Enabling Enterprise Decision-Making with AI Analytics: Lessons from eBay and AbbVie

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In the era of data-driven decision-making, enterprises are increasingly turning to AI-powered analytics to unlock insights, streamline processes, and, ultimately, drive growth for their organization. 

At this year’s Gartner Data & Analytics Summit, we had the pleasure of sharing the stage with thought leaders from two different industries but with one similar journey—harnessing the power of AI to achieve impactful results. 🤝

The data & analytics vision

eBay’s Shruti Gupta, Senior Product Manager of Finance, heads up finance business intelligence and reporting for the ecommerce giant, which boasts more than 130 million buyers looking at 2 billion online listings (and counting). 

As part of their mission to “connect people and build communities to create economic opportunity for everyone,” Shruti explained, eBay turned to AI-powered technology with a few main goals in mind: to analyze its massive data lake more easily, to unlock new insights to help propel growth, and to enhance eBay’s overall shopping experience.

AbbVie’s Nandita Shetty, Associate Director of Commercial Analytics & Business Technology Solutions, focuses on commercial analytics for oncology products at the multibillion-dollar pharma firm.

AbbVie’s vision was to invest in a long-term analytics solution to support data-driven decision-making, aligning with a goal to “discover and deliver innovative medicines and solutions,” she explained, to ultimately “address complex health issues and enhance people’s lives.”

But using legacy data and analytics tools, both Shruti and Nandita faced the challenge of needing deeper insights while grappling with limited and time-consuming accessibility to data.

Here’s how they saw an opportunity to democratize data access, empower users with self-service analytics, and drive efficiencies across their respective organizations (i.e., they came, they saw, they conquered 💪💪).

The AI analytics opportunity

eBay’s goal was to enable self-service analytics for business users, allowing them to make data-driven decisions more swiftly, especially amidst giant shifts in the market, particularly during the pandemic.

Previously, using traditional, pre-aggregated dashboards, business users required a three-day turn-around time to file a request to the analytics team to get granular answers to their questions. While they were able to explore, for example, the gross merchandise value of luxury shoes in the last six months, they weren’t able to take it a step further: e.g., which brands, models, or prices were driving growth in this category? 

Essentially, they needed to move beyond just what happened and into why things happened—and then, how to build a strategy based on those growth drivers.

But with so much data, existing BI tools weren’t able to handle eBay’s scale. And data science tools were too complex and expensive, said Shruti, who explained that the firm needed a self-service solution that would ensure AI automation, scale, and ease of use; enable business users to get ad hoc answers and insights; and free up time for analysts.

Meanwhile, Nandita shared AbbVie’s deliberate approach to investing in an analytics infrastructure focused on democratizing data, empowering non-technical users (i.e., not burdening them with learning brand new technology), and boosting sales and marketing effectiveness. Another key goal was to cut operational costs by moving away from legacy data and analytics tools that came with hefty prices.

Like eBay, Nandita also noted the time-consuming nature of dashboards, which provided insights into only historical views, leaving business users struggling to see why things happened in order to effect change and improve outcomes. 

“It became very clear that we had to rethink our analytics strategy to adopt new technologies and ensure our teams would make better, faster data-driven decisions,” she said.

The solution: self-service analytics powered by AI

To make that happen, AbbVie’s users needed a solution that would put relevant information right at their fingertips, no matter their technical expertise. 

Using self-service analytics and natural language querying, the commercial team can derive their own insights on anomalies by asking questions in plain English narratives, while the sales team can get automated visuals for all of their ad hoc questions. 

These insights and predictive modeling, driven by AI and ML, enable the teams to perform advanced ad hoc analysis to address any business queries and help improve outcomes—e.g., show me growth in sales since last year, or show me new patient counts by territory this year—while significantly democratizing data access.

“That’s the true meaning of having data at your fingertips,” Nandita said.

She emphasized a three-pronged approach to driving this success: providing self-service analytics for field users, focusing on commercial operational efficiency and cost-savings, and enabling insights for commercial user groups. 

Another critical focus was on adoption, “the key part of change management planning,” she said. This involved “building a level of comfort to work with the data for non-technical users,” thanks to initiatives like search guides, tutorials, working sessions, and office hours. And, of course, don’t forget a human-in-the-loop approach—self-service analytics doesn’t negate the need for humans to ensure business context to provide the most valuable outcomes. 

On eBay’s side, rather than slicing and dicing answers in Excel and spending time creating visualizations and analysis in BI tools, teams can now answer their own business questions—e.g., how is the market share of brand X vs. brand Y each month? Or show me key drivers of brand campaign performance—using AI-powered self-service analytics on top of their data cloud.

“You can imagine the tremendous opportunities this has helped us unlock,” Shruti said. 

Specifically, Shruti outlined three key capabilities: highly accessible and explorable data analysis; AI-powered data prep, search, insights, and advanced analytics (AutoML); and newfound collaboration and analytical upskilling. 

For category management in particular, eBay has enabled the business to analyze shopping trends that are “critical to their category”: e.g., brand and price ranges for watches, authentication for handbags, or fitment satisfaction for accessories.

In other use cases, the finance team can now ensure that eBay’s books are closed “effectively, timely, and accurately,” while on the charity impact side, eBay can closely track who’s donating, how they’re donating, and how it’s making an impact to the global community. 🙌 🌎

The business impact

The AI initiatives led by eBay and AbbVie have certainly yielded significant impacts: eBay saw faster insights, streamlined analytics processes, increased user adoption, targeted growth in sales, and technology cost-savings. Similarly, AbbVie experienced operational cost-savings through consolidated analytics tools, increased productivity among field teams, and a “sense of empowerment” within their user community, Nandita said.

On eBay’s side, Shruti explained, “We were spending days just identifying what was driving the business and what the impact was. Now, our business team has direct access to that information. Within hours, they’re able to see what’s selling, who’s buying, and who’s looking for products to buy.”

Here’s a quantifiable look at the impact:

eBay: Targeted, cost-effective growth

✅ Streamlined analytics brings answers to critical business questions in under 2 hours, vs. 3 days previously. A 40% quicker market response rate helps enable the business to capitalize on emerging trends and new growth areas. 

10x faster insights, driven by AI, empower teams to identify strategies to increase sales in targeted categories.

✅ Consolidated analytics tools save eBay more than $500,000 each year. 

“What didn’t seem possible in the past is now available instantly in the hands of our technology teams, engineering teams, product teams, and business teams within a couple of hours,” Shruti added. 

AbbVie: Operational efficiencies and agility

✅ A higher user engagement rate on one single AI analytics platform results in an average of 400+ monthly interactions for users to gain insights and get answers to business queries.

✅ Automated analytics workflows save the commercial team 200+ hours monthly, reallocating their time to strategic initiatives. 

✅ A 60% increase in self-service adoption empowers the commercial team with direct access to data and reduces dependency on IT.

“Overall, even our sales operations team is getting more time on their hands because the number of queries coming back from the field teams is reduced,” Nandita said. “They can spend more time focusing on strategic targeting and planning, rather than responding to data queries.”

Embracing AI analytics for transformative change

This conversation highlighted the transformative power of AI analytics in accelerating decision-making processes, driving efficiencies, and enabling organizations to stay ahead in their respective industries—or, as Nandita put it, “seriously upping their analytics game.” 📈

Whether you’re an ecommerce powerhouse managing constantly moving market shifts (we’re guessing the buyer demand for, say, Beanie Babies and cell phone belt clips have shifted a bit over the years) or a pharma giant rolling out some of the world’s most commonly known lifesaving medicines, enterprises are increasingly using the power of AI to drive sustainable growth for whatever opportunities lie ahead—and whatever critical data analysis needs will follow. 

Want to see how AI-powered self-service analytics can democratize data access and transform your business? Check out a free trial of Tellius, or schedule a customized demo.


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