Customer Satisfaction is creating a huge gap between promise and delivery and this feeling is creeping up from both sides of the counter. Only 17% of CMOs are saying that they have been extremely successful at delivering highly relevant customer experience. At the same time, customer satisfaction in the UK has been dropping slowly for the past 2 years. Mobile networks operators (MNOs) are just one of the examples. In the UK, telecommunications and media is one of the sectors with the lowest customer satisfaction rates – people are unhappier only with Local Public Services, Utilities, Transport. Globally, mobile/internet service providers rank among the industries with the largest gap between the customer experience expectations and reality.
Yet, some would argue that gaining results from a customer experience programme needs more time and bigger investments; Or that customer satisfaction doesn’t matter as much as communicating better deals and offers than the competitors. And at first, it might appear that it’s true.
TABLE OF CONTENTS
- Increase the scope and precision of retention and upsell efforts >
- Understand your customers better >
- Be agile >
- Prove the business value of improving customer experience >
In the UK mobile virtual network operators (MVNOs) are driving down monthly contract prices consistently and aggressively, leading to a total of 2.9 million people changing networks in 2018. The number of churners is twice as high in Spain (13% in 2018), with tariff price by far the leading driver for change, cited by 62% of the individual respondents.
On the other hand, research also shows that consumers are willing to pay up to 8% more for a cell phone plan if the mobile network operator provides great customer experience.
So is there a successful strategy to address the increasing demands and expectations of mobile network clients while sustaining business growth?
Stop responding to metrics that describe the past
Stop focusing on metrics of the past. Metrics like NPS® or CSat scores have one big disadvantage and it’s that they measure a certain state or moment back in time. In today’s dynamic world, customer satisfaction is too fluid to be accurately represented by a static metric. It’s more important than ever to obtain knowledge in a dynamic, near real-time way about customers, directly from them, to understand how they think and feel about your services and products, what they require more or less of, and what they might want to change.
Modern algorithms provide the most comprehensive and up-to-date picture of the whole customer base by combining what they do and what they say, to predict what they are likely to do next. Harnessing the true power of predictive customer analytics can bring multiple benefits to an organisation.
Increase the scope and precision of retention and upsell efforts
Most providers are targeting customers based on their Net Promoter Scores or C-Sat scores. The problem is that traditional methods of surveying customers typically have a very low response rate (between 5% and 20%). Our experience suggests that only 10% of telco clients bother replying when prompted about their levels of satisfaction. On one hand, this means that at any given time mobile network operators (MNOs) are aware of 10 out of every 100 dissatisfied customers who are ready to leave right now.
On the other, traditional methods of surveying are not overly reliable in identifying which are the most important factors that drive satisfaction. What if there was an accurate way to predict what customers will do next and to personalise responses at the level of the individual customer? As a result of the Predictive NPS algorithmic model that leverages a combination of surveys and data to identify likely detractors, satisfaction scores can be predicted with over 80% confidence for each customer in your base, even the ones that do not respond to surveys. Dynamic algorithms are used to continuously flag lists of potential churners, and feed directly in to call centre call sheets, to help increase retention.
In an industry where costs for acquisition of new customers are staggeringly high, referrals from brand advocates are the quickest way to get new clients without engaging in blood-drenching price wars. The Predictive NPS algorithmic model can be utilised not only for retention of customers likely to leave, but also to identify the happiest clients that will gladly recommend your service to a friend.
Understand your customers better
Individual customer feedback is a huge untapped source of valuable suggestions, but its real power can be uncovered when large amounts of unstructured text data is analysed at scale. The best approach to achieving a 360-degree view of the customer is to submit transcribed recordings from customer services centres, complaint and other form submissions, ratings or reviews or open-ended questions from surveys to one framework of categorisation, that is carefully tailored to correspond to the structure of the business. Machine learning algorithms can then automatically categorise the text by topic and subtopics and assign sentiment to each phrase, to extract the most important topics or factors that influence customer satisfaction.
According to the “Adapt or Fail: The Customer Experience Imperative” Study, 61% of CX execs say their company’s ability to quickly adapt is a top strategic priority. But how can one decide where to focus next if there are a hundred factors that influence customer experience at hundreds of touchpoints across the modern consumer’s journey? One way to take confident decisions and move quickly on to the next investment area is by applying Key Driver Analysis to your customer data. Factors are categorized in three groups according to their importance – hygienic (Basics), must-haves (One-Dimensional) and Nice-to-have (Delighters). The model helps you prioritise the improvement initiatives with the highest impact on satisfaction.
Prove the business value of improving customer experience
With only 14% of companies measuring the ROI of Customer Experience, it is no wonder that the whole philosophy is being questioned. Research shows that NPS is proven to be positively correlated with revenue growth. Happy customers spend more, churn less, and require less service effort. The challenge however is assigning an exact amount to the specific improvements. Our analytics models take the NPS to business value link one step further. We look at CX Improvement Action ROIs by calculating the expected CX uplift from specific improvement initiatives. The analysis is done on both customer as well as on store or overall level. Customer executives are now able to utilise data to prove how their initiatives increase lifetime customer value and size of the contract, while reducing cost to serve.
Advanced machine learning and text analytics algorithms can serve as a powerful tool to help drive customer-level initiatives, enabling you to prioritise improvements with the highest potential and evaluate the real financial value your CX programmes offer. By doing so you can help transform your CX offer into an initiative that drives business growth.