We’ve had quite a lot to say last year about analytics in the context of customer service. We talked about customer analytics, defined as the process by which predictive insights will aid the achievement of acquiring new customers; growing customer lifetime value; retaining customers at risk of defection; enhancing customer loyalty and advocacy can be derived from available data.
Then there was predictive analytics, defined by the Harvard Business Review as “a way to predict the future using data from the past.” HBR provided several examples to illustrate how it could be a powerful tool for customer service organisations.”
And we looked at self-service analytics: putting the tools of analysis directly into the hands of customer service staff rather than keeping it locked away with IT departments.
Finally there was the rather more obscure ‘cohort analysis’, a generic term but defined by Wikipedia in the context of customer services as a subset of behavioural analytics that takes the data from a given eCommerce platform, web application, or online game and, rather than looking at all users as one unit, breaks them into related groups for analysis.
What all these techniques have in common regardless of name or purpose is dependence on data and the tools to analyse that data, so to this end a study from analyst firm Analysys Mason Harnessing Big Data to Cost Effectively Create a Customer-Centric Enterprise, is relevant and timely.
It was produced to examine the rollout of big data analytics solutions, including the use of data preparation solutions, in supporting customer experience management processes in communications service providers, but it is equally relevant to any customer service organisation.
The good news is that new big data technology is greatly reducing the cost of data analytics projects for customer experience management.
The not so good news is that identifying the right data and filtering out redundant data can be quite a challenge.
The report makes a strong case for the use of the open source software Hadoop to underpin an analytics project because of its low cost and its ability to scale to support large data sets.
It makes the point that communications service providers are well placed to deploy Hadoop in the service of customer experience management because their engineers were early adopters.
“Initially the use of Hadoop was primarily by engineers who were interested in the technology. They set-up test instances in labs and may get some minor, non-operational, use cases running on the system,” it says.
However, Hadoop is also well suited to analytics in the cause of customer experience management.
“Customer experience management requires the combining of data from many different sources to create a 360-degree view of each subscriber. This requires the use of very large data sets and the running of complex models to understand different aspects of the customer.”
The benefits, according to Analysys Mason, can be considerable: increased usage, higher customer retention rates, reduction in staff recruitment costs, reduced cost of customer care, reduced acquisition costs, reduced marketing costs.
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