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Into the mainstream: Socialytics for effective CRM

21st Feb 2011
MyCustomer.com

Michael Fauscette provides a guide to socialytic solutions - including their potential impact on sales, marketing and customer service.

Recently I've seen a profusion of articles about online information and its exponential growth as a direct result of the internet and specifically the adoption of the social web. User generated content on blogs, wiki's, microblogs, video sharing sites and social networks adds to this mountain of data minute by minute, all day, every day. This explosion of big data offers big opportunities along with many problems for storing and for turning the data into useful information.
Analytic applications, data warehouses, business intelligence tools and executive information systems are experiencing rapid growth in the enterprise for dealing with corporate data, but what about all of the social data that is being created? How can businesses leverage this data to drive business improvements, executive decisions and competitive advantage?
Traditional analytic tools are very adept at analysing structured corporate data but data from the social web is inherently unstructured. This lack of structure creates big problems for traditional tools and has led to the development of other types of tools for analysing unstructured or semi-structured data and in some cases specifically for use on social data.
Noisy data
For analysing unstructured / semi-structured enterprise data in general, text analytics and data mining are well established, mature techniques with a robust and diverse set of tools. Text analytics works well for more 'formal' text like business documents, but social data like email, instant messaging, SMS, activity streams, blogs, microblogs, wiki's, etc. is often very informal, which can pose problems for traditional text analytics engines. This informal data differs from the standard form of the language and is termed 'noisy' and has given rise to new types of text analytics focused on extracting structure from noisy data. 
Social data holds a great deal of potential for business use and has led to a new class of analytic tools that the technology analyst firm IDC is calling 'socialytics'. Socialytics is a set of cross-discipline solutions that are used to analyse social data created through socially-based interactions. Socialytics includes tools and applications that are either purpose built to analyse social data or may act on social data in a more generalised fashion as another form of data input. 
Socially-based interactions create social data that reflects the relationship of people to people, topics, ideas or locations. Social interactions may occur within employee, customer or partner-based communities, corporate communication tools like email and instant messaging, public communication tools, as well as within public and private social networking environments and forums. 
Public and private
Socialytic solutions generally have an analytic platform and applications that are built for a specific function or functions. The socialytic platform provides the foundation for the different socialytic applications and provides a method for collecting the social data, usually through what is referred to as a listening post. The listening post can collect social data from whatever source(s) the business defines, which varies based on the intended purpose of the analysis. It's important to note that privacy and permissions play a key role in the type and use of the data. On the public social side, only data that individuals have made public through permissions can be collected and used to prevent privacy violations.
On the private social side, permissions are still important but in some cases might be implicit based on other corporate policy. For example, most businesses spell out corporate ownership of company provided email and instant messaging in human resources policies and therefore could collect and analyse this data without explicit permission from the employee. 
Socialytic applications can be used to listen for compliments, complaints, questions, problems, competitors, crisis, influencers, voice of the crowd and opportunities and customer needs. The data comes from any combination of social data sources; public, like facebook, Twitter, blogs; or private, like closed communities, internal networks, email, etc. and provide the capability to do detailed analysis on the data. The information gleaned from this data might include positive mentions, brand sentiment, product / support issues, synchronisation, tally, activation and resonance. In community management, analytics is critical for identifying and understanding influence and in increasing engagement of members.
The data is relevant in analysing and interacting with customers of course, but it also provides important insights into employees, suppliers and other partners. By examining connectivity, for example, businesses can understand the people network created across their business and determine who is connected to whom and the strength of that connection. This information is highly useful in sales and customer service, and can also be used in partner and supplier management. Social information can find use across many departments including sales, marketing, customer support, product strategy and design, procurement, partner management, human resources and the executive staff.  
The promise of predicting future behaviour
Traditionally, business intelligence and analytics has provided companies the ability to react to some existing or developing condition. For example a sales manager could identify a territory that was below quota and take some action to try and correct the situation. A fulfillment clerk might identify a stock shortage and notify production of increasing demand or an executive might drill into an over budget project to take corrective action. For all of these examples the action is taken in reaction to some information that is developed from data analysis. Social data has that same capability but it also holds the promise of predicting future behaviour.
This unique capability of social data comes because of the ability to apply sociology and psychology principals to the social data and gain understanding of behaviour and future behaviour from the patterns and signals. For the sales team, wouldn't it be useful to understand when a prospect is exhibiting buying behaviour or is losing interest in the purchase? Or for community managers what if they could identify future influencers before they had assumed that role? The promise of predictive analytics is already evidenced by socialytic solutions that can help determine future behaviour and over time the behavioural analytic algorithms are improving and getting much more accurate. 
Socialytic applications are available as stand alone packages and also are being embedded into other social platforms. Often the stand alone offerings are targeted at one of a few specific social business areas like brand monitoring, social marketing, sales intelligence and customer support/service. The embedded offering are a critical part of community management platforms, social business platforms, social marketing automation solutions, supplier and partner networks/communities and other social collaboration solutions. Over the past 12 to 18 months companies have piloted the use of socialytic solutions and this year are moving those pilots into mainstream use to support decision making and business strategy.
Michael Fauscette leads IDC’s Software Business Solutions Group which includes research and consulting in enterprise software applications, collaboration and social applications, software partner and alliances, open source, software vendor business models, cloud computing and software pricing and licensing. He also provides thought leadership in the area of social applications and the transition to the social business. With extensive executive experience with software vendors ranging from large enterprise companies to small Silicon Valley start ups, Mr. Fauscette brings a unique perspective by relating research data and trends to the overall strategic focus and go to market strategy of application software companies. Prior to joining IDC, Mr. Fauscette held senior consulting and services roles with seven software vendors including Autodesk, Inc., PeopleSoft, Inc. and MRO, Inc. Mr. Fauscette is a published author, blogger and accomplished public speaker on software, social business and software services strategies.

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