Businesses need to stay in front of their customers to stay competitive. Retargeting company FetchBack this month released an interesting recommendation-powered retargeting solution that helps merchants do just that by combining machine-learning technologies and shopping history to ensure that the products displayed in ads actually fit consumers’ interests.
A definition of retargeting is in order. Retargeting is a form of online advertising wherein ads are delivered to consumers based on previous actions that in the past did not result in a conversion. For example, you visit my website because you are interested in buying a digital camera. You don’t buy today but since I now know who you are I can display an ad to you later on perhaps when you are more ready to buy.
Fetchback is taking retargeting a step further by enabling its clients to recommend products that, while maybe not directly associated with what the user initially searched for, may support it nicely. For example, you came to a website searching for a digital camera and eventually bought one but now retailers can offer up associated products such as carrying cases, extra batteries, etc, even if they did not initially sell the camera itself.
Every merchant (at least those without their heads in the virtual sand) wants to increase the opportunities they have to make a sale and retargeting is increasingly getting attention as a way to accomplish that. The issue is really that too many of your site visitors (the one you’ve paid good money to attract) leave without taking any action. Retargeting helps to change all that.
“We are proud of our commitment to constant improvement here at FetchBack,” says Chad Little, President and CEO. “This goes beyond our industry-leading technology; providing tailored content and offers that help convert prospects to customers quicker. It will give our clients the ability to showcase more products, provide fresh branding and overall have more data and intelligence about what consumers want.”
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