Customer experience management (CEM) is poised to be revolutionised by the insights generated by big data analytics. Jeff Stacey, the vice president of CEM and Analytics at Ericsson tells VanillaPlus that, while there are technology and organisational challenges to achieving CSPs’ ultimate CEM goals, there are lessons to be learnt from the early big data analytics deployments of other industries. The good news is that CSPs have vast, unexploited customer data that can be turned into actionable insights, that create up-sell and cross-sell opportunities that enable them to protect customer base, and grow in an increasingly aggressive market.
VanillaPlus: The telecoms industry is being seen as the next area in which investments in big data analytics will boom. Why is that?
Jeff Stacey: Insights gained into our operations and customer interactions are by nature, very perishable. It goes without saying that we must improve our responsiveness to remain competitive, but there are also significant opportunities to become more efficient, which we will talk about.
The telecoms industry faces high volume, and high velocity demand patterns that will require us increasingly to act on data in motion, rather than only aggregating and analysing data at rest.
What’s fortunate about what has been happening in big data analytics is that it was first birthed, and nurtured, in the high-tech and finance industries. These early adopters have funded the bleeding edge technologies, gone through the hype and taken out some of the pitfalls. They’ve spent the dollars on making the technology applicable to business in general, not just the .com sector. The next big spend is lined up for the telecoms industry because it has vast, and complex sources of data – from billing, to networking, to applications – that can now can be exploited to deliver highly differentiated services.
There’s a significant difference between what is generically being called big data, and big data analytics. Loosely defined, big data is generally associated with the challenges of harnessing, storing and managing this fantastic amount of data growth we are experiencing. Some argue that more data has been created in the last five years than in the entirety of history – but that is not inherently an asset, unless it comes to extracting value from the immense volumes.
In fact, we are getting reports from our CSP customers that sometimes only small, single digit percentages of the big data they are collecting, gets analysed. Effective big data analytics is all about finding the right data at the right time and making that available to the person who can take action on it, in time.
According to TM Forum, 62% of the world’s CSP CxOs view improving customer experience as at the top of their agendas. CSPs’ challenges include market saturation, which that means they need to compete with rivals to win new customers, and fight to retain the customers they already have. CEM initiative is the key way in which they will drive growth.
CEM is not a new concept, but the next generation of CEM software solutions are definitely being transformed by analytics. However, the reverse is not necessarily true, in that loosely defined big data analytics projects do not give you CEM.
VP: So how is big data analytics redefining the customer experience?
JS: There are a lot of obvious areas, such as being able to look at messages in big data from an operational standpoint. Less obvious is being able to correlate those to an individual customer. That’s the holy grail the CEM industry is working towards – a complete context of a customer interaction.
Inevitably, that requires culture and process change, in addition to technical change. It almost goes without saying that having the right sponsorship and backing for a project is key.
In my past life, I’ve seen literally hundreds of individual big data consulting projects launch. It has become crystal clear that projects that have alignment with business priorities are wildly more successful than those that don’t.
In the vast majority of projects – those that were not aligned with business initiatives, the project got stuck in IT for almost a year. There’s therefore a lot of opportunity for CSPs to avoid these pitfalls drawing on the experience of seasoned business consulting, to keep the project aligned with business priorities.
The goal should be to take the science project out of data science, and instead make available actionable insights as part of routine, daily operations.
Big data analytics labs and CEM driven data projects should be held responsible for enriching the CEM software applications, CSR tools, and marketing campaigns.
Setting up a centre of excellence model has proven very effective in the finance industry, where project teams prove out their ROI to the test every 90 days, and scrap the project for another initiative, if it doesn’t move the needle.
Analytics resources, and the potential payoff, are too valuable to just let it run blindly within IT for a year or more.
VP: If next generation CEM is about drawing on insights uncovered by big data analytics, how can CSPs assess what insights are valuable?
JS: Next generation CEM is often a dedicated analytics application, but it has to be insight driven, not data driven. The quality of the insights has to enable a decision to be made whether that’s network, operational, customer or marketing. The insight has to give the right information at the right time to the right people.
Ideally, such an application is embeddable in existing business process, without disruption to existing CSR applications. But often, the value is so great, that it drives consolidation, or single pane of glass initiatives, because a superior process, and optimisation of process is now possible.
The timing is very important. Traditional reporting, for example, provides a snapshot of the past and, while there’s a place for that, some critical actions have a very short expiration date. Those include customer retention and the ability to respond rapidly to a customer upset.
Real-time or just-in-time as I’d say is sometimes more accurately said – conceptually is exactly what organisations need to effect change operationally on their networks. They need a real-time view of what the network and handset is experiencing now, and what factors cause customer concern. In addition, they ideally need to have a predictive view, to take preventive action in their network, and on high-value individuals.
From a return on investment point of view, it’s important to emphasise that not all of this is negative. There are up-sell and cross-sell opportunities among customers who are demanding better experience, and packages. That’s incremental new revenue for CSPs.
A lot of the ROI isn’t as far away as people thought. There are engagements that can show results within 90 days and also bring the ability to generate immediate responses to campaigns for up-sell and cross-sell.
One of the most important observations we have made is not to try and boil the ocean but to remain focused on clearly defined projects that will help CSPs reduce costs, reduce calls to customer care or increase satisfaction. This project-specific approach can then be proved out in a manageable way.
A successful approach here is to define a reusable, extendable analytics platform that feeds a variety of analytics applications that in turn support specific use cases for high value users or micro-markets. This way, the analytics investments and expertise are utilised, and yet the results are measurable, and aligned to support everyday business actions.
VP: You spoke earlier of CSPs being able to learn from the early adopters in other industries. What are the key lessons?
JS: In the financial industry, seconds made all the difference, and operationally the same reference architectures and the same approaches apply to CSPs. One of the hardest lessons learned was the patterns used to aggregate data, did not match the sometimes erratic patterns of demand a finance company would use, to take market advantage.
Contextual extraction, and delivery of information is key. The telecoms industry also faces erratic demand patterns and will require us to increasingly act on data in motion, rather than only aggregating and analysing data at rest.
Not enough big data efforts have accounted for the value of data in motion because of the contextual disconnect of IT with the stakeholder’s needs.
Many CTO’s at our CSP’s now are seeing that there is significant efficiency gains on only analysing and processing real-time data that is relevant, even if overall data will be archived at rest.
Also, keep in mind the promise of the cloud and virtualisation revolution was that you could utilise the maximum capacity of your technology assets by
managing a dynamic infrastructure that deals with the fits and starts of your business.
The better portion of your analytics investment should not be sitting idle between projects, but directly feeding CEM applications. New big data technologies are designed to maximise your workloads for just that aim. An experienced partner can make use of these proven blueprints, and architect a solution, that stays in touch with the specific needs of our industry and your business priorities.
VP: How must CSP leaders think differently about analytics to take account of that?
JS: They really have to think carefully about how their goals are to effect change in the customer experience they provide, as well as their approach to marketing. Then, based on measurable objectives, identify what actions they’ll need to take upon each insight cycle.
Siloed analytics projects are not only limiting in terms of what can be achieved, it is also a recipe for duplicated IT investment across each silo.
A bottom up approach does have its place, but mostly to inventory what data assets you have, for potential harvesting.
Therefore there needs to be a top down approach that requires you to take prioritisation of what investment must actually be used to create insights.
VP: So what are CSPs’ options to deploy big data analytics that feeds CEM?
JS: Lets name three stages to this – crawl, walk, run – and though every company may be in different stages, technologies, and projects – we all have much more to gain.
Crawl – means start looking at the facts, even in a past tense fashion, if necessary. The intelligence you gain may not be immediately actionable, and will tend to only raise blood pressure, for now.
This level of honesty and corporate self-awareness is required to align your CEM strategy with strategic initiatives.
Define, and prioritise which KPIs can be measured to effect change and how can that cascade throughout the line of business to become a measurable solution.
Walk – usually encompasses engaging the right partner – whether internal or external – and moving forward carefully to execute an action plan based on the requirements of the business – and testing the impact of contextual analytics in the NOC, call centre, and marketing operations.
Run – this stage moves beyond just taking a snapshot of the past and current state – it involves being able to see around the curve, and on an individual level. Advanced, but very attainable analytics are needed to do correlations that operators may not have considered. For example, most CSP’s are evaluating analytics to prevent and act on an individual experiencing poor network, but an unseen combination of bill shock is the perfect storm for a customer to leave.
On the opportunistic side, being able to take the next best action or offer is something that can revolutionise the call centre experience and make the jobs of call centre representatives far simpler. Predicting when a user will call and why and then pinpointing the issue and troubleshooting it in potentially 40% less time provides a truly enhanced level of customer experience management. The up-sell and cross-sell opportunities then follow.
CEM winners will be able to move forward aggressively because they see where the risk versus rewards are, and define what opportunities they want to take.
Clearly, those who wait and see are paying a terrible price in churn, network quality, and marketing effectiveness. There is strong evidence showing direct correlation of customer experience to top line revenue, quality of customer base, and even a company’s stock price.
Those that act will gain clarity, prioritise and then place educated bets. Those CSPs that respond organisationally – and swiftly – will achieve the highest return on their customer experience investments and that is what will set the winners apart in the telecoms market.
