|Why Data Mining Is the Next Frontier for Social Media Marketing
The thinking about social media in corporate marketing departments is rapidly evolving. Initially, social media was seen as yet another broadcast opportunity for pushing messages out into the world, and for many companies that view persists. A social media consultant recently said that even today, when he approaches potential clients for the first time, they typically refer him to their PR agency, because “they handle Facebook for us.”
There’s nothing wrong with using social media as a tool for disseminating marketing messages or trying to establish deeper relationships with current or potential customers. However, there is another use of social media which may prove to be more powerful over the long term: Listening to the voice of the customer by data mining social networks.
Currently, CRM systems create customer profiles to help with marketing decisions using a combination of demographics and prior behavior, primarily historical buying patterns. These systems essentially enable companies to see their customers in the rear view mirror.
The customer data available via online communities like Facebook is both richer and more forward looking. A financial organization with access to such data would not only know that a customer had a checking account, savings account, two CDs and a mortgage, but also that the same customer was interested in golf or gourmet cooking — information that could be useful in planning future marketing initiatives. Every minute of every day, Facebook, Twitter and other online communities generate enormous amounts of this data. If it could be tapped, it could function like a real-time CRM system, continually revealing new trends and opportunities. Here’s how.
Tapping Social Media Data
The good news is that with today’s technology, this data can be tapped. But the process is not without its challenges. The data stream is a prime example of “Big Data.” Dealing with data sets measured in petabytes is a challenge in itself, and there is a serious problem with the signal-to-noise ratio. At my company, we estimate that at best, only 20% of the social media data stream contains relevant information. But before this problem even arises, companies face the issue of identifying their customers among the millions of participants in any given online community.
The Problem of Customer Identity
Most companies approach the problem of finding customers on social sites through the slow, arduous and expensive process of participating themselves. On Facebook, for example, businesses can gain access to the profiles of anyone who clicks the “Like” button on the company’s business site (depending on each customer’s privacy settings). With the right pitch, offer or game, companies can gradually gain an enhanced understanding of a subset of their social customer base.
With new matching technology that’s now available, the process is faster and more comprehensive. For example, matching technology uses artificial intelligence to figure out whether a given “John Smith” in a company’s customer database is the same individual as a particular John Smith on Facebook. The algorithms that accomplish this are extremely sophisticated, and they work. In fact, matching technology has been successfully used by law enforcement agencies to locate criminals.
If a company has one or two key pieces of information about its customers — e-mail address is often the most important — that company can accurately identify them on a social site and extract a substantial amount of data, including both profile data and transactional data that can reveal relationships important for marketing purposes. (Again, the amount of data available for any given customer depends on that customer’s personal privacy settings.)
Putting Data to Work
The second problem with social media is transforming data that is potentially useful into data that is actually useful. Social media data is generated by an entirely different technology stack than the transactional data that typically feeds CRM systems. Accordingly, it is stored in entirely different formats. That data can be transformed into a useful format with Master Data Management (MDM) technology.
MDM is the process of managing business-critical data, also known as master data (about customers, products, employees, suppliers, etc.) on an ongoing basis, creating and maintaining it as the system of record for the enterprise. MDM is implemented in order to ensure that the master data is validated as correct, consistent, and complete.
MDM has been used for more than a decade by companies that want to integrate disparate databases for a 360 degree view of their customers (or product portfolios, for that matter). It is equally effective in integrating social media data into existing CRM systems, and filtering that data for relevance.
What this all means is that companies can achieve important process improvements with bottom-line significance. For example, they can:
The disciplined use of demographic and historical customer data has enabled large numbers of companies to substantially increase the effectiveness of their marketing campaigns. Social media data will enable marketers to take targeting to the next level. It’s Big Data, but today’s technology can handle it.