This discussion is about how Attensity is using unstructured data analysis to prevent churn in customer bases of Telecom, Consumer Electronics, and other verticals.
Sramana Mitra: Howard, let’s start with introducing our audience to Attensity. Tell us what the company does. Also give us some of your background.
Howard Lau: Thank you for this opportunity. Attensity is a Big Data company. The value that we bring is we ingest a tremendous amount of data. With the advent of social networking, a lot of the volume of this data that we ingest is obviously social data. It includes Facebook, Google Plus, and Twitter. As you know, Twitter has exploded onto the scene. At this point, it generates about 500 million tweets per day. We ingest that information into our system and then we analyze that information so that we can do query analysis based on topics defined by our customers. We have this listening post where we do the suggestion. We have this analytical tool that does the analysis and annotation of the data. Other times, there’s a specific tweet that our customers want to be aware of. We engage with that customer. We support that type of engagement model as well.
Sramana Mitra: Your customers are enterprise customers?
Howard Lau: We target the Fortune 2000. We’re working on verticals such as telecommunications, financial service, e-commerce, consumer electronics, and hospitality.
Sramana Mitra: It would be helpful to our audience to take one example from each of the vertical that you mentioned and do a use case.
Howard Lau: One vertical is in telecommunications. I’ll use two use cases for that. One of our customers is the leading provider of prepaid cellphones in the United States. They use us as a listening post. They target their customers using Twitter. They also mine the data that they get from the surveys as well. They want to be aware of situations as soon as possible so they can react. By using us as a listening post, they are able to effectively reduce consumer churn by 25%. The way that this works is that 25% reduction in customer churn translates into 25% increase in revenue. That’s a very substantial impact on bottom line.
Sramana Mitra: Let’s double click down a little bit on what kinds of information you gather from what you’re extracting and analyzing. Let’s give us a bit more color on that.
Howard Lau: Let’s say we’re talking about the level of happiness of a consumer – happiness with a service program or unhappiness with a customer satisfaction. By providing this type of information to the company, they can then either respond to that consumer directly or put in place programs to improve customer service levels or improve pricing points for some of the service programs. It’s about being aware and listening to consumers and being able to respond to them as soon as possible to prevent problems.