Customer experience (CX) and Digital Transformation (DT) are the buzzwords for success in corporate corridors today. Top management has woken up to the competitive advantage on offer through CX and DT. They realize facilitating the customer and delivering an engaging experience is the route to success, considering more than half of all customers having switched providers just because of poor user experience.
Analytics – the Driving Force
The driving force behind CX and DT is data analytics. Advanced analytics allow enterprises to deliver better user experiences, leading to higher satisfaction and in turn greater customer loyalty. Today’s fast-paced nature of business leaves no room for finding out what customers want or what they prefer by actually asking the customer. Businesses have little choice but to ascertain customer wants and preferences proactively, by crunching data, and engaging with them on their terms.
A seamless app or a portal is no longer a big deal. Rather, what impresses the customer is an interface which recognizes their interests or preferences, preferably leveraging Artificial Intelligence to make choices for the customer. Netflix, Spotify and Amazon have already adopted the art of such personalization to a high degree of perfection. Personalization is not possible without crunching data of customer preferences, wants, needs, and sentiments.
A direct corollary to creating better CX is DT. The best DT initiatives stem from customer preferences, or making things better for the customer. Enterprises create new value for their customers by leveraging new technologies such as IoT and Machine Learning to disrupt existing models. Analytics connects these technologies to the enterprise platform, enabling insights which allow strategists to make smarter decisions, keeping pace with the speed of changes in the external environment.
Measuring CX
A fallout of data dominance is the need to quantify decisions and actions. It is not enough if someone at the top “knows” CX and DT will enhance customer engagement, and deliver rich returns. Today’s highly challenging and competitive environment places a premium on every investment dollar, and assurance of a positive ROI, made explicit in quantifiable terms, is imperative.
A methodological approach to measure CX, as expounded by research major Forrester includes prioritizing customer segments most important to the business as the first step. The enterprise next selects the level of experiences – discrete customer journeys, or individual interactions, again depending on what is most important for the business, to measure: overall relationship. Next, the enterprise defines CX metrics for the selected experiences, in terms of customer perceptions, what actually happened, and the business outcomes connected with each experience. The enterprise then collects data for the selected CX metrics.
Effective data analytics and comparing results with an internal benchmark for each metric would not just set performance targets, but also motivate both internal stakeholders and external partners to work towards improving CX in a mythological way. The insights a good analytics engine offers enable the enterprise to identify problems of individual customers, collate it prioritize broad-scale improvement opportunities.
End-to-end interaction metrics
The importance of data can never be understated. About 80% of data remains dark, or never actually used to improve CX. Many enterprises burn themselves out collecting data that they are not able to do the critical step of putting such data to analysis.
Again, the type and nature of data matter just as the quantity of data matters. For all the talk on the importance of live and real-time data in the scheme of things, only about 23% of companies are actually able to integrate customer insights in real-time, as a SAS study reveals. However, at the same time, enterprises caught on the real-time data trap run the risk of losing sight of the bigger picture.
What truly sets apart an enterprise is the ability to use both active and passive data to gauge customer sentiment not just at any one point of time, including current time, but to get an end-to-end understanding of CX data.
An end-to-end understanding of CX data not only reveals what the customer did but also the rationale of why he did so. For instance, live analytics would make explicit a customer who is making a “high effort.” Business managers usually indulge in fire-fighting to facilitate such customers and resolve their wants. However, it could be the customer, having visited the website and not finding what he sought, turned to online chat and ended up dissatisfied with the partial or vague answers provided by the chat agent, ended up making two or more phone calls to indicate the “high effort,” and finally reached resolution three days later. Considering the end-to-end interaction metrics enables the business to address the root cause of the customer’s frustration, and make the necessary CX and DT changes, rather than merely offer a remedy the symptom.
Across the business landscape of today, the common thread running across market leaders is them having integrated analytics into everything, right from everyday discussions to formal contracts. They align appropriate internal resources with analytical skills and ensure each relevant business area feeds into the larger data management strategy.
A successful end-to-end data management strategy co-opts not just structured data but also various unstructured data even outside the provider’s systems. Twitter feeds, Facebook updates or any external source where customers indicate their mood, like or dislike for the product or service is useful in making course corrections and upgrades. Smart businesses adapt to procedures based on customer preferences rather than expect the customer to adjust to their internal process efficiency requirements, applying DT initiatives to cater to what their customers want.
Data improve the customer journey dramatically, but only for companies willing to be led to where the data leads them. Enterprises entrapped in the sentiments of legacy structures or incumbent products will find their CX and DT interventions stifled and pay a heavy price in terms of missed opportunities.
