Amazon plans to acquire Whole Foods. Retailers and grocers have only had a couple weeks to process the news, and they still have many questions as they plan their competitive positions and strategies.
That news was still fresh (no pun intended) in my mind as I read the eMarketer Retail headline “Online Grocery Shopping Is No Longer Just a Millennial Story.” Twenty-three percent of households have purchased groceries online in the last three months, according to a survey by TrendSource cited by the article. That statistic may not surprise you. But the fact that 21 percent of baby boomers claim to have done so really opened my eyes and made me think of how rapidly the grocery space is evolving.
There is no denying it — if you as a grocer are not making personalized product recommendations, your days are numbered.
Amazon, along with other web-native powerhouses like Google and Facebook, has conditioned us as consumers to expect extremely personal experiences when shopping online. According to some articles, Amazon generates 35 percent of its revenue through recommendations. Seems like that is certainly one area that Amazon will focus on to increase the return on their planned $13.7 billion investment in Whole Foods.
If you are losing sleep trying to figure out how you can compete with the Amazon juggernaut, allow me to offer up a key piece of what you may be missing — our Product Recommender solution. The solution is built on a variety of advanced analytic techniques including collaborative filtering (which items are purchased together), frequent pattern growth (which sets of items are purchased together) and personalized stochastic approach to link structure analysis (what should I recommend to customer x based on what similar customers are buying).
If you don't know anything about these techniques, don't worry — we can guide you on which techniques are most appropriate and yield the best results based on your use cases.
In addition to powering your recommendations, the Product Recommender provides visualizations that help you understand the results of recommendation analyses.
If you’re interested in learning more, I’d be happy to discuss the Product Recommender solution or provide a demo. Feel free to send a note to email@example.com.
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