How do you define customer experience — and, more importantly, how do you create the best customer experience model? I started wondering about both concepts while working on an assignment to increase sales from the digital channel of a hospitality giant.
It seems like everyone is interested in the idea of “great customer experiences." But both businesses and scholars have struggled to understand what that really means, and have fared even worse at attempts to measure the outcomes of the "Customer Experience."
My suggestion: Divide the customer experience into six dimensions that can work cohesively to improve the requisite "experience" to customers, provide competitive differentiation and even affect the bottom line.
The diagram above visualizes the six dimensions of customer experiences, a few factors that characterize those dimensions (inner circle) and the enablers for succeeding in those dimensions (outer circle).
Knowing the Customer
Knowing the customer cannot be limited to just collecting personal and demographic data about the customer as it has traditionally been done. Increased varieties of touch points, particularly the digital touch points, help in collecting advanced data such as transaction history and behavior across multiple channels.
This is amplified by advantages of big data analytics in synthesizing the collected data and understanding more about customers, their individual behaviors as well as preferences. Similarly, speech analytics helps in understanding customer behavior and problems in the interactive voice response (IVR). All of these together, using data from all channels, provides a consolidated view of the customer by creating customer profiles.
Imagine calling the IVR of a hotel booking agency and hearing “Hi, I am Martha, can I help you?“ versus “Good Morning Mr. Wong. I am Martha” and offering to book a room based on your previous preferences such as type of room, amenities, etc. A key step in personalization is customer identification. Techniques such as recognizing automatic number identification, cookies, email ID, Facebook handle and more can help in identifying customers. A few touch points by their very nature help in customer identification — for instance mobile apps.
The next step would be to provide a personalized service to the identified customer. Previous point about knowing the customer combined with interaction design methodologies help in ensuring that the customer feels valued. Advanced statistical techniques coupled with big data help in micro segmentation of customer and thus in targeting effectively. Traditional loyalty programs also aid in personalization. Some well known examples of personalization in action are Amazon’s dynamic recommendations and Zite’s story/news selection.
Every customer wants to be treated according to their individual needs and does not like generalized interactions. This necessitates a clear understanding of customer needs and the intent of transactions. Advanced predictive analytical techniques using machine learning algorithms such as regression models or Bayesian Models help in intent prediction and thus in designing customer journey, particularly in digital channels.
For instance, predicting the intents of the callers of a nationwide directory assistance service helped us increase the self-service rate by nearly 5 percent and also reduce the time taken for completing a transaction. Similarly, my colleagues could predict the intents and problems of web visitors using Naïve Bayesian model for a telecom giant and thus designed optimal interventions to increase the sales.
Recent research from Google shows that 90 percent of the customers use multiple screens sequentially to accomplish a task over time and 98 percent of them move between devices on the same day. Also, 67 percent of customers start shopping on one device and continue on another. Retaining context across multiple touch points and transactions further facilitates predicting the exact intent of the caller so you can provide seamless service. How nice would it be to start making an airline reservation on the web, pause and continue in the IVR or your mobile app without duplicating any steps!
Ease of Transactions
Imagine you are talking to an airline agent using your smartphone to book a ticket. She offers you 10 different choices, but you've forgotten the first choice after she completes the tenth. What if instead you could simultaneously see the flight choices on your smartphone screen while talking to the agent? Research by Google shows that 66 percent of customers use their smartphone and laptop simultaneously and 22 percent use both simultaneously usage for the same transaction. Innovative multichannel solutions greatly simplify and ease transactions.
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