Big Data is changing the game around customer analytics, giving organizations the means to develop far better analytics about their customers than ever before.
Numerous examples, in fact, illustrate how high-performing and cost-effective Big Data processing can shed a world of new insight on customers’ wants and preferences.
Offering their insights in this podcast are Rob Winters, director of reporting and analytics at Spil Games; Davide Conforti, business intelligence director at Jobrapido; and Pete Fishman, director of analytics at Yammer. The discussion is moderated by Dana Gardner, principal analyst at Interarbor Solutions.
Listen to the podcast (26:43 minutes).
Here are some excerpts:
Dana Gardner: Businesses have been analyzing their customers for a long time. What’s different now?
Pete Fishman: We’re a cloud software service, and the data is big. Our data on the customers is now all living in a central place. By aggregating across companies that are using your software, you can get really significant sample sizes and real inference, both from an economic sense, in terms of measuring the lift, but actually because the sample sizes are so big, you can get statistical inference.
That’s the starting point for making analytics valuable and learning about your customers.
Rob Winters: For me, the problem space is extremely different from what I was dealing with a couple of years back.
I was in telecom before this. There, you’re dealing with 25 million people, and if you rescore them once a month, that’s fast enough. On a Web scale problem, I’m dealing with 200 million customers and I have to rescore them within 10 or 15 minutes. So you’re capturing significantly more data. We’re looking at billions of records per day coming into our systems. We have to use it as fast as possible, because with the customer experience online, minutes matter.
Davide Conforti: It’s absolutely the same story with us. We have about 40 million unique visitors per month now. We’ve grown by double-digits since our start as a startup in 2006. Now, everything is about user interaction, how our users behave on-site, and how we can engage them more on-site and provide them a tremendous ad-hoc user experience.
Winters: We’re primarily a platform. We do some game development and publishing, but our core business is just being the platform where people can come and find content that’s interesting to them. We’ve been around for about nine years.
We started out as just a Dutch [gaming] company and then we’ve acquired other local domain names in a variety of languages. At this point, we have about 50 different platforms running in about 20 different languages. So we support customers from all over the world. In a given month, we have over 200 countries with traffic onto our sites.
The entire business is changing, and you’re competing based off that customer experience that you can deliver. We have a couple target audiences: girls, young girls, 8 to 14; boys; and then women.
Fishman: Yammer is a startup in San Francisco. We were acquired about a year ago by Microsoft and we’re part of the larger Office organization. We view ourselves as enterprise social, taking this many-to-many communication model and making communication at your company much more efficient.
It’s about surfacing relevant knowledge and experts and making work lives better. I run an analytics team there, and we essentially look at the aggregate customer behaviors and what parts of our tool people are using.
This was a really revolutionary idea that our founders David Sacks and Adam Pisoni had, way back when Facebook wasn’t nearly as relevant as it is today. But we’ve leveraged a lot of the way that people have learned to interact in their social life and bring some of that efficiency of communication. They saw that these social networks would grow and be relevant in a private, secured context of your business.
Conforti: Jobrapido started in 2006 as an entrepreneurial challenge that Vito Lomele, an Italian guy, started in Milan. It’s quite a challenge to live in the online market in Italy, because talent pooling isn’t as wide as in U.S. or in other countries in Europe. What we do is provide job-seekers the opportunity to find their new job.
We’re an online job-search engine and we currently operate in 58 different countries with more than 20 languages. We’re all in this big headquarters in Milan with a lot of different nationalities, because of course, we provide the service in local languages for most of our customers.
Recently, we have been purchased by the Daily Mail group, a big media group based in London. For us, it’s everything from job-seeker acquisition and retention and engagement deals with constant quality and user experience on-site. We use our Big data warehouse in order to understand how to better attract and retain customers on the basis of their preferences. And we also use it to tweak our matching algorithm, which works more or less like a Google algorithm.
We crawl a lot of contents from different sources, both job boards and other job sites or directly in the working pages of individual companies. We put them together in a big database and, using statistical tools, we infer which kind of rankings our job-seekers are willing to see.
So it’s a pretty heavy data-crunching exercise that we do everyday on millions and millions of different sponsored or organic postings.
For example, if Yammer guys or if Spil Games guys want to hire a software engineer, they can directly promote their sponsored ads on Jobrapido without having to sponsor them on a job board. So we’re trying to aggregate and simplify the chain of job search.
Gardner: What was the problem you had to solve when it comes to getting at this Big Data for analysis?
Winters: For me the challenge was multifold. How do you deal with this data problem, with this variety and volume information? How do you present it in a meaningful fashion for employees who’ve never looked at data before, so that they can make good decisions on it? And how do you run models against it and feed that back into a production environment as quickly as possible, so that you can give those customers a better experience than they were ever getting before on your platform?
My problem was that no one had ever tried to do it in my company before. We walked in with effectively a clean slate. But as you start to bring in different data sources, you start with all the stuff that you know you’re going to need right away.
You start seeing needed links for other data sources. At this point, we’re pulling data from thousands of databases, merging with dozens of application programming interfaces. You’re pulling in your Web log data so that you can personalize for those folks who aren’t giving you registration information.
I wanted to share a video that I think can be helpful for your readers that deals with planning and executing a Big Data program. (http://www.youtube.com/watch?v=Ow76L0IEZNY) This video is based off of TEKsystems research and delivers the message in a cute way through multiple sci-fi references.