Understanding the customer is more and more complex with each passing every day, especially due to the ever-increasing amoung of customer data - generating what is commonly known as "Big Data." Adding to the complexity is all the unstructured data from social media, which happens to be highly relevant to understanding the customer. So in this environment, it is increasingly difficult for organizations to reliably make faster decisions in predicting customer retention, attrition, and return rates.
How do we make sense and sort out this rapidly changing and increasing complex customer dynamic? How do we determine the best marketing offer to make using fact-based decision making rather than making decisions by intuition or gut feel using little or no data to back up our decisions? What technologies need to be pursued to maximize gaining the most customer insight in the social age?
I found some great answers to those questions in a best practices report from TDWI exploring this very subject in great detail. TDWI examined organizations’ current practices and future plans for customer analytics technology implementations, with a special focus on how organizations are adapting to both the data opportunities and challenges of social media networks.
From the many strategies laid out in the report, two strategies stood out above the rest for me:
Use social media analytics to support an active, not passive, social media strategy.
Gone are the days when organizatons could be passive with social media by just listening and analyzing social data at a basic level. Social media has matured to the point that it's no longer enough to simply measure success by the number of "likes," and instead marketers need to see all the ways the customer interacts with the organization as opportunities for positive engagement, as well as opportunities for new insights.A key source of insights is Social media analytics, used by leading organizations today to derive richer customer insights, therefore enabling their strategy development to be forward-looking in ways that are more relevant and more satisfying for the customer.
Evaluate and implement specialized analytic database technologies for customer analytics.
The speed and depth for analyzing big data information using customer analytics is enabled by technologies such as columnar databases and Hadoop/MapReduce. In a big data environment, the biggest challenge can be simply filtering out the noise, and sometimes all the trends, patterns, and other insights are lost due to aggregation and filtering of the data.
The need to analyze raw, detailed data is a major driver behind the implementation of Hadoop. Hadoop puts all kinds of data (structured, unstructured and semi-structured) together in its pure form, rather than in a more structured data warehouse environment. This allows organizations to gain an integrated view of complex customer behavioral data that is usually separated into incompatible silos and makes it ready for customer analysis. Hadoop is an attractive technology being inherently scalable that runs on commodity shared-nothing clusters that cost less than licensing systems from the big database companies.
The title of this report is Customer Analytics in the Age of Social Media. The report is pretty comprehensive in that it presents insights from an extensive research survey as well as interviews with users and industry specialists in customer analytics, social media analytics, marketing, and customer intelligence. I recommend it and encourage you to download it.
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