“We wanted to understand which channel the customer would respond to better, whether that’s a text message or a phone call, or an in-store offer, so optimizing our process was a really important goal,” added Raghu. In need of a more robust platform to achieve the level of personalization they wanted, the CVS Health team once again began setting goals and exploring their options. “There’s an actual constraint around building additional hardware to support the scale that we wanted to get to,” said Michelle Un, Director of Enterprise Analytics at CVS Health. They built out an environment and launched their first personalized campaign to 1% of customers within a few months, but ran into roadblocks when they tried to scale from 1% to 5% because of a lack of processing power and physical data storage. So the predictor of the behavior could be unpredictable, which is why our data dimensionality leads to overfitting issues for any kind of machine learning model.”ĬVS Health kicked off its personalization journey in Hadoop. “It’s really hard to predict behavior because a customer could go to a convenience store just because he forgot the milk, or he could go to a convenience store like CVS to pick up his prescription and stop to grab some candy. “We’re not dealing with a typical grocery store customer,” explained Raghu Nakka, Sr.
#Cvs timing full
In 2018, CVS Health was ready to focus on personalization, but with 10,000 stores across the United States and a large number of microsegments full of unpredictable behaviors, personalization was easier said than done.
Understanding varied and unpredictable customer behavior
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