Many of my colleagues have recently posted articles on Consumption Sensing, a forecasting approach that focuses on anticipating consumer demand. This approach has become even more vital during the pandemic as consumers’ needs and shopping behaviors change dramatically. But the new challenges don’t stop there.
E-commerce Forecasting on Amazon
on June 10, 2020
| By Jasneet Kohli
|
Demand Forecasting
Five months ago, feels like five years, back in late 2019, I wrote, "CPG companies' online channel is rapidly growing … with sales expected to double in five years." My, how things have changed. In less than one month, there were 35% more e-commerce CPG buyers, Amazon could no longer meet its 2-day prime shipping promise, and I am still waiting for the desk that I ordered two months ago.
With growing eCommerce channels and increased online shopping behavior, omnichannel retailers are grappling with the increased complexity brought on, in part, by current events, but more so the anticipated need to combine online and in-store merchandising, especially for the “new normal”. Siloed decisions around allocation, promotions, in-season pricing, inventory transfers and fulfillment will fail to deliver the expected margin performance unless they address the interconnectedness of online and stores.
Personalization: Two Surprising Highlights from this Year's What's Hot Retail Tech Report
on May 19, 2020
| By Anthony Wintheiser
|
Personalization
Staying up to date on the latest retail tech and thinking through the possibilities and implications for our clients' businesses is one of the more exciting things we get to do. That's why we look forward to reading what's hot, what's not, and what's hype in Forrester's annual "What's Hot And What's Not In Retail Tech For 2020".
Crossing the Great Forecasting Divide Part 2 – Consumption Sensing is New Forecasting Frontier
on May 14, 2020
| By Patrick Smith
In my last post, I focused on how retailers and consumer products companies can triage through the worst of the COVID pandemic as consumers were adjusting to a stay-at-home lifestyle. I discussed using a hybrid model of humans and AI/ML-based approaches, delivering science-as-a-service, to bridge the gap and react quickly to the dramatic swings in demand.
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