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.
Challenges
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.
Blending AI with Human Expertise
Data and analytics can help narrow down the options in terms of challenges, but it comes down to human judgement to understand what to do, Pearson said.
“There are so many decisions that can be made when you’re using AI, ML, or all of this data, but how do you also make sure that one decision that you’re making for one functional team doesn’t step on the toes of one of the decisions for another one of the teams?” she asked.
There are some cases where you let data drive the decision and some where you bring in human expertise to fill the gap, Kazi said.
Kazi shared an example from his experience working with an auto parts retailer to answer the question. He said that data helped them understand what parts for which cars they needed to stock up on, this data-driven insight helped land an answer that was 95% accurate. Data, however, couldn’t predict what customers who come in the store would ask for, this was when that prediction gap would be filled with human intervention and assessment.
“You’re never going to get it a hundred percent, but it gets you to additional value by closing that gap,” Kazi said.
When solving challenges, you need a combination of human and AI-powered decision-making.
“The human kind of understands the why behind what’s actually happening and can really apply that and that also helps it become even more so predictive and prescriptive for future efforts and being able to understand what’s going to happen and really understand new trends that might be emerging, that they can start to capitalize,” Pearson added.
Human intervention helps aid with the flow of processes and making sense of data sets.
“That’s right. I mean, you don’t want to get to a Terminator two-type scenario where the machines control everything. We know how that movie ends up,” Kazi joked.
Tips
Pearson asked Kazi for some success tips for retailers to use, when they kickstart their analytics-powered AI journey.
He mentioned three things that organizations can focus on:
- Make investments over time to fix the underlying technology infrastructure. Whether that’s building the data platform or fixing the underlying technology stack that allows you to be more nimble, more efficient, quicker in your decision making. It won’t happen overnight, but it is a multi-year journey. Don’t forget to have a vision.
- Getting started with use cases that you can show value and find some quick wins is needed. It doesn’t have to be the end state solution but finding uses for data in your analytics in your decision making early, will help you gain momentum within the organization to then put more investment dollars into it and have data be a bigger driver of decisions, or bigger input into the decision-making process.
- Pay attention to assembling the right cross-functional team. There is often a disconnect between the data analytics team and the end-user. The lack of a good cross-functional team, experts result in immobilized insights.
“Really understanding what’s the north star of the company, what are the big metrics that are going to impact it, putting together the process and the technology in place, and building a culture around that as well,” Pearson said, adding to the tips above.
Learn more about AI, analytics, and how retailers can leverage them, listen to this episode of Decisions Now podcast featuring Mansoor Kazi for some expert advice. Subscribe today!
