Thought Leaders in Mobile and Social: Clara Shih, CEO of Hearsay Social (Part 5)

Sramana Mitra: What other things have you seen in the social media performance measurement space in general that are worthy of note?

Clara Shih: More broadly in social media, outside of sales, the easiest is if you’re an e-commerce provider because you can trace directly the fact that someone clicked on a post or a paid ad inside of Facebook or Twitter. That’s the most direct way. There have been correlation studies done between the fact that consumer is a fan of your Facebook page or a follower of yours and on whether that’s an indicator of a more loyal client who spends more. Also, on the customer service side, there’s a number of companies now that handle customer service questions and issues over Twitter. They found that it can be more efficient, and it can actually save in terms of customer call center volume. >>>

Thought Leaders in Mobile and Social: Clara Shih, CEO of Hearsay Social (Part 4)

Sramana Mitra: To all of the connections, that’s not necessarily trigger-driven. The trigger is coming from one particular person.

Clara Shih: We can identify patterns across their networks. Insurance agents tend to have peer groups and sell to customers similar to their age range. You see these clusters of people, typically around 30 or 40, who all suddenly get engaged in the same amount of time. If you see many of the same signals in your network, you can share. Then, there’s just good general knowledge content that agents like to share like earthquake readiness, or winterizing your home before the storms come. There’s a general content that’s engaging to a wide population of people. >>>

Thought Leaders in Mobile and Social: Clara Shih, CEO of Hearsay Social (Part 3)

Sramana Mitra: Does that naturally break down your business into the kinds of insurance or products that you’re helping sell to? For example, marriage, engagement, or having a baby as opposed to cars or something that has a less pervasive presence on social media?

Clara Shih: A lot of people share photos of their new car also. Maybe, they’re more boastful. As a company, we have chosen to focus on insurance and financial services. I think it’s part of a broader trend that you’re seeing of the rise of vertical SaaS companies. SaaS is becoming a more pervasive delivery model. We’re able to provide much deeper customization for each industry and that allows us to focus and drive greater value to that industry. >>>

Thought Leaders in Mobile and Social: Clara Shih, CEO of Hearsay Social (Part 2)

Sramana Mitra: Let’s double-click on this example a bit. Let me ask you a few questions that will clarify how you do it and how much of this is viable. What is the assumption? Are we talking about running these kinds of trigger analysis on Jimmy’s already connected set of friends or are we running this trigger analysis algorithm on a broader set?

Clara Shih: That’s a great question. On Facebook and on LinkedIn, we run this predictive algorithm on your existing connections. This is due to privacy policies. In a more public social network like Twitter where you can follow people without them having to follow you back, we can apply this analysis more broadly. Part of what Hearsay does is, we train our users on how to authentically connect with more people on their network whether those people are existing customers or not. >>>

Thought Leaders in Big Data: Howard Lau, CEO of Attensity (Part 3)

Sramana Mitra: You mentioned that there’s also feedback going into engineering. Does that imply that you see bug reports on social media?

Howard Lau: It’s not so much bug reports. It’s something like product enhancement request.

Sramana Mitra: That’s for product management though, right?

Howard Lau: Yes, product management.

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Thought Leaders in Big Data: Howard Lau, CEO of Attensity (Part 2)

Sramana Mitra: You’re doing a sentiment analysis. If it’s a negative sentiment analysis, you’re trying to take action against that?

Howard Lau: That’s a great insight. It is sentiment analysis. At the most basic level, you’re looking for things like positive, neutral, or negative sentiment. Our engine allows us to dive in to another level of data. If what they’re looking to is negative sentiment, they can dive in to what is that negative sentiment due to. Is it due to coverage, pricing, or service level? It is in that fine level of detail that they can then respond to appropriately.

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Thought Leaders in Big Data: Howard Lau, CEO of Attensity (Part 1)

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. >>>

How I Built my Social Media Network with 1M/1M

By guest author and 1M/1M ambassador Javier Hernández

After almost three months collaborating with the One Million by One Million initiative, I can proudly say that reaching entrepreneurs and economic development organizations has been both exciting and promising. Not only because many of them were interested in joining our program or becoming our local partners, but because to they all together have significantly improved my personal network of contacts within the entrepreneurship ecosystem in my country (Spain) and globally.

Today I passed 1,000 new contacts, most of them entrepreneurs and/or influencers who either follow my Twitter account, are one of my LinkedIn connections or are my Facebook friends. How did I do it? Well, it wasn’t that difficult. 1M/1M came up with great resources and tips to make it happen. I will try to share with you all the steps I followed to reach such an astonishing number of new contacts.

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