First of all, to those who pinged me privately about this deal, now you know why I didn’t respond. Although I was eager to talk about it, I was under NDA. I am sure you understand. But now that it’s public knowledge, I’m excited to tell you how I feel about it. Today, I will focus on how I feel about this acquisition in general. In my follow up blog, I’ll give you my thoughts on the possibilities when you combine Lithium + Klout.
As you can imagine, I have strong feelings about this acquisition, because I am deeply passionate about the subject of consumer influence on social media. I’ve researched and written much about this subject in an attempt to advance our understanding and move the industry forward. So the Lithium + Klout deal is really exciting for me. Not only will I have a more direct contribution to advancing the science of influence, I get to delve into a boat load of new consumer behavior data.
Klout has over 500 million user profiles across the most popular social platforms (e.g. twitter, facebook, google+, etc.). They’ve collected all kinds of user actions and interactions—from retweets, favorites, +1, comments, replies, even bing search queries. To a data scientist, this is not just your ordinary data playground; it’s a data Disneyland. With this rich data set, we can answer more fundamental questions and understand consumer behaviors at a much deeper and more meaningful level—beyond just how to monetize them.
So how do I feel? I’m very excited.
A Data Scientist’s Perspective of the Deal
Beyond my personal enthusiasm, I feel that this is a smart move for Lithium as well. I can tell you 3 good reasons from the narrow spectacles of a data scientist.
1. Klout turbocharges our big data infrastructure
Lithium has invested heavily in building our own big data infrastructure, but we are traditionally an enterprise social platform. That means our technology infrastructures need to handle many things—real-time interactivity, security compliance, performance/reliability, etc.—beyond data processing. Despite our sizable big data investment, it’s been an incremental effort (i.e. like a ramp) rather than a big bang approach (i.e. like a step function).
Klout, on the other hand, is purely a data company. Their entire business and product offering is built on the processing of consumer social data. Their infrastructure is designed and built with the sole purpose to capture, ingest, process, store, and retrieve consumer behavior data. At the scale of 500M users, even when users only interact 10 times a day on average—an overly conservative estimate—across any one of the social platforms they tracked, that is 5B interactions per day. In fact, they are processing about 15B interactions daily. So, in terms of raw data processing capability, I must say that they are ahead of us, and I’m very impressed with their big data infrastructure.
This augments our data processing capability and allows us to leapfrog our incremental approach to building our big data infrastructure.
2. Klout boosts our data science talent
As I’ve written in an AdMap article, the most expensive part of any big data initiative is the talent—the human resources—required to perform the analysis and machine learning on your big data. This is partly due to the shortage of data science talent in the job market now. If you’ve been trying to hire data scientists, you’ll understand how fierce the competition is. This is exacerbated by the fact that Lithium is a b2b white-label brand that many fresh talents out of college won’t know about. Every so often I still get chemistry or biology students coming to our college recruiting events thinking we do something entirely different!
With the acquisition, we not only get Klout’s state-of-the-art data platform, we get the talented people who designed and built it. This greatly increases our data science talent several folds. Now, I have more peers, with whom I can discuss how to use boosting, bagging, or random forest to increase prediction accuracy, how to use Markov chain Monte Carlo method to estimate the integral of people’s behavior distribution, etc. So with the addition of Klout, we kill two birds with one stone—we get the infrastructure and the talent.
3. Klout is a consumer brand that people care about
Despite the controversies about Klout as a standard for influence, it is an algorithm that tries to score consumers’ social interaction—social capital. Although the algorithm has lot of room for improvement in my opinion, it is a decent first attempt at its scale. However, an accurate influence scoring algorithm must be adaptive anyway. So even if Klout has the perfect algorithm now, it will need to change in the future in order to adapt to consumers’ behaviors. So having a less-than-perfect algorithm is not really a concern for me. What’s important to me is that they are able to change their algorithms and re-process their data quickly, and their big data infrastructure certainly provides this capability. It would’ve been more alarming to me if they had the perfect algorithm today, but it’s completely hard coded with little flexibility to change, adapt, and re-process historical data.
Still, the most important thing is that consumers are using Klout. Regardless of the controversy of the influence score, it’s a good-enough first attempt for consumers. In addition to the 500M profiles Klout has, they are adding ~450,000 new profiles every day. Why would consumers adopt this service? First, it’s simple—simplicity drives adoption. There is virtually no additional work other than signing up and opting in the network you want to Klout to track. It’s personally relevant, because it’s a score about you—whether it truly represents your influence or not. Consequently, people care about it, for curiosity, vanity, or other reasons. Even those who recognize the score is incomplete will look at it occasionally. Yet most of them won’t bother to opt-out because it doesn’t cost anything.
This is critically important, because it means that Klout will continue to get more and more data about consumers. After all, what good is it in having the big data infrastructure and talent if there is no new data to store, process and analyze? Without the consumers who are generating the data, we will never be able to extract any reliable information or uncover any valuable insights that help brands better connect with their customers.
The magic is the combination of all 3—the data, the infrastructure, and the talent. When these 3 ingredients are combined, you can create an engine that turns consumer data into consumer insight, which is what brands want. Add those to our team at Lithium, and the opportunities are endless.
I couldn’t be more pumped about Klout joining Lithium, but not because of its score. Many people think of Klout as only a score (or an algorithm), but that’s just the final product. And when the final product isn’t good enough, they naturally conclude that nothing is, because the final product is all they see.
But if you are a data scientist, like me, you’ll understand that behind that number in an orange box, there are many ingredients that enabled the production of that final number. And to me, 3 of the most crucial enablers are
Although the final product—the score—isn’t perfect, Klout has all the ingredients and the 3 critical enablers. This means we can still improve the final product, and we will. But what’s more exciting to me is that we can actually leverage these enablers creatively to build completely new data products altogether.
That’s my two cents for today. Stay tuned for the next blog where I will elaborate on some of the new opportunities with the combined expertise of Lithium + Klout. Thanks for reading.
Michael Wu, Ph.D. is Lithium's Chief Scientist. His research includes: deriving insights from big data, understanding the behavioral economics of gamification, engaging + finding true social media influencers, developing predictive + actionable social analytics algorithms, social CRM, and using cyber anthropology + social network analysis to unravel the collective dynamics of communities + social networks.
Michael was voted a 2010 Influential Leader by CRM Magazine for his work on predictive social analytics + its application to Social CRM. He's a blogger on Lithosphere, and you can follow him @mich8elwu or Google+.
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