Using Hadoop and big data for more complete CRM insights

Brands of all types are excited by the idea of delivering more personalized customer experiences through the use of Hadoop and big data analytics, but, in reality, many are falling short by failing to integrate data from multiple sources. A recent Harvard Business Review blog post highlighted the shortcomings present in many companies' marketing efforts due to incomplete data and insufficient analytics. To overcome these barriers, companies can use tools such as Hadoop and MapReduce to make sense of information stored in customer relationship management (CRM) systems.

"Though digital channels continue to proliferate and consumers continue to distribute their time spent with a brand across this fragmented landscape, most brands are still using outdated tactics to reach the masses," wrote HBR contributor Richard Ting, executive vice president and global executive creative director of mobile and social platforms at digital agency R/GA. "…To surgically cut through the noise, advertisers need to develop richer customer profiles."

He suggested that companies that use data pulled from sources such as CRM software, social media conversation and interactions, brand interactions such as website behavior and purchase histories can better accomplish their goals. These can include targeting segments of consumers, improving their understanding of their audience for real-time marketing campaigns and increasing the long term value of each customer. The barrier to using this information, he said, is technology.

How Hadoop helps handle CRM data
In a recent Smart Data Collective post, contributor and big data executive Mark van Rijmenam similarly identified four aspects of CRM big data:

– Customer management using structured database entries
– Interaction through the use of unstructured data in the form of emails, social media posts and comments
– Analysis of structured data from online behavior, such as click-through rates
– Knowledge of the customer based on a synthesis of the other factors that can create predictive recommendations

The combination of these aspects can create massive data streams, van Rijmenam warned. He recommended using technologies such as Hadoop and MapReduce to turn this information into insight on behavior patterns and audience sentiments and to power recommendations that influence future actions.

"Customers [who] contact organizations through whatever channel want to be recognized and [served] appropriately," he wrote. "Using big data technologies to collect, store and analyze the necessary data will truly make your customer relationship management valuable and give your organization a competitive advantage."

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