You’ve no doubt felt a massive transformation in consumer expectations over the past few years. Here’s a few insights from Adyen’s 2022 retail report that put these shifts into perspective:
61% of consumers believe that retailers must deliver the same omnichannel flexibility they provided during the pandemic, from curbside pick-up to brand-owned apps
70% of consumers won’t shop with organizations that provide a bad shopping experience—whether that’s online or in-store
73% of growing businesses have created unified commerce systems (allowing customers to complete transactions or returns seamlessly across online and offline channels)
In other words, consumer expectations for seamless shopping experiences are higher than ever.
“Every company today is a data and tech company, whether it realizes it or not.”
— Dr. Katia Walsh, Chief Global Strategy and AI Officer, Levi Strauss & Co.
Retail is changing fast—and data is driving that change. What does all this digital transformation and unification mean for business intelligence (BI) in the retail sector? In this article, we’ve gathered insights from top data leaders in the industry to answer that question.
Top 5 use cases for business intelligence in retail
1. Transforming the customer-retailer relationship
According to John Hughes, Chief Strategy Officer of The Modern Milkman, data has been key to transforming the customer experience and driving their runaway growth.
As a startup, the company took a risk by sinking nearly a quarter of their original funding into building a best-in-breed modern data stack. However, Hughes attributes their unique, collaborative customer relationship to their innovative use of data.
The Modern Milkman offers customers a supermarket alternative by swapping single-use plastics for more environment-friendly options and shipping those options right to your door. Using advanced data and analytics, they can track how much plastic customers have saved from landfills. And they allow users to drill down, so every customer can discover their exact contribution to the shared environmental initiative.
As a result, the Modern Milkman’s customers are collaborators, working together towards a shared mission. By embedding analytics into their product, the company has achieved every retailer's dream—a true relationship with their customers.
2. Using third-party data to optimize supply chain and improve CX
Abhi Bhatt is the AVP of Technology, Data and Analytics at CarMax. Bhatt joined CarMax after a particularly seamless experience selling his own car via their marketplace during the pandemic—in less than an hour. For him and his team, data and business intelligence is the foundation of building their outstanding digital customer experience.
Bhatt argues that the key to creating a transformational data ecosystem is to fully exploit both your internal data and third-party data to build a holistic view of both the business and the CX.
“Don’t just think about internal data in your own ecosystem, but also third-party external data—and how you can merge both of them to make things better.”
- Abhi Bhatt, AVP of Technology, Data and Analytics at CarMax
A rich and unified data set makes it easier to scale because you can use predictive analytics to validate your assumptions about supply and demand in the supply chain. For instance, CarMax analyzes weather data to optimize their transportation routes, ensuring a speedier delivery of their cars to their new owners.
This transition toward a 360-degree view of your business within the context of external, third-party data is being driven by the prevalence of cloud-based BI and analytics tools. However, Bhatt still believes that the true driver of data maturity in retail is cultural as much as it is technological.
“It comes from having the right mindset when it comes to data. Data is an asset, and you want to invest in the right set of technology that makes that data readily available to your end-users.”
- Abhi Bhatt, AVP of Technology, Data and Analytics at CarMax
3. Creating personalized in-store experiences
Adyen’s research report also found some insights that might surprise you:
41% have a new appreciation for being able to physically interact with products before they buy
59% still prefer to shop in-store
59% expect physical stores to create a meaningful or exciting experience
For Dr. Katia Walsh, the Chief Global Strategy and AI Officer at Levi Strauss & Co., digital data and AI augmented analytics solutions are crucial to the success of those powerful in-store experiences. Levi Strauss leverages BI is to create a personalized experience at every point of the customer journey—specially working smarter with what Walsh refers to as the three C’s:
1. consumer connections
2. commerce
3. creation
“No two Levi consumers are the same—why should the rewards that we offer in our loyalty program be the same? We use more data than we have ever had in the past and apply machine learning to that data to ensure that we provide exclusive, fully individualized benefits for each of our individual loyalty program members.”
- Dr. Katia Walsh, the Chief Global Strategy and AI Officer at Levi Strauss & Co.
For example, a Levi customer that loves music might get access to concert tickets, whereas a more fashion-forward customer might prefer early access to a Levi designer collaboration.
When it comes to retail today, Walsh says, “It’s not business—it’s all personal.” It’s about turning your customers into raving fans by using retail analytics to provide a fully personalized experience, both in-store and online.
4. Treating data as a product for your internal stakeholders
Sol Rashidi serves as the Chief Analytics Officer for the Estee Lauder Company. For her, treating data as a service for internal stakeholders is just as important as designing consumer-facing data products.
Retail is intensely competitive; acting as a partner to your internal teams sets them up to move much quicker. The benefits can be felt across the company, but specifically in the areas of sales forecasting, competitive analysis, and fraud prevention—all areas where retail companies rely heavily on BI.
“If you’re in an industry where there's always competition, you meet individuals and functional groups and tech teams where they’re like, ‘We’ve got to keep going! We can’t stop!’”
- Sol Rashidi, Chief Analytics Officer for the Estee Lauder Company
For Rashidi, the role of her Analytics team is to support a highly competitive, innovation-driven retail business with “data as a service.” You need to understand your data end-user, and then create data products that address the needs of those internal end-users.
By building data apps that put the business user front and center, Rashidi believes you can dramatically speed up and facilitate business decision-making. She calls this product-management-style approach to building internal data apps “data at your fingertips.” That’s where treating data as an internal product comes in for Rashidi and her team.
“I'd rather you move forward and make a decision because you did so based on the facts at hand. Whether it worked or not, is a totally different story. Because if something didn't work, guess what? We're all going to learn from it.”
- Sol Rashidi, Chief Analytics Officer for the Estee Lauder Company
5. Building a culture of experimentation
Chu-Cheng Hsieh, Etsy’s first Chief Data Officer, explains that Business intelligence isn’t just changing the game for customers. It’s also changing retail organizations by triggering an overhaul of the company culture from the ground up.
For ecommerce businesses like Etsy, Hsieh explains, the pandemic triggered “three years of transformation in three months.” In practical terms, that means Etsy has migrated their data to the cloud, built a new data stack with Google’s Big Query, and implemented a new BI platform. This is no easy feat.
But Etsy couldn’t stop there. In order to fully realize their data, it required a cultural shift. While Etsy has always had a highly experimental culture, their upgraded data and analytics approach gave them access to more reliable and consistent insights.
One practical example of how Etsy uses this real-time data is with A/B testing. Their access to improved business intelligence empowers their product and marketing teams to experiment at a higher velocity. Hsieh points out that this is especially important in a fast-paced sector like retail, because human instinct is so often wrong.
“You don't want to judge your success based on just [the] outcome. You should judge your success based on the decision quality.”
-Chu-Cheng Hsieh, Chief Data Officer at Etsy
Other B2C retailers can apply this same concept to their Amazon advertising strategy. By investing in BI tools to support thoughtful, data-driven decision-making, you can increase your probability of success, even when you’re working with uncertain or incomplete information.
BI is transforming retail—but the shift depends on data accessibility
From customer experience to logistics; from innovative new business models to more efficient decision-making—data is dramatically reshaping the retail landscape, and there are many business intelligence benefits. However, these transformations depend on a foundation of accessible, reliable data, coupled with self-service analytics tools in the hands of the front-line workers who need them.
For retailers, the outcomes can be exciting. Think:
Personalized experiences for every customer, both in-store and online
Physical stores where every inch has been optimized
Maximum employee productivity and efficient staffing
Measurable revenue growth
To get there, you need an intuitive, AI-Powered Analytics experience that empowers you to operationalize data and act on insights. ThoughtSpot lets anyone—store associates, brand managers, operations professionals, and merchandisers—use natural-language search to analyze billions of rows of data from any source in seconds. You can also embed those insights into current workflows, and trigger actions to achieve business results.
Start a 30-day free trial to see how ThoughtSpot can help your retail business realize the power of data.
