skip to primary navigationskip to content
 

Customer Experience Management Using Data Analytics

This study addresses the research question 'How can qualitative and quantitative measures be combined to measure the customer experience'.

Customer experience management is listed in the top ten priorities of CEOs around the globe. Carefully managing the customer experience can reap rewards including increased customer satisfaction and loyalty, increased revenue and greater employee satisfaction. Conceptualized as holistic, comprised of multiple touch points in an end-to-end journey the customer experience involves the customer’s cognitive, affective, emotional social and sensory elements. This conceptualization views the customer experience as a process rather than an outcome - a process made up of interactions and activities across multiple touch points with several employees, as well as other customers.

While it is acknowledged that the customer experience is complex and longitudinal, measurement is usually made at one point in time, typically at the end of the journey.  Such a “snap shot” approach does not adequately account for the multiple touch points.  Single measures taken at the end of the customer experience journey mask the underlying issues of concern, which are the basis for identifying improvements. Moreover, these individual single measures typically force customers to provide an “overall” assessment of the journey.  Not surprisingly, these aggregate single measures tend to mask the customer’s true feelings and evaluations of their experience. Even if multiple measures are taken at several touch points across the customer experience journey, they are often “averaged out”, masking important details that matter.

Research Objective:

This project seeks to create a novel way of analysing customer experience data, by combining qualitative and quantitative customer data that captures details of positive and negative experiences. Using a longitudinal customer data set of customer satisfaction data, that covering multiple key touch points for two case studies across a selection of service alliance partners, we will demonstrate a novel methodology for combining qualitative and quantitative customer data that captures details of positive and negative experiences to generate deep insights.

Research Question:

This study addresses the research question “How can qualitative and quantitative measures be combined to measure the customer experience”.

Approach:

customer experience diagramThe project will use text analytics approach to automate the process of analysing customer satisfaction data. In general, we used the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology for a linguistics-based approach using domain specificity. The text mining process adopted the following process: business and data understanding, training or model development and testing or model evaluation.

Outputs:

The outcome of 2015 research is a development and testing of customer experience text analytics method. The model will highlight the key priorities areas for customer experience improvement in B2B context. Specifically, the result of Text Analytics model will be:

  1. an identification of  the most important resources and activities for the customer when using a service.
  2. an evaluation of customer compliments and complaints. This will result to direct actions for improving the customer service experience. 

Contact Details: Dr Mohamed Zaki 

RSS Feed Latest news

Understanding business models in the construction sector

Nov 25, 2020

What’s standing in the way of offsite manufacturing? For decades, the construction sector has been hailing it as the next big thing but we have yet to see it really taking off. Why is that, when the technologies and processes already exist? Dr Zakaria Dakhli believes it is due to a fundamental incompatibility between business models and it is only when this has been fully understood that the long-awaited transformation can take place.

A machine learning approach to quality control

Nov 14, 2020

Most of the products we take for granted contain huge numbers of components assembled in multiple stages by different manufacturers. Quality control is vital throughout the assembly process with rigorous testing required at every step.

Digital twins: driving business model innovation

Nov 06, 2020

For B2B firms struggling to reap the rewards of digitalisation, could digital twins be the way forward? In this article, CSA's Dr Erika Pärn and colleagues from the Fraunhofer Institute and Technische Universität in Dortmund, explore the relationship between this emerging technology and business model innovation.

Making business model innovation happen: the Business Model Cohesiveness Scorecard

Oct 30, 2020

Digital transformation is failing to live up to its hype, at least as far as productivity gains are concerned. If, as research suggests, a lack of business model innovation is the main culprit, we need a way of making it happen. Dr Chander Velu thinks a balanced scorecard approach could be the answer.

2020 Annual Review

Oct 23, 2020

Read about our latest research in our tenth anniversary Annual Review.

View all news