The Application of Sentiment Analysis and Text Analytics to Customer Experience Reviews to Understand What Customers Are Really Saying

The Application of Sentiment Analysis and Text Analytics to Customer Experience Reviews to Understand What Customers Are Really Saying

Conor Gallagher, Eoghan Furey, Kevin Curran
Copyright: © 2019 |Pages: 27
DOI: 10.4018/IJDWM.2019100102
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In a world of ever-growing customer data, businesses are required to have a clear line of sight into what their customers think about the business, its products, people and how it treats them. Insight into these critical areas for a business will aid in the development of a robust customer experience strategy and in turn drive loyalty and recommendations to others by their customers. It is key for business to access and mine their customer data to drive a modern customer experience. This article investigates the use of a text mining approach to aid sentiment analysis in the pursuit of understanding what customers are saying about products, services and interactions with a business. This is commonly known as Voice of the Customer (VOC) data and it is key to unlocking customer sentiment. The authors analyse the relationship between unstructured customer sentiment in the form of verbatim feedback and structured data in the form of user review ratings or satisfaction ratings to explore the question of whether customers say what they really think when given the opportunity to provide free text feedback as opposed to how they rate a product on a scale of one to five. Using various Sentiment Analysis approaches, the authors assign a sentiment score to a piece of verbatim feedback and then categorise it as positive, negative, or neutral. Using this normalised sentiment score, they compare it to the corresponding rating score and investigate the potential business insights. The results obtained indicate that a business cannot rely solely on a standalone single metric as a source of truth regarding customer experience. There is a significant difference between the customer ratings score and the sentiment of their corresponding review of the product. The authors propose that it is imperative that a business supplements their customer feedback scores with a robust sentiment analysis strategy.
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1. Introduction

Increasingly, leading modern businesses are looking to gain more insights from customer verbatim data they collect. Unfiltered customer feedback provides a tremendous opportunity to learn more about customer sentiment in relation to products and their end-to-end experience with a company. It also gives the business an opportunity to understand how they can ‘close-the-loop’ on any poor feedback they may receive and conversely how they can capitalise on positive feedback from customers. One of the drawbacks of online customer verbatim is its quality and quantity of data (Maritz, 2018). How do businesses mine and analyse data to ensure it reveals key insights that can ultimately drive profits in the right direction. Once a business recognises the benefit of analysing its customer feedback data, the question becomes, how? Identifying customer verbatim as unstructured data is the first step and secondly using a ‘Big Data’ approach such as text mining will allow the business to employ a more sophisticated and advanced modelling technique to uncover patterns in the data, employing sentiment analysis to identify key themes from the feedback. However, businesses will continue to use structured quantitative data gathering techniques such as asking customers to rate customer/client interactions, products and employees on various scales i.e. 1-5 Stars, 0 – 11-point scales and so on. These methods cannot be solely replied upon and it is key for a business to critically analyse its unstructured data whether than be on social media channels, ad-hoc emails to the business, letters and customer surveys.

All businesses need customers to prosper and grow. They are the main source of revenue for most businesses. It can be argued that the success of a business is directly proportional to its ability to acquire customers (Smith, 2016), keep customers happy, identify issues or irritants and consequently drive more selling opportunities. But for a business to achieve this it needs to identify the key indicators that will provide insights to facilitate this. There are many variables that a business needs to identify, the who, what, why, and how. Who are the potential customers in the marketplace? What do they want? Why do they want a product? How as a business can they retain customers and grow their market share. Examples of where a business can utilise their customer data are sales demographics, product and channel product preferences, social media or website sentiment and interactions and transaction behaviours. Customer analytics is becoming one of the key enablers available to companies to facilitate the translation of raw data into useful insights about their customers. This research intends to highlight the importance of customer analytics in the delivery of customer insights back to the business to drive decision making (Fiedler, et al., 2016). 60% of companies said that organisational silos were a major obstacle to improving customer experience (Google, 2016). Companies are finding it difficult to understand what customer data they have and often are underutilising the customer data that they do have (Department of Industry, 2018). The most successful companies have found ways around this and are actively implementing Customer Experience strategies i.e. building Customer Experience teams that are agile and can pivot based on business needs and goals (Hollyoake, 2009). While any company can use data to report financials or drive cost savings, the key differentiation comes from how this data can be used to drive business insights. While businesses are quite good at tracking structed feedback i.e. numerical ratings such as 1 - 5-star ratings they need to improve on understanding what customers are saying about their products and experiences with the company. This poses a serious challenge and difficulty increases as a business grows its customer base (Parasuraman, et al., 1991). With that in mind it seems impossible for a business to contact every potential and current customer individually to understand their sentiment. There are many methods or channels in which customers can interact with a business, for example, social media, email, customer satisfaction surveys, registering satisfaction via IVRs, capturing feedback on calls via Speech Analytics. So, the challenge for the business is how to accurately capture, store, analyse and obtain insights from this data. This becomes more difficult as the customer base grows, and it seems very unlikely that a business will create a touchpoint with every single customer they have.

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