Leveraging Communities through Analytic Engines

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Guest post by Esteban Kolsky.

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 posts I will be writing with Attensity 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?

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