It’s easy to see why Customer Relationship Management (CRM) could benefit hugely from the use of big data and analytics. Better insights= Better policies=Better engagement, right? Right, but unfortunately, it’s not as simple as it sounds.






 In reality, there are many factors that determine the effectiveness of your analysis and credibility of the insights that you derive. For example, getting insights on customer intent is crucial and can help design a great customer engagement program. But  this article in Forbes quotes a  recent study by Forrester, which found that  found that while 78% of surveyed marketers believe using intent data can lead to better ad relevancy, and 67% think it could help them gain a competitive edge;  factors such as inaccurate data (57%), inability to combine first and third-party data (49%), and not knowing how to feed intent data into targeting technology (54%) were cited as some of the biggest roadblocks of using intent data to reveal desired insights. In addition, there are basic shortcomings such as lack of proper technologies and limited human resources, which indicate that marketers may not be fully equipped to benefit from intent-based targeting just yet.


Don’t under-estimate the importance of quality of data

According to this survey by ZS Associates and the Sales Management Association, one of the biggest barriers to greater CRM adoption is the accuracy of data. Few respondents rated the accuracy of data about existing customers, prospective customers or future sales as “high” or “very high” in areas such as sales results (42 percent), opportunities (24 percent), and prospect profiles (19 percent).


Like Peter O’Kelly discusses in this podcast on SearchCRM, applying big data to customer relationship management (CRM) enables companies to get a more complete picture of their customers and perform real-time customer service. But these goals aren’t possible when an organization’s systems are siloed or customer data is poor quality. If the data you’re working with is incomplete or inconsistent, it can create bad customer experience patterns.


Always be aware of the big picture

Anjali Lai, an analyst with Forrester Research, is quoted in this InfoWorld article saying, “Data can often raise more questions than provide answers, and there is always the question of ‘why?’ behind the quantitative data trends. Data analyzed in a vacuum risks telling an incomplete story, and qualitative data can provide this contextual view.”


When it comes to Big Data, the questions themselves are just as important as the answers. Organizations that are able to come up with effective Big Data questioning are normally those that are well aware of their own business goals and the state of the surrounding market. Therefore, accurate questioning suggest that a business is more focused than one that blindly runs analytics for the sake of it, according to this article on IT Portal.