Last time, I explained why I think acquiring Klout is such a smart move from a data science perspective. And it’s primarily because of the powerful enablers that come with this deal:
The consumers who are willing to opt-in their data
An agile, flexible, and scalable big data infrastructure (both hardware and software)
The data science talent who can use the infrastructure to derive insights from the consumer data
I also expressed my personal excitement about this acquisition, but I don’t think I did justice there, because I haven’t had the chance to talk about how we plan to leverage these enablers. Today, I’m going to put on my product hat and try to give you a flavor of the products we could build with Lithium + Klout. Keep in mind that this is not a roadmap, but they are ideas we’re exploring. Since the possibilities are endless, I will describe 4 in total—2 that are immediate on the top of my mind, and 2 more that is further out in the future.
1. Scores for Everything
Klout’s algorithm was originally developed to score people’s influence on social media. However, with Klout’s big data infrastructure, we can score other quantities of interest—engagement, interest, expertise, popularity, reputation, contextual influence, etc. Moreover, we can score these quantities for other entities in addition to people—products and services, brands, even places with mobile. These entities can come from a product catalog, extracted from entity extraction algorithms from conversations, or manually entered for tracking.
It boils down to developing new algorithms that use different signals to scoring these entities, since the big data infrastructure is already in place ready to crunch the numbers. With the talented data science team at Klout, I’m confident that we can develop algorithms to score anything. And with the rigorous validation framework I developed at Lithium, we’ll make sure that the scores are predictive and mean something substantial.
In addition to the signals that Klout is currently using to score their users, now we can augment them with social signals from the community. This provides more context, specificity, and interpretability to all the new scores to come. These signals can be derived explicitly from ratings and reviews of product, people, and contents they create, or implicitly from the conversations among members of a community.
2. Gamification with Portable Reputation for All
Lithium has a long history of success applying gamification within the community context. With Klout’s cross network profile, we can level up the game even further. This means reputation earned within a community is no longer limited to that community. It becomes portable and visible to the wider social networks, which is more relevant, personal and valuable to the users. This is how reputation operates in the physical world.
Since we can score different entities for different attributes, what’s more exciting is that products, brands, and even the place may have portable reputations just as people do. For example, we can answer questions like, which speaker has the best sound quality, and which is most desirable? Which brand has the best warranty, and which has the best customer service? Which park is most popular during Easter, and which during the week of your birthday? These new scores are all context specific. That means the same entity (whether it’s a person or speaker) may have a different score depending on the context.
I must emphasize that the reputation score of a brand (or product) is not the same thing as treating the social profile of a brand (or a product) as a person—which is currently done. They are scored with a completely different algorithm using different social signals from the community and the broader social networks. Such brand reputation scores provide a transparent benchmark for brands to see where their customer centric efforts stand among their competitors. The implication of this is profound. It means not only can we gamify consumer behaviors; we can gamify brands behaviors. And the consumers are the ones deciding what brand behaviors are desirable, what needs improvement and what’s acceptable.
3. Analytics as a Service
Once we’ve developed enough algorithms for processing different types of signal and scoring different kinds of entities, these calculations could be offered as a service.
In the data as a service model, you specify precisely the data you want from a set of available searches and filters, and the service providers will provide that data to you. Likewise, in analytics as a service, you specify precisely what processing and calculation you want to perform on the data from a set of available composable analytics modules, and the service provider will do the heavy number crunching and deliver the results.
To understand analytics as a service, we’ll consider an illustrative example in influence scoring. For example, you may have the communication data from your internal enterprise social network, and you’d like to find the top 10 influencers from that data set. Since this data is not public, Klout couldn’t have access to it. But if you provide the data (in a pre-specified input format) and specify the computation you want performed (i.e. score influence), then analytics as a service enables you to use the existing infrastructure and algorithm at Klout to identify the top 10 influencers within your enterprise social network.
Since these analytics modules are designed to be composable, you can chain them up by specifying a sequence of desired computations. For example, after you scored people’s influence to find the top 10 influencers, you can feed the results into an expertise identification module that tells you the expertise of these 10 influencers. As we perfect more algorithms, they will be added to the set of available analytics modules. So the number of new analytics you can create by chaining up pre-existing modules is virtually infinite. With enough analytics modules, analytics as a service provides brands the ability to perform arbitrary data processing tasks to address unique business inquiries.
4. Shared Value Network for Brands and Consumers
Since we score both the consumers as well as the brands (along with its products/services) on public social networks, we achieve full transparency on both ends. Brands can see consumers’ digital reputation, but consumers can also line up the brands (or products) and decide which could best meet their needs with trusted information from their peers
Consumers who are not shy to show their passion for a brand will be recognized and rewarded. They may get a Klout perk or a badge initially—which are extrinsic rewards. But as they level up through gamification, they could earn a digital reputation that is recognized wherever they go, by their friends, by the brand, and by other consumers—an intrinsic reward. As they continue to share their passion, brands can learn more about these passionate consumers and serve them even better.
On the other hand, brands that serve their customers well—customer centric—will also be recognized and rewarded. Consumers will give them more digital love and rate them higher among the competitors, which translate to more business. Customers will be more loyal, which means longer term business. Consequently, not only are consumers incented to share their experience with brands, brands are also incented to be more customer centric, so consumers have a good experience to share.
Traditionally, it is believed that the financial market is fairly efficient—prices on traded assets will reflect all available information in the market. However, consumer markets are far from efficient, because there is an asymmetric access to information on consumer transactions. Brands typically keep all the data on consumer transactions in their CRM system—completely inaccessible to other consumers. With Lithium + Klout, we can create a 2-sided platform where this data can be made public should any consumer want to share it.
As more information is shared, the shared value to both the brands and consumers increase; and the consumer market becomes efficient. Thus ensuring both parties are getting the service and value they deserve in any transaction. This provides a natural counterpart to CRM that has a flavor of vendor relationship management (VRM) , where customers are empowered collectively with access to data on other customers’ experience with brands.
These 4 exciting possibilities are some of the most obvious from the Klout acquisition. These are not going to happen tomorrow and there is lots of work ahead. But it’s fun and exciting work. As we watch and learn more about the market, these ideas will evolve and new ones may present themselves, too. But if these are obvious, what could be some of the less obvious outcomes? Who knows? Who could’ve imagined that Google will be developing a driverless car 10 years ago? Having the consumer data, the big data infrastructure, and the data science talent can open up a whole new future that we can only begin to imagine.
As Peter Drucker once said, “the best way to predict the future is to create it.” With Lithium + Klout, we have the necessary ingredients to create something truly extraordinary—something extremely disruptive and yet transformative. So let’s take our combined expertise and assets to create a better future for brands and consumers. That is why I’m so excited.
Alright, what’s next? When I started writing about this acquisition, I wasn’t planning to write 3 parts. However, as I was writing part 2, I felt something was missing—the human side of the story. I’ve already shared my reactions to the Klout acquisition from a data science perspective and from a future product perspective. Next time, I like to share something more personal. That’s the final part coming soon.
Until next time...
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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Thank you for the vouch of confidence.
I will definitely do my best to make something better for both the consumers and the brands out of what we've acquired. It's not going to happen tomorrow, as we have much work to do ahead of us. But I'm looking forward to the challenge. So thank you for being patient with us.
Thank you for the conversation here.
Look forward to seeing you again on Lithosphere.
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Thank you for commenting on my blog. I appreciate your honesty. And it is OK to be vocal about your dislike and distrust about anything. On social, specially, here on my blog, everyone is encourage to voice their opinion. That is the whole point of being social. So here is my attempt to address your questions and concerns.
I agree that the Klout score has probably been taken too seriously in the industry. However, I don’t think that is a problem unique to Klout though. Any startup in any industry will try very hard to get everyone to take them seriously. Lithium did, too, and so did many other start ups — in gamification, big data, mobile, etc. That is a necessary step to establish a business. That fact that people are take them seriously is a sign of success of their marketing—had to give them credit for that.
However, the fact that the industry is taking a score (as well as other new technologies—gamification, big data, etc.) too seriously is a failure of the industry to learn, comprehend, and understand what these new technologies really means to their business. Instead of looking under the hood and really understanding how influence (as well as gamification, big data, etc.) actually works, too many decisions in the industry are made based on marketing materials, competitive pressure, simply following the trend, and a lot of hype. This is precisely why I want to write my blogs on these topics—influencers, gamification, big data, etc. Someone has to provide a more objective view on what these new technologies really mean to business and how they really work under the hood.
Now, back to the topic of influence. First, I would recommend that you take a look at a post on some of the fundamental concepts on influence—What is Influence, Really? – No Carrot, No Stick, No Annoyance, No Trick. In that post, we cited one definition of "influence" from the Webster’s dictionary—"the power or capacity of causing an effect in indirect or intangible ways : sway." But what does indirect, and intangible means? This is actually very important because, it means that influence is something that people can affect, but not directly or easily.
If people can affect it directly and easily, then people will game it, leading to what I call the influence irony. If people cannot affect it at all, then there is no point showing people that score if they cannot change it. So a good influence algorithm must find that narrow balance that reflect people’s true capacity to influence others.
This is easier said than done. So occasional algorithmic changes are inevitable. If you actually write code, you’ll know that no one can gets any complex algorithms perfect in 1 shot—it’s usually an iterative process over many years to perfect and fine tune such algorithm. And these are complex algorithms that probably have probably over millions of lines of code.
The problem is, of course, it impacts the final score, which consumers see—sometimes quite dramatically. But there are ways to overcome the sense of having no control over one’s score. One simple way is by re-computing one’s historical data with the new algorithm, and showing both the score under the old algorithm and the new algorithm, so people can get a sense of what changes are due to algorithm, and what changes is really a reflection of their behavior. I must say that this is not a new problem and financial and business intelligence software have encounter this problem long ago; and they have develop best practices on dealing with these problems. What I’ve described is just one of the simplest way to address this problem.
To answer your final question, what’s in it for the consumers. The answer is Lithium provides the context.
Simply looking at a score (e.g. I see that your K=61, and my K=57) offers no context in how these number should be interpreted or compared. In fact, it gives the false sense that these numbers are directly comparable. Do those score means that you are more influential than me? Maybe, maybe not. The reality is that you are probably more influential than me under some context—some domain of expertise, to certain groups of people, in certain geographical location, etc., but not under another context. These scores are not really comparable at all.
What does Lithium’s community provide?—that context you need to interpret the score. You know well that gamification has a long history of usage in communities. When you give people a badge or when someone achieve a rank, that is not so different from getting a score. But community members have the context to interpret these rank and badges, because they are part of it. In fact, these gamification feedback creates a sense of trust in the community, guiding new members to trusted content and other reputable members of the community. With more social signal that is contextually specific, we can certainly improve the score. Moreover, we can even create a contextual influence score, which is probably better described as a reputation score. That is the simple answer.
OK, this reply is getting long. So I’ll stop here. In Part 2 of this post, I will talk more about our vision, which is to create a 2 sided platform that even the playground between consumers and brands—much like those in the sharing economy (e.g. airbnb, uber, sidecar, etc.). So more concrete answers to your question about what’s in it for the consumer is coming in Part 2. Stay tuned…
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First, thank you for taking the time to comment here. And Thank you @DayleH for the defense as well.
I remember those quotes very clearly. I was in New York, and I had a great phone interview with Charlie Warzel from BuzzFeed. Please look at the original report here and don’t take words out of context.
When I talked to Charlie, I was raising a problem that most influence algorithms face today—the fact that most of them can be gamed easily. I talk about this in my own blog post here, too—The Influence Irony – Influence Engine Optimization. However, I also suggested a fix to this problem in the following post—Adaptive Influence Model: Fixing the Influence Irony—hoping that the influence vendors (Klout included) would adopt this method. It was under this context that I surface my concerns for most existing influence vendors, because building a system that is bullet proof to gaming is nearly impossible—it is very computational intensive and would require computing infrastructures comparable to that of Google to building something close to such an adaptive influence algorithm. But that is not what we are trying to do with Klout’s big data infrastructure.
The Klout score is certainly isn’t perfect, even as of today, and I’m well aware of that. I haven’t change a bit about my perspective on Klout’s score being the standard of influence—IMHO it isn’t, and it (as well as other influence vendors) are still suffering from many problems I pointed out. But that doesn’t mean it can’t improve. More importantly, that doesn’t mean the machinery (i.e. the big data infrastructure) behind the scene, the talent, and the data they are continuing to collect are useless for what we are trying to do. Besides, we haven’t even talk about how we plan to use Klout’s big data infrastructure yet. That will come next week—part 2 of this post, so please be patient. ;-)
Every company have their strength and weakness just like people do. Saying Klout’s score isn’t perfect and therefore the company is worth nothing is as narrow-minded as concluding a person is worthless when he didn’t get straight A’s in school. So let’s not be prejudiced and embrace the future with an open mind.
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Thx for the comment.
Yeah, it's exciting isn't it. That is the ingenuity of our senior leadership team, and many months of hardwork of the due diligence team. I actually didn't do much. They deserve all the credit.
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