If your company is a well-known brand, somebody, somewhere is publicly talking about it. Right now. It may be on your own Social
channels, in an Internet forum, a blog or other user-generated content
site. Social Media Monitoring, which put simply is keeping a constant
eye on Social sites including Twitter, Facebook and hundreds of other
platforms to monitor what is being said about your brand, has become a
necessity for most large organizations, and it is an art and a science
to manage this well. Manage it badly, and you can have a catastrophic
image issue (i.e. the
2010 Nestle Palm Oil debacle
on Nestle’s own Facebook page). Handle it well, and you can cement a
solid relationship with existing clients and convert new clients to your
brand (i.e. HP’s little-known but truly brilliant efforts to provide
temporary replacement HP hardware to certain individual users on social
platforms complaining of broken computers).
From a commercial aspect, companies are increasingly looking at
Social Media to contribute to driving revenue, largely through lofty
concepts such as “engagement” and “conversion.” Social is unique in not
only the speed of the communication, but also the intimate nature of
content. In addition, and importantly, what companies must understand
is that in the Social realm, the customer controls the conversation. The implication here is a fundamental paradigm shift for Customer
Relationship Management and Marketing, to understand the customer on a
personal level, and to handle – with great sensitivity – both the
positive and negative sentiment expressed on Social platforms.
(Social) Business Intelligence
A growing and compelling new flavor of Business Intelligence is
attempting to tap into the unstructured content on social platforms and
attempt to structure that data into a format that can be analyzed and
mined using new methods such as Sentiment Analysis, which measures the
aggregate sentiment across user posted content.
Social Business Intelligence
uniquely sits in the convergence of Knowledge Management, Social Media
Monitoring, Collaboration, Social Networking, Analytics , Customer
Relationship Management (CRM) and Business Intelligence (BI).
Social Business Intelligence is at a unique convergence point between several key technologies.
First, there is an important roadblock to get out of the way. Today there
are a selection of tools to do everything I am discussing below in one
way or another. With a simple sentence I have rendered technology
irrelevant for the purposes of this blog. So let’s focus on what Social
BI is, how it is done and what it means because that is what is
important to business.
I’m not really a catch-word kind of guy, but this is Big Data in its
truest form. There are thousands of platforms and sites, of course, but
if we only talk about the current Big Guys (Facebook, Twitter and
Foursquare for example), this would add up to billions or trillions of
conversation segments over a given (even conservative) time horizon. To put this in context: that customer data warehouse you
have built over all these years probably doesn’t come close…
Social Business Intelligence offers both Internal and External Opportunity
There are both internal and external opportunities to be realized
through Social Media Business Intelligence, and many tools are evolving
to support these, some even going so far as to adopt a “Facebook-like”
or “Twitter-like” interface, mimicking social interaction and Social
Networking site features.
Social Business Intelligence applied internally to an organization
could be termed Social Collaboration. For example, certain tools might
feature collaborative review where colleagues can ask questions and link
those answers to specific reports, or collaboratively comment and
markup objects such as Business Intelligence ad-hoc analytics, graphs
or reports. This functionality to comment in real time on
powerful business intelligence (even if it is only based on Traditional
data sources that exclude Social Media data) has the potential to add
value to interpretation of the reports that companies produce and use
today to base key decisions upon, thereby potentially improving
decisions made from today’s Decision Support Systems. Many traditional
software vendors already have adopted such functionality.
Of course, where Social Business Intelligence as a disruptive
technology becomes particularly interesting is when we start gathering
and analyzing that unstructured user-generated content, or even more
compelling, when we combine it with our existing “traditional”
Enterprise Analytics environments. This empowers organizations to
produce new innovative products that target user segments more
accurately and respond better to customer support or relationship
development opportunities. The value of Social Business Intelligence
is not really “about” the frequency of words and phrases users post on
social platforms. The value is in segmenting, categorizing, mining and
understanding the aggregate of the users’ behavior, and the sentiment
of those posts across products, segments and channels.
Social Media has its own unique segments, which include Employees,
Partners, Influencers, Detractors and Advocates. We can analyze social
network traffic, understand and identify our segments, and tailor
personalized/semi-personalized interaction to individuals or one of
these segments, flagging key comments, monitoring Likes, +1’s, trending
subjects and use of hashtags, enabling rapid and targeted response to
user comments to avert public relations crisis, measure success of our
Social Marketing programs or capitalize on new opportunities.
It’s all about the conversation. And you don’t control it.
Again, companies need to understand that the customer controls the
conversation. However, the tools exist that can arrange and present
structured knowledge from unstructured noise, providing key information
input to areas such as Marketing and Manufacturing to be responsive and
agile, acting on data that correlates highly to real-life fact.
At its root, Social Media is about the conversation. This implies
new requirements for how to manage our link to the customer, and how to
most effectively target and market to them. Increasingly, consumers are
mistrustful of the marketing messages and advertising. They are more
likely to find more relevance and see more value in the reviews and
purchasing of their friends and peers.
Social Business Intelligence in Practice
I thought to finish, I would provide two examples that support the
claim that through mining user-generated content, we can correlate with
very high level of confidence, to known and validated facts.
Google Flu Trends
An example of single-source user generated content analysis is
Google Flu Trends. Google has been analyzing aggregated web search
terms to see if it is possible to correlate geographic frequency of user
search terms on Google’s search engine to real data on flu epidemics.
While I recognize this is not Social Media per se, this example is
very relevant to the argument that user-generated content can be tied to
sentiment and can also be used as a predictor for future events, when
we clearly understand and define the objective, then identify and
measure indicators supporting that objective.
Google’s site
http://www.google.org/flutrends/ca/#CA
provides up-to-current-day results to allow tracking of current and
developing flu incidents and epidemics. In addition, on this site there
are historical graphs over a multi-year period for regions around the
globe that prove, using known, validated historical data, that reality
and future events can indisputably be predicted by user-generated
content.
United Nations Global Pulse.
Between 2009 and 2011, the United Nations and SAS studied how Social
Media and other user-generated content from public internet sources such
as blogs, Internet forums, and news published in Ireland and the US
could be correlated to validated statistics and leveraged as a
compliment and an qualitative indicator of real-life events.
For Global Pulse, the focus was on employment status. To summarize from the document found at
http://www.sas.com/resources/asset/un-global-pulse.pdf,
the UN identified keywords indicating changes in employment status
(i.e.”fired”), level of anxiety (i.e. “depressed”) or economic
indicators (i.e. loss of housing or auto repossession, cancellation of
vacations) in order to monitor sentiment. The results were
astonishing. The analysis of sentiment allowed them to predict
increases in unemployment as much as four months in advance of an uptick
in unemployment claims with a 90-95% level of confidence. Further,
they were able to predict precisely, again with a 90-95% confidence, how
long after an uptick in unemployment that there would be an increase in
clear economic indicators in the form of talk of loss of, or negative
changes to housing, changes of transport method or cancellation of
travel plans.
These two examples underscore that user-generated content in the
social realm represents a new and potentially highly accurate source of
knowledge when tied to clearly defined objectives and supporting metrics
(leveraging appropriate keywords). Indeed, Social Media Business
Intelligence has the potential to facilitate very personal customer
understanding and when backed by a well defined strategy, to strengthen
the relationship with our customer, avert PR disasters and increase
customer engagement and conversion.
What are your thoughts? Is the world ready for Social Business
Intelligence? Has your company thought about imposing order and
structure to the chaos that is Social Media user-generated Content?
I believe that Web Analytics
is a complement to BI. It can be integrated into a dashboard, or can stand
alone to guide developers and webmasters to optimize content. It does have an
effect on our database architecture – we must adapt the design of the database
to integrate web data. But does it “change the game”? No – it
makes it more interesting. And as a Business Intelligence professional, I
welcome another tool that will add value to my service offering and to my
clients.
