The new frontier for customer analytics is social media. The advent of social media networks and related commenting and information sharing zones is a revolutionary change, full of both potential and challenges. In the social media sphere, customers are influencers, not just generators of sales transactions as seen though point-of-sale and e-commerce systems.
Using social media networks, customers can influence each other by commenting on brands, reviewing products, reacting to marketing campaigns and product or service introductions, and revealing shared buying interests. Unlike casual conversations, the commentary and social network connections are recorded and can therefore be analyzed and measured. The result is a data tsunami: the actions and content generated by participants in social media create “big data” sources that are full of potential for tracking and understanding behavior, trends, and sentiments.
The biggest difficulty is filtering out the noise, but not so much that the trends, patterns, and other insights hidden in the raw data are lost through aggregation and filtering. The need to analyze raw, detailed data is a major driver behind the implementation of Hadoop. Organizations need an unstructured place such as Hadoop files to put all kinds of big data in its pure form, rather than in a more structured data warehousing environment. The reason is that depending on the intent of the analysis, what might be considered just “noise” in the raw data from one perspective could be full of important “signals” from a different perspective.
Discovery, including what-if analysis, is an important part of customer analytics because users in marketing and other functions do not always know what they are looking for in the data and must try different types of analysis to produce the insight needed. They need to filter out noise, yet not be limited to standard, expected types of information such as what they might receive in a BI report.
Of course, to know how to filter out the noise, organizations need to define their purposes for analyzing social media data. Among the frequent targets for analysis are the following:
- Understanding sentiment drivers.
- Identifying characteristics for better segmentation.
- Measuring the organization’s share of voice and brand reputation compared with the competition.
- Determining the effectiveness of marketing touches and messages in buying behavior.
- Using predictive analytics on social media to discover patterns and anticipate customers’ problems with products or services.
Social media data garners attention because it is new and exciting; however, it isn’t necessarily the primary customer analytics focus in all organizations. Sources such as transactions, service records, call detail records, and Web clickstreams are often far more important for customer analytics. Data quality can also be a concern with social media. However, few organizations can ignore social media, because it is a living and adaptive ecosystem where consumers are actively participating around the clock and around the world. It is the public square of customer empowerment.
For more information, download the full white paper: Customer Analytics in the Age of Social Media