Artificial Intelligence (AI) algorithms have paved the way for the retail industry to gain great insights into consumer behaviors as well as other market needs. This digital transformation in the retail sector was accelerated as a result of the COVID-19 pandemic, making AI-powered technology a necessity for these companies. In the eighth episode of the Decisions Now podcast, we are joined by Mansoor Kazi, partner at Bain&Company, who works specifically in the retail space.
Co-hosts Rigvinath Chevala, EVS CTO, and Erin Pearson, our VP of Marketing, talk with Kazi who has clients ranging from Walmart to Food Lion, Harris Teeter, and more, as he shares an insightful conversation on AI, data, and analytics. Subscribe to the Decisions Now podcast today, you can find us on Spotify, Apple Podcasts, and Amazon Music among other platforms.
Let’s Talk Data
In today’s world – data talks. You know it, we know it, so does everyone.
“Retailers have massive amounts of data and it’s becoming more and more important in terms of driving value for the retailer,” Kazi said. “Because if you think about how consumers are shopping and what drives preferences, one of the underlying trends that we’re seeing is, from consumers is the need to have personalized, curated experiences.”
Data-driven solutions can help solve many challenges ranging from growth strategy, personalized customer targeting, pricing, and supply chain all the way to M&A and partnerships, all things Kazi’s team at Bain assists their clients with.
But data doesn’t stop, it doesn’t rest and there’s always more. Every day, we create about 2.5 quintillion bytes of data, a Forbes article said.
Retailers receive lots of data on customer behaviors from buying patterns, frequency of purchases as well as non-traditional data like location, credit card purchases and stores customers shop at outside of their stores, Kazi said.
Some of the challenges that come with these vast disparate data sets included:
-Retailers may struggle to organize/aggregate the available data in a usable format that would allow them to make decisions.
-These data sets could answer many questions and issues; however, retailers must prioritize the use cases they want to apply these to.
“I think the prioritization and figuring out what value it adds to the retailer, that kind of process, getting that right, allows the good retailers to use data to really extract value and the ones who aren’t so good at, they’re kind of meddling and struggling to figure out what use case you want to pursue,” Kazi said.
Chevala said a perfect example he sees of retailers not using available data is when he and his wife who have different loyalty cards for the same store get the same email promos, instead of personalized ones.
Kazi agreed and shared how his clients were able to leverage something called ‘adjacent product category’ using algorithms, where you get suggestions based on your buyer spending and purchasing. For e.g.: If x person bought Doritos, they may also like IZZE sparkling juice.
“If you’re going to wait to get a perfect data set, you’re never going to get started. And so, what we try to encourage people to do is, while you’re parallel pathing, getting, building your customer data platform and making sense of all the disparate data sources, what are the top five or 10 use cases that you could maybe attack in a version 1.0 now, knowing that the way you get to the insight may not be the most optimal,” he said.
It’s good to narrow down a few use cases to tackle first, and when teams get to the final platform or more robust version of it, organizations can refine these algorithms to refine the insights.