In today’s ICT marketplace almost all networks provide a comparably high degree of objectively measurable quality. As a result, quality alone is no longer sufficient to distinguish a service provider from the competition and ensure customer loyalty. Instead, customer experience awareness has emerged as one of the most important business enablers for service providers, by helping them understand the opinions, needs and motivations of users. New enabling technologies for data analytics in the customer experience awareness field can provide richly detailed and actionable insights for business optimization.
The internet of things (IoT) offers users many great opportunities and simplifies many facets of life, but for some, its all-encompassing nature can be overwhelming and alienating. Service providers that recognize this risk have the opportunity to differentiate and create environments where each individual user feels comfortable and can rely on their intuition. Doing so successfully, however, requires a high degree of customer experience awareness.
In the not so distant past, objective QoS was the central concern for service providers. The idea was that a high degree of QoS enabled by an excellent infrastructure would lead to a positive customer experience with a high degree of satisfaction and loyalty. This was reflected in business metrics such as churn rates and users’ propensity to call customer care.
While that logic still holds true to a certain extent, there are many factors that it fails to take into account on an individual user level. The fact is, individual users are never truly objective, and a subjective individual user might not always feel satisfied – even when experiencing good service. Understanding why this is the case is essential to developing customer experience awareness and gaining the insights required to make the right decisions to actively manage the user’s perception.
There are several ways to go about developing a higher level of customer experience awareness, including training, goal setting and optimization of the organizational structure. In many cases, a customer experience management (CEM) system will also play a key role.
A CEM system is a business assurance tool that monitors and actively controls the impact that the user’s perception of a product, brand or service has on the business result of a service provider. The central figure in CEM thinking is the individual user: a human being with personal opinions based on subjective perceptions, who is embedded within a particular social environment. The user’s perception is based on their individual expectations. The moods, feelings and specific context of every user play a major role in developing their opinions and attitudes, and ultimately determining their actions and behaviors. Just as users can never be objective in forming their perception of their service providers, they are frequently inconsistent in their actions.
A good CEM system requires a holistic understanding that goes all the way down to an individual level, with a broad range of contextual information about the user taken into consideration. The resulting insights become actionable if they are combined with business processes at the individual user’s level that make it possible to personalize their experience.
The path to actionable insights
A CEM system provides insights that can be used as the basis for decision making and action taking that will help optimize business results. These insights are typically expressed in scores and indicators that quantify a particular aspect of users and their experience. For example, the Net Promoter Score (NPS) quantifies in a single number the user’s general willingness to promote the service provider, which is an indirect expression of their level of satisfaction and loyalty. The NPS measures user perception of the overall performance of the service provider, and has become a very useful tool for raising awareness of customer experience within an organization.
CEM systems are specifically designed for particular business optimization use cases, generating a variety of use-case-specific scores and indicators as their primary output. In network operations, for example, any substandard user experience is detected automatically with low latency from performance metrics and brought to the attention of support technicians for further analysis and rapid response. This contributes to overall business optimization, as problems are solved quickly, hence limiting their effects as well as the number of users who are exposed to them.
Every business process or optimization use case that would benefit from customer experience awareness will have its own particular requirements with respect to scores. Aside from the main subject that a particular score expresses, the following characteristics are relevant:
- Scope: Does the score reflect an insight at the individual user level, for a group of users, or for an entire organization?
- Outreach: How many users are included?
- Subjectivity: Does the insight reflect an objective fact or a subjective perception?
- Predictive: Is the insight directly measured or the result of a predictive model?
- Latency: How quickly does the score need to reflect an experience?
- Frequency: How often is an update of the score needed?
These use-case-specific requirements are directly reflected in the way a score is obtained and implemented. A service KPI (S-KPI) and similar low-latency, high-outreach measurements are needed for swift corrective action to be taken. Most of them are objective metrics measuring technical performance, and they make it possible to distinguish individual users. The characteristics of the KPI are reached with considerable effort in terms of the efficient handling of a real-time input data stream. The raw data comes from an extensive distributed probe network, and is correlated and processed in near real time.
Surveys and studies that approach the user directly and ask for feedback have a completely different technical profile from the S-KPI – that is, the characteristics of these scores are quite different. The NPS is a prominent example, as it is a direct measurement that typically has high latency, with significant time intervals between distinct measurements. Furthermore, the outreach is not very high, with only a few percent of the user base included in every measurement activity. In Figure 1, the characteristics of four different types of scores are compared – S-KPI, NPS, service level index (SLI) and mean opinion score (MOS).
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