How eBay Uses Data and Analytics to Get Closer to Its (Massive) Customer Base

Online auction site eBay uses data about the behavior of its millions of customers to drive analytics at every level of the organization, and get closer to its customers.

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Competing With Data & Analytics

How does data inform business processes, offerings, and engagement with customers? This research looks at trends in the use of analytics, the evolution of analytics strategy, optimal team composition, and new opportunities for data-driven innovation.
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You can find just about anything on eBay: A vintage BMW, a Lear jet, a half-million-dollar yacht. Or perhaps a domain name, industrial equipment, software and services from the likes of IBM, a food safari in San Francisco. Or even a previously undiscovered species, such as Coelopleurus exquisitus, a heretofore-unknown sea urchin sold on eBay.

The e-commerce giant has localized operations in over 30 countries, with 100 million registered users. The latest number of sellers listed by eBay in 2009 is well in excess of 1.5 million (it’s hard to tell the exact number of sellers, given buyers are often sellers and vice versa).

From all that activity stems a lot of data and, eventually, information — which eBay is capitalizing upon through the use of data analytics research. The results: eBay is much closer to its tremendous customer base than ever before, and it is able to iterate faster on fulfilling customer requirements.

In a conversation with MIT Sloan Management Review contributing editor Renee Boucher Ferguson, Neel Sundaresan, senior director of research at eBay discusses how the company uses data and analytics at every level to continuously evolve eBay’s numerous sites and services for buyers and sellers.

Can you talk briefly about how eBay uses analytics?

Analytics at eBay is used at every level and scale. A/B tests are common in understanding user response to site or feature changes, and policy changes. These tests can get complex, as the site has many complementary and competing features and policies. So one has to be systematic in ensuring that the experiments are clean, and also in reading the results of the experiments and in attributing measures of success to the changes. Then, if the result reveals a positive or a negative response, the algorithm or systems designers can take that information and update the models or design better algorithms, features or systems and the policy makers can revisit the policies. Data from these experiments can come in various forms — user behavior data, transactional data, and customer service data.

What would you say are the biggest technical issues that you’re facing with data today?

Everybody talks about big data.

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Topics

Competing With Data & Analytics

How does data inform business processes, offerings, and engagement with customers? This research looks at trends in the use of analytics, the evolution of analytics strategy, optimal team composition, and new opportunities for data-driven innovation.
See All Articles in This Section

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Comments (2)
Kimberly DeWald
I agree with Sunny.  This is not a poorly written article; merely it doesn’t provide any ground breaking wisdom.  No disrespect intended Renee.  Although I am interested in how the giants use big data, I am more interested in how small and mid size companies and corporations are dealing with the implementation and training of analytics on a restricted budget.  What forms of training have been implemented successfully?  What strategies have been used to encourage data education at different levels of management and individual contributors?  Are we encouraging/training managers to learn SQL queries?  What’s the most effective way to get management involved and educated on the use of all this data?
sunny.youn
this is from the Senior Director of Research at eBay???  Not sure anything was really thought-provoking or revolutionary was said about how eBay uses data to get closer to customers...