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.
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. The first aspect of this is building or implementing hardware and software systems that can handle data at a large scale, and can make them available and respond at speed and scale.