Customers like the in-store experience.
They want to meet kind sales associates, try out the merchandise for themselves, and work with experts to help them find the best items.
Online merchandisers are trying to provide as close an experience as possible.
Their new solution? Recommendation algorithms.
Programming on websites can record, track, and promote information which corresponds with the interests of each prospective buyer., which sells outdoor apparel and equipment, wanted to provide the most personalized customer experience possible experimented with two programs:

  1. Predictive Product Merchandizing tool
  2. Individualized Site Search

The first option would incorporate web behavior, clothing categories, and machine learning to produce individualized recommendations for visitors to the website. With more visits, the recommendations become more precise and accurate.

With the second option, prospects click in the search to find items which are currently trending. After the customer types in one item that he or she is looking for, the program will predict which item they are looking for.
In a test, the personalized experiences were served to half the site visitors versus the regular visiting experience for the other half
Marmot received a 13% in conversion rate. Not only that, but there was a 10% decrease in the exit rate and 13% decrease in bounce rates!
While websites cannot provide the perfect one-on-one experience which comes with customers meeting individual sales reps, technology is progressing to permit companies to streamline and produce a more individualized experience for prospects.
Want to learn more about recommendation algorithms, and we can use them to improve your next marketing campaign? Call me at (310) 212-5727 or email me at for more information!