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descriptive analytics

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There’s no escaping it-customer experience management (CXM) is what makes or breaks a brand. This is especially true today, since telecom operators live in precarious times, with falling average revenue per user and wafer-thin profit margins. To top it all, customers are just itching to jump ship at the first sight of trouble-i.e.-bad customer service. Let’s look at a few analyst statistics in this regard:

  • By 2020, customer experience will overtake price and product as the key brand differentiator-Walker
  • 68 per cent of customers say that they’ve switched service providers owing to poor customer experience-Accenture
  • 95 per cent of dissatisfied customers tell others about their bad experience-Zendesk
  • Customers who experience a positive social customer care experiences are nearly three times more likely to recommend a brand-Harvard Business Review

In other words, operators, please sit up and take notice! A sound CXM roadmap is the secret ingredient that gives your business extra bite. And here are a few more statistics to prove it:

  • According to American Express, one happy customer can equal as many as nine referrals for your business.
  • Maximizing satisfaction with customer journeys has the potential not only to increase customer satisfaction by 20 per cent but also to lift revenue by up to 15 per cent while lowering the cost of serving customers by as much as 20 per cent-McKinsey
  • Customer experience leaders have more than a 16 per cent advantage over competitors in willingness to buy, reluctance to switch brands, and likelihood to recommend – Temkin Group

Now the tricky bit-where do operators start? Well, it’s a two-fold step, really, that begins with proactively tracking down what customers (actually) want. This, in turn, will (hopefully) throw up actionable insights into the challenges related to ensuring customer satisfaction. Chalking out a CXM strategy is merely the most obvious result of the above plan.

So, what makes a customer tick? While there is no single appropriate response to this question, I would like to offer up analytics as a viable option. Here’s why-the age of data is upon us. We collect copious amounts (some analysts say an estimated 2.5 quintillion bytes) about individuals, places, processes, et all, every day. This data comes from multiple sources, such as sensors used to gather climate-related information, posts on social media sites, digital pictures and videos, purchase transaction records, cell phone GPS signals, the list is endless.

Here’s the catch, though. While there is little doubt that the intelligence of our systems is responsible for facilitating this growth, the volume of structured and unstructured data being collected isn’t necessarily valuable on its own. To gain actionable insights, an operator ought to know what to do with this data, how to leverage it to the fullest.

This is where analytics steps in. Before jumping the gun, however, operators ought to be aware of the essential elements of using analytics successfully:

  • Aggregate data from multiple sources. Let’s face it, a multi-tiered approach is essential to gain a 360 degree view of the customer journey. Operators, think Facebook, think Twitter and all the social media platforms out there today. The customer uses these mediums to broadcast their views on every brand. The trick, therefore, is to integrate analytical tools from CEM into social media monitoring to identify customer behavioural patterns. The result? Proactive engagement at every stage of the customer lifecycle!
  • Utilize existing CRM system data: This essentially ensures that data is centralized, accessible and can be used to gain a holistic picture of the customer. Operators need not waste precious time in collecting the same data over and over again.
  • Examine unstructured data: Going forward, data volumes are only going to increase substantially. The idea, therefore, is to adopt an all-encompassing approach, which may have to include complex data mining practises. If carried out proactively, companies can gain a competitive edge and unearth previous unevaluated customer data links.

There’s another catch-operators, please note, analytics isn’t a “one size fits all” strategy. Choose your best fit. Interestingly, the evolution of analytics itself took place over multiple stages. According to industry analysts, in the past, all available data was scrutinized using descriptive analytics, which looks at the reasons behind past success or failure. An example is the results a business gets from the web server through Google Analytics tools. The outcomes help understand what actually happened in the past and validate if a promotional campaign was successful or not based on basic parameters like page views.

The next step (and with the advent of big data) is predictive analytics, which focuses on the question: “What is probably going to happen in the future?” An interesting example of an application is in producing the credit score. Credit score helps financial institutions decide the probability of a customer paying credit bills on time.

Next up is prescriptive analytics, which goes beyond future outcomes to answer the question: “What is the able action?” Interestingly, analytics are taken a step further, with the advent of highly intelligent cognitive systems. Instead of needing to be programmed, they use natural language processing and machine learning algorithms to help make key decisions using huge volumes of fast-moving big data.

Please note dear readers that these four categories of analytics should ideally co-exist. There is no question of one outweighing the other, in terms of benefits, etc; they’re all different, with their own merits. Do remember, though, that all are equally necessary to obtain a complete and clear picture of what a customer actually wants by using all of the available information and data.

I’d like to conclude with a caveat which is that analytics is only as good or as bad as the implementation of the requisite action plan. For analytics to be successfully leveraged, the operator ought to be guided to the actionable tasks which can be implemented. If not, the company runs the risk of “analysis paralysis”, which doesn’t leave any room for any quantifiable outcome. In the end, the numbers say it all!

July 4, 2016 0 comment
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