Here it is, my presentation at the Open Conference in Milan at the Social Business Forum 2012. Great experience, great people, lots of insight and lots of new points of view about the evolution of topics like Social CRM, Social Business and Analytics. I need some time to handle all this information and to elaborate some thought to express on a new blog post (stay tuned cause outcomes will be surprising ;)). A big “thank you” to the deus-ex-machina Emanuele Quintarelli (that I called the Event Man), Emanuele Scotti and Rosario Sica and all the OpenKnowledge guys for their invitation.
In my speech I wanted to give to the audience – even if the topic was maybe a little bit “tough” – some information about a topic that, from a hype phase, is now consolidating and that I’m sure will bring lots of value to various aspects of your business strategies and, in particular, to Social CRM.
What’s it all about?
Big data is about technologies and practices that allow us to handle huge amount of data coming from a variety of sources typically in a stream structure. The phenomenon of social platform adoption and people collaboration together with a new set of antennas/sensors embedded in different products and contexts (first of all mobile phones and Rfid) let these data explosion become a reality giving new challenge to the companies hungry of infomation about their customers. The three drivers that typify Big data are volume, variety and velocity. These respectively bring with them business opportunities and risks as well: richness of information and lots of noise, completeness of information and too many sources to be handled, up-to-date information and risk of information loss.
Big data sources inside Social Business ecosystem
In a social business ecosystem where all actors exchange information of all kinds, everyone is characterized by three classes of attributes.
- Myself: all data typically defining my unique profile.
- Myworld: all data representing my experiences with products/services I typically use for personal or professional use.
- Myrelations: all data identyfing my membership to different communities (friendship, interests, hobbies, professional links, etc.).
Let’s get a Social CRM definition
How Big data are useful for Social CRM? Before we have to give a definition of Social CRM and the most appreciated one was given by the most famous CRM expert Paul Greenberg which highlighted the strategic perspective of CRM (social or not).
The shift from CRM to Social CRM
Starting from the three famous CRM pillars (collaborative, operational and analytical ones) we can see that shifting from a traditional to a social approach we have gained, for sure, more channels where conversations occur (direct to the companies or within people excluding a direct invovment of companies), new activities and multi/cross-channel operational approaches and, finally, what about analytical improvements?
The Big data funnel for Social CRM
Now we have the opportunity to start from the real human experience that, interacting with different touchpoints, leaves tracks in the shape of raw data (i.e. transactions, interactions through traditional channels, web & social, location-based). The challenge for modern companies is to take these continuos data streams, transform them in information (giving meaning to data) and make insight (extract piece of information that is relevant for the business cause is actionable).
Now we are plenty of “human” data
From a customer perspective the three classes of attributes Myself, Myworld and Myrelations can give us a really complete view on his essence (more than traditional profiling and transacational attributes).
What’s pratically changing?
Segmentation practices must change cause we can enrich our understading of customers through new information that characterize them not as simple targets but as beings that naturally express themselves belonging to communities (or tribes if you want).
So it’s time to really understand your people
Now you have the opportunity to really understand customers’ attitudes, experiences, emotions and opinions. You are more close to understand their real nature as human beings.
How can we handle it?
But there’s a problem. Big data, with their specific characteristics, make difficult to address our attention to the right information, and “right” means useful for your customers and your business. Mainly in the case of unstructured data handling (i.e. text) you need human intervention for decision making but because of our cognitive limitations we need more and more some help to support us in processing, understanding and selecting information.
What’s in it for me?
Thanks to descriptive and predictive analytics we have all the methods and analysis that can support us in many businessapplications, especially in a proactive way.
Can we trust Analytics?
But lots of companies are suspicious about analytics accuracy and think that only high values of accuracy bring significant outcomes. Wrong. First of all people confuse precision with accuaracy (http://en.wikipedia.org/wiki/Precision_and_recall) and that can create dangerous misunderstandings but, more than that, the impact of analytical errors strongly depends on the business contexts and priorities so it’s normal that you maight focus your attention only to some kind of errors and drop others depending on your business objective.
An example for Social Customer Service
Well, it0s clear directly seeing the slide that correct analytical handling of structured and unstrctured data can provide proactively more information at your CSRs in order to better serve your customers fulfilling their expectations and improving their satisfaction.
Great opportunities but pay attention to the issues
Finally you’ve to remember that big opportunities bring also big potential issues that you must address. First of all about policies (liability, security and privacy), access (not every data is transparent and available so it’s mandatory to find trustworthy approaches to incentivize data sharing) and technologies (framework like hadoop are necessary to support massive parallel and distributed application processes). But be sure to spread inside your oganizations an analytical culture and to find the best analytical talent that must have not only strong analytical skills but also great expertise in decision making to support top and middle management in this important phase.