The driving force for the Social Customer era is the participation in communities both for social and professional purposes. From the structured social networks (e.g. Facebook and Twitter) to company-owned or company-sponsored communities used for support, sales prospecting, or research and development, through communities used internally for collaboration between workers – communities are showing up just about anywhere.
This change brings vast amounts of content generated by the communities. In spite of the extensive experience gained by organizations in the past few years dealing with large data sets and knowledge, the user-generated content still remains untamed. What to do with it, and how to leverage it for value, are almost as mysterious today as they were when we first began accumulating Knowledge in the 1980s. Organizations are struggling to understand how to utilize it and how to derive value from it. Alas, Content Management Systems and similar enterprise tools can help manage the creation and processing of structured content – but the largest problem still remains the unstructured content produced in these communities.
Realizing Value from the New Large Volume of Content
Consider the size of some of these communities: Facebook is close to 500 million people, Twitter nears 100 million, and a few of the corporate-sponsored communities have over two million members. The amount of content generated is bringing organizations that were already drowning in data from transactional CRM systems to desperate levels. They are now saddled by massive volumes of knowledge and feedback that makes finding the needle in the haystack look like child’s play. In spite of the amazing volume, the storage and management of the content is not the problem – storage space is cheap these days so virtually any amount of content and data can be stored for – well, as close to forever as we need to. The solution of cheap storage has given place to a bigger problem: what to do with it?
An organization wants to capture and leverage critical information from their customers’ needs and wants to deliver better experiences and products. On the other hand customers fear that their feedback is not being heard and used. To show customers they care about their opinion, companies must act on the feedback. Alas, given the volume, and short of scanning each entry posted in any community for useful information or data, how can they capture and act on this feedback?
Enter analytical engines.
There are two roles that an analytical engine can play in a community – they can either be used to monitor and report on usage, sentiment and trends, or they can be used to structure the unstructured.
Monitoring for the Sake of Monitoring
Social Media brought with it standard monitoring tools. Whether from Social Media Monitoring (SMM) vendors like INgage, Radian6, ScoutLabs, and Visible Technologies, or embedded within the products of other vendors, these tools are quickly becoming the “first line of defense” for the barrage of data produced. The ability to collect the raw data, summarize it and report on specific terms is valuable for organizations that are suddenly overwhelmed by these new channels.
These tools are used for monitoring specific words and phrases, brand mentions (or competitors’ brands), and people talking about industries or products. For example, during the TV airing of Super Bowl XLIV there was an analysis of brand mentions done by Radian6 and partners, called BrandBowl 2010, which resulted in the naming of a winner by number of mentions and “positive” (like or dislike expressions) sentiment. During the same event, another analysis done by MarketIQ contrasting Coke and Pepsi, aptly named the SodaBowl, also looked at mentions and sentiments for both drink manufacturers. Again, the conclusion was to which was more popular – they actually used the term “buzzworthy” – not who gained what from their different approach to promoting themselves.
While certainly entertaining, it yielded no value to the brands mentioned on the success of failure of their campaigns – just whether they were popular or not.
Although there is room for improvement in sentiment analysis, the near-real-time analysis of these events allows marketers to identify which communities are important to them, and which ones need further attention. It also allows them, for the first time, to understand immediately what effects their actions have and adjust campaigns and plans in real time –invaluable to improve the message and ensure a good reception by the public.
However, monitoring for the sake of monitoring yields limited value to businesses on their way to becoming social. Listening is the first step, but engaging with the customer and providing a return on their feedback is closer to becoming a social entity. Organizations leveraging analytical engines to find and structure this feedback are on a more interesting path to assess.
Structuring the Unstructured
Among the contributions to communities by their members there are very interesting nuggets of information, opinions, and suggestions that are often lost since there are no tools that can extract it, organize it, and use it. This information could be used to improve products, create better experiences, or to better understand the needs of the customers and prospects. Customers are more open in their opinions among peers than when being asked to complete surveys or participate in focus groups. This candor and openness often results in very valuable data – which is not always leveraged.
Analytical engines can find that information and structure it (create a data record from it), distribute it to the specific system that can utilize it, and keep track of trends and patterns on the data they find. Organizations use them to carry out actions like ideation (the creation of new products and services), feedback management (understanding how customers really feel beyond the surveys), social prospecting (finding more about their prospects and segments to target in sales), and virtual focus groups (leveraging customers’ opinions without formally convening a group).
Good analytical engines will automatically classify all the information collected (using an SMM – social media monitoring tool – is the best way to collect all this information) into different buckets, and analyze those buckets to generate insights. This categorized information in its raw form is somewhat valuable, but the use of workflows and databases to store this data and process it further yield very powerful knowledge for the use cases mentioned above.
Integration Rules the Analytics World
The most valuable output an analytical engine can produce is the ability to take different inputs, across channels and across functions, and use all that in search of insights. Organizations receive communications via email, chat transactions, online comments, surveys with free-text boxes, and many other methods. To focus the efforts only on the communities, because they are the “hot item”, leaves a lot of potentially valuable data un-examined. This data must be merged and integrated with the community insights for further analysis. Analytical engines cannot stop at simply producing a report for each community; they have to become a critical part of the platform used by the organizations to interact with and manage their customers.
This platform will then integrate the content generated by all channels and all methods the organization uses to communicate, and produce great insights that can be analyzed for different channels and segments, or altogether. This analysis, and the subsequent insights, yield far more powerful customer profiles and help the organization identify needs and wants faster and better.
Alas, the role of analytical engines for communities is not to analyze the community as a stand-alone channel, although there is some value on that as a starting point, but to integrate the valuable data from the communities into the rest of the data the organization collects and produce insights from this superset of feedback.
What do you think?This is the first in a series of sponsored research posts I will be writing with Attensity (cross-posted to their blog as well) to look at the value and purpose of deeper analytics on communities (i.e. beyond simply mentions and sentiments-like words and phrases) and social channels. Any ideas or areas I should explore further?
15 Replies to “Leveraging Communities through Analytic Engines”
You are spot on with the analytical engines and the need to parse all of the random pieces of “conversation” with all of the members of the customer community to get the information needed to understand the customer. If we must think like the customer then only by listening through analytical engines will we be able to grab all of the pieces of information we need and by share force of numbers, it is a daunting task.
You actually say it very concisely here: “Organizations receive communications via email, chat transactions, online comments, surveys with free-text boxes, and many other methods. To focus the efforts only on the communities, because they are the “hot item”, leaves a lot of potentially valuable data un-examined. This data must be merged and integrated with the community insights for further analysis. Analytical engines cannot stop at simply producing a report for each community; they have to become a critical part of the platform used by the organizations to interact with and manage their customers.”
Thanks for an interesting post.
Thanks for the kind words and the read. As an old-timer in Enterprise Feedback Management and other sources of feedback I probably take the old hammer-nail approach to all this social stuff and just see more feedback to process.
Maybe it is time for me to stop messing around with this new found model and go back to preach what I know: feedback rules!
Thanks for the comment…
Quite a nice theme i’ve followed today, first with Venessa Miemis blog post on networks and innovation (I read your comment as well) and this blog post on communities and analytical engines.
There really isn’t any more excuses when it comes to metrics, we can build the tools necessary, now it’s a matter of what you say structuring the unstructured where the fruit of innovation and consumer centric objectives are.
I think that feedback does rule, but it’s also the notion of user experience that gets feedback that is more then just surveys and questionnairs, and real time experiences.
One company that has begun something of a community analytics engine is GE’s Healthymagination.com which is a community for health topics of interest.
They created a user experience that entices feedback and does so in a way that provides feedback but as well as capturing the metrics, such as their ideas pool to share health ideas, capturing user’s in real time with their needs and wants.
it also has cooperative efforts with WEbmd for a detailed feedback questionnaire to prepar you for your visit to the Doctors, as well it has a section that brings in a variety of informational sources under one section.
The transparency of this is the metrics, that comes from this type of platform for GE, they are in the background capturing information, results, ideas, and filtering this information in their innovation model.
Feedback does rule, i think social media just stepped up the analytical engine game as well.
.-= Spiro Spiliadis´s last blog ..spirospiliadis: in-form-ation is like a mathematical formula you have to align it in the order it needs to be to arrive to an answer. A better one. =-.
my apologies for the delay in replying, not sure how your comments got lost in my queue.
Thanks for the comment, I think that communities that further the sharing of information and the message (like Healthymaginations) are essential for people to feel like they get something out of collaboration. Of course, the collection of insights and the analysis of the feedback is what essentially proves the value of the community to GE — they probably won’t say that is their real objective, of course.
Thanks for the example, it is quite interesting indeed.
Hi and thanks for the Radian6 shout-out! One of your comments really struck me – “However, monitoring for the sake of monitoring yields limited value to businesses on their way to becoming social. Listening is the first step, but engaging with the customer and providing a return on their feedback is closer to becoming a social entity.” – well said!
Listening is a great first step, but you’re correct- if an organization really wants to socialize their enterprise it’s only a first step!
Thanks for the read and the comment — I can see that Radian6 filters are working for you guys just fine 🙂
Sorry for the delay in approving, it takes an approval the first time a person posts, only when there are links in the answer. You should be all set to comment on my wonderful blog going forward.
Interesting you mention that, I am conducting research on a social media maturity model — and just about everyone starts at Listen and then the next steps (most people have four, a few three — no one has more than four, which is interesting in itself) is where it breaks down. I want to make sure that we move beyond the stupid statement that “to be social you just gotta jump in and listen” and its ugly cousin “you must engage” — same as monitoring, just to do it for the sake of doing it is not useful and the sheer magnitude of data and feedback coming back will scare most people into not doing it anymore.
Just doing my part to make sure people adopt social media and the rest of the social goodness.
Thanks for the read!
You touch on some great points above. I would love to see you explore the following in future posts:
1. What companies are doing to align the structure data to the unstructured and what the expected outcomes/uses shold be. For example aligning call center data to VOC and community data.
2. While you mention ideation, feedback management, social prospecting and virtual focus groups as possible uses for the communities and the data within them. I’d like to see you explore the necessary process and organization requirememnts to acctuall accomplish such tasks. Folks have been suggesting these 4 uses, but I have yet to see them implemented with success and at scale.
Thanks for the comment, sorry for the delay — long week.
I am certainly interested and working on what you are talking about in both your points, but as you probably now is the hardest part of aligning actions and metrics. I will continue to work on it, and as soon as I have an interesting case to share I will definitely do that.
As for your second point, I would imagine that you would have seen some examples of at least feedback management while in your previous roles (maybe even at your current one). Alas, I can imagine that the word scale means something completely different at the level you are using it.
However, one thing that I think is key to keep in mind is that segmentation plays a critical role in good community management. A community of 1,000,000 provides very limited value to anyone, versus a smaller and more manageable community that certainly provides a lot of value when properly segmented and targeted.
I think we are going to see derived value from communities increase as we see the size decrease and the focus sharpen. Not there yet, most companies cannot handle a single or a handful of communities — less along a large number of them.
If you have some time, would love to hear what you guys are doing at ATT in regards to community. Just send me an email if you have some time to chat, easiest way is through the link on top.
Many thanks for an exceptional comment — stay tuned as more on communities and analytics is coming!
Thank you for this interesting post.
I fully agree with you that “the role of analytical engines for communities is not to analyze the community as a stand-alone channel […] but to integrate the valuable data from the communities into the rest of the data the organization collects and produce insights from this superset of feedback.”
I see analytical engines as octopus-like tools (with 8 or more arms) to aggregate, monitor, structure, visualize and analyze data from a variety of internal and external communication channels and data sources. As with all tools, the value of analytical engines lies with the people using them. It requires human judgment and expertise to further analyze data, evaluate and refine insights, and turn these insights into concrete actions for improvement.
Given that we now have technology to assist us with monitoring, aggregating, structuring and analyzing data from various sources, who (or: what kind of team) should ‘own’ these tools? What skills are we looking for, and what kind of mandate and responsibilities should these people have to turn their insights into measurable business improvements? Alternatively: what kind of people/mandates/commitments are required to prevent analytical engines from becoming shelfware?
Thanks for the read and a great comment. And for agreeing. As a long-time proponent of the value of integrated data analysis (been writing about it since 2001 and thinking about since before then), I cannot emphasize enough how much better things are when we aggregated data and analyze it altogether.
Ah, you know about the lack of analysts that can handle the engines then.
I believe there is a very marked difference between people who can manage the tools and the output (i.e. statisticians and computer scientist, people who understand the relationships between data and reality) and the business stakeholders that run the business functions.
The business stakeholders, people who understand the relationship between the specific functions and what the data means, are in short supply. Is it a question of training or supply? Yes. We need to take seasoned business people who understand the functions and the correlations between them and the rest of the organization and train them in data management and data analysis. This is going to be a critical need in the coming years. It cannot be done by outsiders, unfortunately, since the knowledge of the specific business function and the organization’s travails is essential. You can hire a statistician to draw relationships between datum, but without the knowledge of which data to correlate, they would just be bringing you cute and empty information (e.g. 70% of people who use twitter use your product — but how do they get to the product? via twitter? something else? and are those among my client base — or potentials? and what are we doing already for that demographic? is the current campaign working? those questions can only be answered by people that understand both the data and how the company works — and that is just a simple example).
As for responsibilities – they will still need to respond to the needs of their individual business units, so this is part of the job description. Will they have to have their results tied to their compensation? potentially, maybe not — these are questions best handled once they get going and we can see the results. Incentives always make for better performance, but they have to be the right incentives.
Anyway, this is a very long conversation and there aren’t that many data points to pull from, so this is all opinion right now on the people we need and what they should do.
Very interesting question, will definitely keep exploring it.
Thanks for the shoutout to my SodaBowl post – hope you enjoyed it! Actually the company that created this analytic is not MarketIQ, it’s Biz360. We had called our blog MarketIQ, but we are changing that because it’s causing some confusion in the market.
I actually wrote a follow-up post to SodaBowl, attempting to examine which brand made a better decision (to use SM exclusively vs. SM + traditional superbowl advertising) – http://marketiq.biz360.com/2010/02/sodabowl-coke-pepsi-ads-social-media/
Also, wanted to underline the point you made about sentiment analytics just being a starting point for actionable analysis. Listening is first step, but it’s what you do with it that defines whether or not you will win.
Maria Ogneva, Biz360
Better late than never 🙂
Thanks for the read and the comment. I am very skeptical in saying that listening is the first step since a lot of people will be stuck there in their way to doing something with it. I preer to refer to listening as a necessary evil on the way to actionable insights. Listening can be very distracting if you don’t keep the end objective in mind.
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