Social media: Marketing Input, Intelligence Output

Even the slowest followers in the print media mainstream have by now picked up and echoed the imperative to make use of social media for the purpose of reaching out to customers: Get a corporate Twitter ID and twit about everything new in your offerings. Get a corporate Facebook group and start one-on-one dialogues with the buyers of your products. All of that is a new way of doing marketing.

However, very few are talking about what comes out at the other end of these social media based, outbound marketing & PR efforts. While companies have learned to do a lot of Marketing Input, they can also take the next step and pick up the Intelligence Output. By monitoring and listening to what is going on in various social media channels, companies will be able to collect information about their own brand reputation, competitors’ brand reputation, customer satisfaction levels, competitors’ activity, competitors’ customer satisfaction levels, competitors’ product problems etc etc.

In the report “Top 10 trends in Business Intelligence for 2010” from HP (Hewlett-Packard), Social Computing (the use of online social media) is named as one of the top 10 trends for 2010 and described as an increasingly important source of decision support data.

“An important influence in the continuing BI evolution is the impact of social computing on decision-making processes, methods of collaboration and interaction, and enhanced customer experience. BI can expand the insight it provides organizations if it encompasses the information from interactions that occur in social computing environments. The dynamic conversation channels available through blogs, online communities, Twitter, Facebook, LinkedIn, and a host of social computing venues engage customers, prospects, partners, influencers, and employees—touching virtually every key constituent in an organization’s value chain. Very importantly, these channels are reshaping how customers evaluate and choose products, how brands are perceived, how business processes evolve, and how people work together.

Today most organizations are only beginning to analyze the learnings from online conversation. Technologies such as Social Mining and Social Intelligence use sophisticated data mining and text analytics to understand the implicit meaning of this unstructured data, which is completely reliant on the context in which it occurs. These include social behaviors, attitudes, relationships, and knowledge, all of which carry subjective qualities not easily categorized.  We will see the expanded use of these disciplines to harvest both implicit and explicit information. They may predict future behavior that can impact plans, for example, when strong online chatter suggests product interest that drives a decision to increase production. Or they may help organizations respond to explicit feedback, for example, when user experiences reported in communities lead to a product adjustment. This wealth of intelligence can and should align with, and augment, the intelligence delivered through the organization’s traditional BI initiatives.  For now, the integration of BI with social computing will be managed through the attention of a vigilant few within an organization. Emerging technologies, such as MapReduce, are evolving to help bridge the gap between this new frontier and traditional BI. Look for BI to expand its footprint beyond its traditional realm as it embraces the additional insight available through social computing.”

There is at least one commercial service provider specializing in tracking social media: Whitevector‘s Chat Reports service is a web-based service that provides consumer brand teams with a comprehensive picture of what is being said within online dissocial media discussions such as forums, blogs, and networks like Facebook and Twitter.

Tamara Barber at Forrester recently posted an article on her blog,  Three Key Considerations On Social Media For Market Research, where she lists three of the challenges that have to be met by systems for mining social media. She quotes people from Conversition, Attensity and Alterian. The headlines are:

• Process and methods need to be developed to make social media data be another source for Marketing Research
• To “connect the dots” on text mining data, you need to extract noun-verb relationships, sentiment, suggestions and intent.
• “[In social media research] 80% of your time is spent on identifying the right content, getting it into the right shape, and getting the gems out of it. Social media research is not magic.