Check this out if you dare; I’m going to test drive this beast of a template tomorrow so if I’m not completely wrecked before Dr Michael Wu’s ‘Science of Social’ event on Thursday for Lithium, I’ll feedback my thoughts…
The Friendship Paradox
Feld’s friendship paradox states that ‘your friends have more friends than you, on average’. This paradox arises because extremely popular people, despite being rare, are overrepresented when averaging over friends.
Using a sample of the Twitter firehose, we confirm that the friendship paradox holds for >98% of Twitter users. Because of the directed nature of the follower graph on Twitter, we are further able to confirm more detailed forms of the friendship paradox: everyone you follow or who follows you has more friends and followers than you.This is likely caused by a correlation we demonstrate between Twitter activity, number of friends, and number of followers.
But wait, there’s more..
In addition, we discover two new paradoxes: the virality paradox that states ‘your friends receive more viral content than you, on average’, and the activity paradox, which states ‘your friends are more active than you, on average’. The latter paradox is important in regulating online communication. It may result in users having difficulty maintaining optimal incoming information rates, because following additional users causes the volume of incoming tweets to increase super-linearly. (And this also may relate to why in large complex communities personalized moderation works better than community moderation, as explored in my last blog post).
While users may compensate for increased information flow by increasing their own activity, users become information overloaded when they receive more information than they are able or willing to process. We compare the average size of cascades that are sent and received by overloaded and underloaded users. And we show that overloaded users post and receive larger cascades and they are poor detector of small cascades.
What are the dangers of overload?
Those users who become overloaded, measured by receiving far more incoming messages than they send out, are contending with more tweets than they can handle. Controlling for activity, they are more likely to participate in viral cascades, likely due to receiving the popular cascades multiple times. Any individual tweet’s visibility is greatly diluted for overloaded users, because overloaded users receive so many more tweets than they can handle. Because of the connection between cognitive load and managing information overload, the present results suggest that users will dynamically adjust their social network to maintain some optimal individual level of information ﬂux. (What does this mean for Facebook’s growth?)
Sorry if I jump around a bit in this blog post but by reading these points, and listening to the video, you’ll have a better idea of how social science can help you design a successful community, using a specific kind of moderation approach. Or at least how to impress to use the difference between a theory vs design-type approach to community building to respond better to new customer needs.
OK, I am paraphrasing here so bear with me, with me taking notes from Robert Kraut’s Stanford presentation above. My aim is to show how social science can inform good online community design. So the first point is that Kraut makes that I want to highlight is that real community design is “highly multidimensional”. And that this is at odds with logic of social science which seeks to understand effects of one variable at a time, while all other variables are else held constant, to discover causality. OK, so that’s some of the fundamentals sorted. Skip to this section on the video to hear the explanation.
This social science approach is at odds with (i.e. online community) design where you are trying to figure out the configuration of all possible variables to have the effect that you want to have. Kraut says that basically with design you don’t want one variable at a time you want ‘kitchen sink experiments which are theory-based experiments which you want to try out in a relatively cheap way.
But they use agent based modelling – allow theory to be tested as models in community environment, change member behaviour, which change environment (see 1:12:56) – where the ‘Identity Benefit’ is greater when agent’s interests are similar to group interests:
Here’s how to simply capture that ‘Identity Benefit’:
# viewed messages that match // # viewed messages
In comparison for the other principal type of community benefit to members Kraut identifies, the ‘Bond-based benefit’ is greater when there is repeated interaction. Kind of obvious I guess, but this is social science, so still worth stating!
Agent-based modelling and simulated communities results
And from simulated communities what Kraut found is that the simulated agent models (taking the place of community members) produced results very similar to that observed in real Usenet groups.
So the next step is that if we have a working agent model that shows how community works we can test out different types of moderation techniques, which can test in this simulated community.
From this Kraut found that ‘Personalised moderation’ out performs ‘Community level moderation’, though this really matters significantly when dealing with a large volume of content, or diverse content. In other words ‘Personalised moderation’ works well with large complex communities.
And as an example, I see this personalised moderation functionality appears to be available in community platform Telligent’s latest version of their analytics, which sounds useful. Be good to know which other major community platforms like Lithium offer such beneficial functionality, and how well it really works in the day-to-day:
Your community can now offer its participants dynamic and personalized recommendations of both people and content. Telligent Analytics looks at your community’s data, compares it with each member’s unique interests, and then delivers personalized recommendations to that member. Telligent Analytics doesn’t just tell you how your community’s doing; it applies the analytics to improve your community members’ experience.
So if you want to go into this study applied in more practical detail here’s Robert Kraut’s paper (pdf) with the graphs and stats:
A Simulation for Designing Online Community: Member Motivation, Contribution, and Discussion Moderation – (pdf: 10.1.1.141.6657)
Or maybe you’d like to read the chapter’s of Kraut’s 2012 book, Building successful online communities: Evidence-based social design:
- Resnick, P. & Kraut, R. Introduction [PDF]
- Kraut, R. E. & Resnick, P. Encouraging contributions to online communities [PDF]
- Ren, Y, Kraut, R. E. & Kiesler, S. Encouraging commitment in online communities [PDF]
- Kraut, R. E., Burke, M. & Riedl, J. Dealing with newcomers [PDF]
- Kiesler, S, Kittur, A., Kraut, R., & Resnick, P. Regulating behavior in online communities [PDF]
- Resnick, P, Konstan, J & Chen, Y. Starting a community. [PDF]
Included below is the Community Manifesto created recently at SXSW, but how useful is it for your organisation? For example, how well does it support emerging new forms of community management, such as ‘employee advocacy‘?
Consider the figures above in the context of the trend – with the 2012 Edeleman Trust Barometer – showing that for the first time regular employees had more credibility than CEOs. (And that was created by a jump of 16 points to 50%, was biggest rise for employee trust since 2004).
But then are you surprised the CEO’s position is eroded when you have middle managers complaining about the lack of initiative in co-dependent corporate cultures which punish initiative and reward obedience at every turn?
I recently contributed to the discussion on the community manager forum e-mint on ways to make money from your community, and still keep your members happy. Today I was contacted by Peter Belden at Extole with a way which I’d tried myself when I worked at Shopping.com after the impact of Google’s search algorithm changes. Namely using user generated content to improve your site’s search rankings, and thus your ability for people to find you and shop if you have a store. So without further ado, here are their 3 big tips:
There are three tips that marketers should keep in mind when launching C2C social marketing programs to help impact and improve their SERP:
1. Engage your customers wherever they are: Customers engage with brands across their websites, social networks, purchase and post-purchase environments, in-store and more. The most effective way to drive participation in a C2C program is to promote it across channels; via your corporate website, email blasts, and on social networks to drive the highest awareness, participation, and amplification rate.
By giving consumers the option to decide where they want to engage, you will drive higher participation and foster more creation and sharing of stories.
2. Make sharing easy: Make it easy for customers to share stories about your brand, products, and services with their friends. Include relevant sharing options (email, Facebook, Twitter, Google+, etc.).
3. Increase participation through incentives: Give customer advocates a compelling reason to share with their friends. This could be an internal offer (free goods, discounts, or loyalty points), gift cards, or charitable donations. Make sure there is an incentive for their friends to make sharing more attractive. With relevant and appropriate incentives, marketers will see more sharing from their advocates, which will produce more social signals that can be picked up by the search engines.
The Bottom Line – Cultivating Consumer-Generated Content About Your Brand Is a Must
By implementing C2C social marketing programs, brands can cultivate advocate sharing of stories with their friends and social communities. These stories will be amplified across the social web and pulled into search engine algorithms, which improves brands’ SERP. With the ongoing updates to search engine algorithms, consumers have increasingly more power over brands’ SERP, making one thing abundantly clear for marketers—harnessing the power of customers advocates to create stories about brands, products, and services is not a nice-to-have, it’s an increasingly critical element of brands’ marketing strategies.
I just ferreted out a community marketing plan I wrote for Shopping.com back in 2011 where I tried to sketch out a strategy to use community-driven SEO to drive revenue and fund incentives to attract more members. Have a read of how I tried to implement that strategy, or simply drop me a line if you want to know more in detail of how I can help your community:
Thanks to a re-tweet from Blaise Grimes-Viort I recall an aspect of this SEO community optimisation I suggested to the Shopping.com software guys at the time, which was to allow the community administrator to ‘tag’ reviews and guides with SEO friendly keywords. That way we could legitmately add value by making useful content more visible to customers via search engines. And we could use free tools like Google Adwords Keyword Tool and discussion with our head of SEO in light of business priorities, where to focus our efforts, not to mention where there are opportunities not spotted by competitors which make it easier to rank more highly for keywords. Hey, I even sat in a workshop with Google where they flagged up such opportunities for under-used keywords to attract people.
I don’t mean editing people’s text content to make it more SEO-friendly, which is hardly going to make community members more amenable to making contributions! Rather tweaking your software platform to allow for ‘tags’ to be added, much as WordPress does for this post. You could even auto-generate a set of suggested relevant tags for the community member to choose from, the way you approach it is up to you. But it does take some research, and it’s worth working with your head of SEO, or SEO consultant to get the right. For example in using WordPress tags for SEO I would heed this advice from wpbegineer:
Often people mistake tags to be like meta keywords for your blogs. This is the main reason why they try to add as many tags as possible. Tags are NOT meta keywords for your blog. At least not by default. Popular plugins like WordPress SEO by Yoast allows you to use your tag values to be in the meta keywords template. But if you don’t have these plugins configured to do that, then your tags DO NOT work like meta keywords.
If you’re here for the two examples of companies that improved customer service by allowing people (customers) to talk to people (employees), highlighted in red – and the 2nd example is in the 3rd comment. You can ignore the stuff about thinslicing:-)
To explain why I like the term thinslicing first take a look at the cool piece about data interpretation written today by Lithium’s Dr Michael Wu, including this neat illustration:
Then consider this, that my response to reading this blog post clarified a key thing I have been trying to say. Firstly, that I’ve come to term the business objective of finding the “interpretable, relevant and novel” in data as Michael terms it – through a combination of art and science – namely that of thinslicing.
But now I’ve made the next step. Identifying the value of thinslicing lies in the elegant and powerful way the term thinslicing connects the approach to data analytics to the behaviour that creates that data - namely with the thinslicing of online consumers who “tend to ignore most information available and instead ‘slice off’ a few relevant information or behavioral cues that are often social to make intuitive decisions,” as Brian Solis puts it.
But perhaps it would help if I made clear what I don’t mean by thinslicing as a strategic tool, is that summed by nicely in these two paragraphs written by Bob Thompson on the CustomerThink community:
“Despite our best efforts to collect and analyze data, good business decisions will always include elements of judgement, intuition or just plain luck. Many day-to-day decisions are made with little or no thought, because the option selected just seems “right.” Gut-feel decisions might be examples of what Malcolm Gladwell called “thin-slicing” in his provocative 2005 bestseller Blink.
“However, the best decision can sometimes be counter-intuitive. For example, the financial services firm Assurant Solutions wanted to improve its “save” rate on customers calling in to cancel their protection insurance. The industry’s conventional wisdom, which resulted in 15-16% retention rates, was to focus on reducing wait time to boost customer satisfaction. But data analysis found a solution that tripled the retention rate: matching customer service reps with customers based on rapport and affinity.”
What I mean is the approach to data as you outline above which I categorize as thinslicing, coupled with the way consumers make purchasing decisions – which like good business “will always include elements of judgment, intuition or just plain luck”.
In other words by thinslicing, rather than using intuition to make decisions, I mean adopting a strategy which is based on the understanding that by connecting the means of analyzing the data with the way the data is created by customers.
The question then is why? While it may be clever to see a way which logically connects the way to analyse data with the way it’s created, why is that potentially so useful to a business? Now there’s a good question. The obvious answer is that by aligning the analytic method used by your business, with the way the data is created by your customers, you are going to produce better results in terms of both better quality actionable recommendations which also produce an increase in ROI. How does that sound?
Update: so there’s a nice response from Dr Michael Wu on that question of linking the too together, the way you approach the data, with the way its created, that connects the two ends of the spectrum together:
Good data scientists must know everything that happen to the data, from its creation, all the way to the point where they get their hands on the data. It is actually a pretty standard practice for hardcore financial/business analysts. Not only you need to “connecting the means of analyzing the data with the way the data is created,” you must know everything that happen to the data along the way, until the data reaches you (or the analyst). Only then can you be certain that your analysis is not biased or confounded by something before you get your hands on it. In statistics term, only then can you know the confidence interval of your result.
In answer to a question on the community manager’s Yahoo group e-mint I came up with a quick suggestion:
One non-intrusive way might be to allow relevant companies like Sony access to your community for a set fee to ask questions for a set time, for example.
I know this can work as I have done community management training for a company in London which sets up communities on that very basis, for market research purposes.
I also know in the movie industry of a site like http://moviepilot.com/ which gives fans the chance to follow news about specific movies before they premier, and in return builds a fan base for those movies;-)
Forgot to add: A Vendor section in the forum can work well too. In order to keep the vendors contained though it should be stated at the beginning of the agreement that their own Vendor section will be the only place that they would be allowed to personally interact with the membership. I think a Vendors section can be quite helpful in many ways, including:
- Community members will have a direct avenue to the advertiser to ask questions about the vendors products and services.
- Allows the “vendor” to be in charge of moderating their own section giving them complete control over the type of postings/topics made in their section (within community guidelines).
- The vendor’s products/services are easily found within the community, but they do not infringe on discussions that are taking place elsewhere on the site.
I think the interesting concept that moviepilot.com offers though is for an enterprising agency to offer a service to companies to find relevant fans/influencers on niche communities. And to work with community managers to help generate income for them that helps sustain their growth. Kind of how Lithium currently sells the value of its community platform to potential customers, by making it the ‘hub’ for social commerce activity:
“Lithium helps you bring your static website alive with social conversations. Deploy Lithium Reviews and Q&A on your product pages to increase conversion rates and average order value. You can also draw customers into conversations by promoting Lithium Blog posts, Knowledge articles, hot Forum topics throughout your site.”
Which is great, but for a company like Sony it’s a case of ‘belt and braces’ of having both such a branded community, and also for specific campaigns being able to reach out to target communities to promote new products, ask for feedback, etc. And that’s why community managers can potentially both help their revenue and benefit their members.
I like the simple process outlined by Sony’s analytics agency Tempero on how they create actionable insights for clients:
Our process for insight is as follows:
- Liaise with each client to create a clear brief and need for the data
- Choose the most appropriate monitoring tool (different briefs need different tools)
- Create and test the search queries
- Review the data and manually clean it up (software tools are generally less than 60% accurate in terms of sentiment and classification)
- Analyse for patterns, trends and useful information
- Roundtable discussion with management team to assess the findings
- Create the output as defined in the brief
- Present to the client
- Amend and add additional information as required and re-submit
I think that the issues of information overload are also worth considering when evaluating your own social analytics needs. Bear in mind you have social savvv customers who make decisions based on word of mouth. But what is that? Is a simple way to cut through all the marketing and advertising and help decide if it’s what you want, explains Brian Solis:
“Based on the work of Robert Cialdini, I analyzed six universal heuristics and the role they play in consumer decision making in social commerce. Referred to as “thinslicing,” consumers tend to ignore most information available and instead ‘slice off’ a few relevant information or behavioral cues that are often social to make intuitive decisions.”
So imaging how powerful a tool you would have if it was able to direct social marketing efforts based on thin slicing, for example with the movie industry?
While Radian6 can provide such a technical capability to gather sentiment and identify influencers, and an agency like Tempero can provide the actionable insights, as a client you still need to be able to look at the data and find what’s really valuable, to consider “investing in the value, productivity and efficiency of consumer decision making” to quote Solis once more.
You need to be able to work with the tools and your analytics resource whether in-house or agency to properly tap into the hearts and minds of your customers, and get them engaging with your brand. As social media consultant Jeremiah Owyang says you need to: “Live in the same behaviors that customers and consumers are.” A thinslicing approach enables that to happen to optimise how you engage with the purchasing journey, as well as existing social analytics to track the ROI process from engagement to conversions.
You need to be able to overcome the ‘analysis paralysis’ and be able to ‘thinslice’.