice paralysis where a customer fails to make a choice between multiple products because there are so many options they’re left incapable of determining which one is the best. This is where recommended products come in.
Personalize the recommendations
A lot of sites feature new, ending, or best selling products in their homepages and while this is alright and bound to include products that appeal to large numbers of shoppers, tailoring your options to every specific customer would probably work better.
The recommendation algorithm would base its recommendations on previous purchases, extensively viewed products, and a customer’s individual profile like their age, sex, and state of residence.
Relevance is the key
Some recommendation algorithms tend to keep recommending things that a customer has already purchased which can be silly. A customer buying toilet paper or pancake mix may be interested in making a similar purchase in a couple of weeks but someone who just bought a refrigerator may not be interested in another one for at least a decade.
Suggest accessories and compatible products
Some products just go well together:
- Someone buying a laptop may be interested in a webcam cover.
- Someone buying cotton shirts may be interested in laundry detergents
- Someone buying a bed or mattress may be interested in bed sheets, blankets, pillows, and duvets.