customer puzzle.jpgHow 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.