Online retail is predicted to hit $480 Billion by 2019 and making effective product recommendations has become increasingly essential for eCommerce brands to drive revenue and customer loyalty at scale.
Recommendations are evolving, and eCommerce marketers are adopting machine-learning engines that tailor recommendations according to behavior, product popularity, and situational context. But it’s not as simple as plug-and-play: how do retailers know which recommendation strategy will ultimately work?
In a recent article featured in the popular site Retail TouchPoints, Dynamic Yield’s CEO Liad Agmon asserts that retailers must personalize their recommendation strategies and let the customers’ position in the purchase journey influence the type of strategy that will yield the best results.
By choosing a recommendation strategy based on the signals received from each customer, retailers can to extract the highest value from recommendations and realize the highest marketing ROI.
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The Naked Truth: Retailers have no idea which recommendation strategy will work