Hadoop, CEM and the application of common sense

When you see the word ‘Hadoop’ in the opening paragraphs of a white paper, you know you are talking about seriously BIG data. What is refreshing about this paper from real-time specialist Openet, is that it examines how Hadoop and big data can be mixed with common sense to improve customer experience management (CEM).

The first point is an admission that a CEM project that tries to include all aspects of customer experience will inevitably be unwieldy. This is one reason why Hadoop is becoming popular in this field, and is being adopted by Tier One telcos, rapidly. And one very good reason for adopting an iterative – not a ‘big bang’ – approach. Another reason to use Hadoop is that there are a huge number of data sources – both internal and external – that need to be managed and combined in order to make progress.

The white paper, written by Analsys Mason, is free, can be downloaded here. It introduces a new way of looking at monitoring and managing the customer experience. Net Promoter Score (NPS) is one way, but using these principles it is possible to design and implement a Customer Experience Index (CEI).

As the paper says, ‘external systems typically use customer surveys, often using NPS (Net Promoter Score) as the metric of satisfaction. Internal systems rely on KPI’s at each system level to monitor different aspects of a subscriber’. As a starting point each of the internal KPIs can be weighted based on their importance to practical customer experience, see the figure below. For instance, the first dropped call is irritating, the second and third much more so.


Analysys Mason, based on their client experience believe that data preparation is the most important element in getting this right, indeed it can take up between 50% and 70% of an analytics project’s time. Like many things, common sense and preparation pay big rewards.

The data suggests that happy customers will buy more products and services, whereas unhappy customers, or detractors are at least twice as likely to churn. Using open source, highly scalable resources such as Hadoop can give a Service Provider a vast array of actionable data, such as:

  • The likelihood for each customer to churn.
  • Which offers a customer is most likely to purchase.
  • The next best actions that a customer services representative should take to address a 
customer need.
  • Which channel a customer has as a preference to purchase through or interact with.
  • The social network in which a subscriber operates.
  • The impact of network performance or faults on a specific subscriber.
  • The effect on customers of the roll-out of new technologies such as NFV/SDN or VoLTE.
  • What processes, network upgrade or pricing will best improve overall customer experiences.

This might be the bridge between theory and practice in improving the customer experience through actionable data.

Overall, CEM projects are becoming more complex, as the delivery methods – and the products being delivered – become more complex. The good news is that new big data technology is radically reducing the cost of initiating them.

The key to success, says this paper, is industry knowledge, preparation, common sense and an iterative approach.

The paper is a good, practical read, is available for free (short registration) here. Download it now.

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