Case Study:
Improving Call Satisfaction: A Case Study
by TRC
TRC presents a case study of analyzing and improving a call center as an on-going data collection process.
BACKGROUND
A utility client wanted to improve the performance of their call center operations. Traditionally the call centers had been run from an inside-out perspective. Hence, performance was measured only in terms of metrics that managers felt were important. The customer perspective was not considered. With the involvement of the market research department, a decision was made to understand the customer perspective and benchmark performance as seen by customers.
TRC (then The Response Center) was selected in 1999 to guide this process through proper design, data collection and analysis. Working with the client we identified customer satisfaction with the calling experience as the single most important variable to focus upon. This variable most effectively captures the customer perspective, and its variation over time would allow the client to understand call center performance as perceived by customers. However, tracking this variable over time alone would not be sufficient. It needs to be positively influenced. For this, a comprehensive customer satisfaction program needed to be created.
In consultation with the client we designed a customer satisfaction tracking program that used baseline and annual follow-up data collection efforts to monitor performance. The baseline wave of data collection was conducted in early 1999 followed by advanced statistical modeling (explained later) to understand the drivers of satisfaction with call experience. Suggested improvements were implemented in the following months and new measurements were made through the following year. Every year thereafter, statistical modeling has been conducted at the end of the year to identify key drivers. Early in the following year the client has implemented the suggestions and performance improvement has been continuously monitored.
Due to the success of the program, the scope of the study has increased over time to include multiple call centers with varying call types and different types of customers (business and residential). The scope of the questionnaire has also enlarged to include issue such as the Interactive Voice Response (IVR) system. This case study details how quantifiable improvement in the primary program was achieved through the use of advanced statistical modeling.
MODELING
Key driver analysis aims to understand the important influencers of a specified variable. For example, it is difficult to directly improve customers’ satisfaction with call experience. A key driver analysis would be required to identify the variables that influence satisfaction with call experience. In order to do this, the questionnaire needs to include adequate and proper questions. Working with the client we were able to design such an instrument and collect appropriate data through telephone surveys.
The specific statistical technique used in this case was SatiscanTM a proprietary product that belongs to TRC. Traditionally key driver analysis uses regression analysis to identify the influencers of the variable of interest. While regression analysis is a pretty robust method, it does have some shortcomings. Primarily, it does not model the inter-relationships between the independent variables. SatiscanTM does this and is hence able to provide a more detailed and accurate picture of the key drivers. For this program, the questionnaire was designed for the use of SatiscanTM to model the data.
RESULTS
Based on the results from the SatiscanTM modeling, several recommendations were made to the client. The most important of these were improvements in:
- CSR taking responsibility for solving the problem
- Customers having to call only once to resolve their problems
As it happens both of these issues are related. Improving performance on the former is one way of improving performance on the latter without necessitating a substantial increase in staffing resources. Further, the model also predicts that these improvements would lead to an improvement in the satisfaction with the CSR who answers the call. Finally, a clear improvement in satisfaction with the call experience (the variable of interest) should be seen.
After identifying the key drivers we engaged in a series of discussions with the client to discuss operational changes in their call centers and changes in training methods. These ultimately resulted in the implementation of these (and other) recommendations in an effort to improve performance.
Performance has been tracked steadily over the last five years and is currently ongoing. The improvement in performance in the variables of interest is as follows:
- Overall call satisfaction has improved by 15% points
- CSR satisfaction has improved by 14% points
- First call resolution has improved by 18% points
- CSR took responsibility has improved by 13% points
Data collection and modeling are continuing on different customer groups and call types. Recommendations continue to be made on a yearly basis and the client is very satisfied with the program.
This case study was provided by TRC, a full-service market research provider located in Fort Washington, PA.
Other content shared by TRC
Better Questions For Segmentation: Use of MAX-DIFF
by Rajan Sambandam, TRC
Using Maximum Difference Scaling as a method in designing surveys may ensure more useful results in your market research. It is a comparative method based on importance that sidesteps the problems associated with traditional importance scales. TRC explains the mechanics behind this method through a detailed example in this white paper. Read Article »
Database Scoring with Object Based Segmentation
by Rajan Sambandam, TRC
Segmentation created from company databases are often lacking the rich segmentation schemes formed by attitudinal surveys. A new approach is Object based segmentation that uses database variables at the basis for forming attitudinal segments, leaving both markets classifiable with clear demographic segments. TRC compares traditional segmentation analysis with Object based. Read Article »
Asymmetry in Product Features: Use of the Kano Method
by Rajan Sambandam, TRC
The presence or absence of product features strongly affect consumer satisfaction with the design. Comparing these features using asymmetry analysis can help identify satisfiers and dissatisfiers from among the features of a product. The Kano method is similar but results in categorizing each respondent's answers. TRC presents this essential method of deciding new product features in detail. Read Article »
Conjoint Analysis versus Self-Explicated Method: A Comparison
by Rajan Sambandam, TRC
Determining feature importance in a product can be divided into two techniques - top-down methods where a customer evaluates the whole product at once, and bottum-up methods where features are evaluated individually or in sets. The former method, Conjoint Analysis, is more common while the latter method, Self-Explicated Method, is not widely used but has practical advantages. TRC compares the two methods in this white paper. Read Article »
Product Configurator
by Rajan Sambandam, TRC
To help customers purchase the right product, companies often use product configurators - tools that let customers design their purchase before ordering. This method is employed as a market research technique, similar to conjoint analysis but without some of the constrictions. This white paper from TRC explains an appropriate use of the product configurator method. Read Article »
Market Segmentation: One Method, Four Examples
by Rajan Sambandam, TRC
Effective market segmentation requires an understanding of the market and the skilled art of finding the appropriate segments. TRC gives four examples of this method's application with results. Read Article »
How to Measure the Value of a Brand
by Rajan Sambandam, TRC
Brand name evokes an inherent value; finding a way to reliably measure that value is crucial in determining product development. A technique called discrete choice conjoint analysis is described in this paper by TRC. Read Article »
Deriving Value from Research: the Use of Conjoint Analysis for Product Development
by Rajan Sambandam, TRC
Marketing research has been used by firms over the last several decades to provide information for decision making. Over time, increasingly sophisticated statistical methods have been developed and deployed in the service of this goal. This article focuses on one such method - conjoint analysis - and its application to product development. Read Article »
Cluster Analysis Gets Complicated
by Rajan Sambandam, TRC
Segmentation studies using cluster analysis have become commonplace. However, the data may be affected by collinearity, which can have a strong impact and affect the results of the analysis unless addressed. This article investigates what level presents a problem, why it's a problem, and how to get around it. Simulated data allows a clear demonstration of the issue without clouding it with extraneous factors. Read Article »
Identifying Feature Importance: A Comparison of Methods
by TRC
Understanding what customers want is fundamental to the new product development process as well as to the process of keeping existing products fresh and relevant. To be successful in this area we need to be able to correctly identify what features are important to consumers. Feature importance can be measured using a variety of methods of differing effectiveness. In this paper we will deal with the following methods: Importance Scales, Pick data, Pairwise Comparisons, and Max-Diff.
Read Article »
Monadic Price Testing vs. Price Laddering
by TRC
Compares two popular pricing methods to understand the difference in take rate information. Read Article »
New Product Development: Stages and Methods
by Rajan Sambandam, TRC
TRC identifies the best methods for each stage of the product development process, from Idea Generation through Feature Development, Product Development and Product Testing. Read Article »
Understand Choice in Banking: Use of Discrete Choice Conjoint Analysis
by TRC
Conjoint analysis provides incentive for survey respondents to determine which features must not be omitted in their final purchase. The method closely mirrors decision-making in the real world, and as shown by TRC in this white paper, is applicable to many situations including how customers choose their bank. Read Article »
Want better product ideas? Try smart incentives
by Rajan Samandam, TRC
Idea generation from survey respondents is strongly dependent on incentive. Introducing competition strengthens the quantity and quality of creative responses. TRC provides examples of smart incentives in this white paper. Read Article »
An alternative method of reporting customer satisfaction scores
by Rajan Sambandam and George Hausser of TRC
Though customer satisfaction evaluations are widely used, reporting of these scores has varied from one study to another. This is likely the result of each method’s advantages and disadvantages, as well as the personal preferences and habits of the researcher. In this article we review various reporting methods and outline our method with an example. Read Article »
Identifying the Key Drivers of Brand Image
by TRC
Measuring brand image requires looking at direct effects as well as indirect effects of a company's performance. TRC compares traditional multiple regression with SatiscanTM, a method that can review all possible path models. Read Article »
Improving Claim Satisfaction: A Case Study
by TRC
A case study on applying full-service market research to help an insurance company improve their client satisfaction with claim handling. Read Article »
Non-Response Bias In Survey Sampling
by TRC
Market research accounts for many scenarios to ensure high quality of data. One of the most overlooked problems is non-response bias. TRC describes ways to reduce its effects through survey design and data adjustment in this white paper. Read Article »
Segmentation Success
by Michael Sosnowski, TRC
This paper explains the basic building blocks of the segmentation process and its implementation. Read Article »
Survey of Analysis Methods Part I
by Rajan Sambandam, TRC
Practical marketing research deals with two major problems: identifying key drivers and developing segments. In this two-part series TRC looks at key driver analysis and segmentation. Read Article »
Survey of Analysis Methods Part II
by Rajan Sambandam, TRC
This is Part II of a series looking at aspects of practical marketing research: identifying key drivers and developing segments. This content describes specific segmentation methods: cluster analysis, neural networks, self-organizing map (SOM), and mixture models. Included is a discussion on ideas for developing good segments. Read Article »
Validating Satiscan Using A Split Sample Approach
by TRC
TRC's SatiscanTM model is tested for validity using call center data and a split sample approach. This shows that SatiscanTM produces similar models when run on random halves of an energy industry dataset. Read Article »
Satiscan and Regression Analysis: A Comparison
by TRC
The comparison shows the advantages of SatiscanTM, an analytical method from TRC, over regression in identifying the correct and cost efficient action steps. Read Article »
TURF: New Methods for Implementation
by Westley Ritz, TRC
TURF is a long-established and quite useful marketing research tool, but not everyone is familiar with how it works, or with the latest developments that can make TURF even more effective. The purposes of this paper are twofold: (1) to explain the technique and (2) to describe the latest methods for implementation.
Read Article »
Product Configuration: A Research Approach for the Times
by Rajan Sambandam & Pankaj Kumar, TRC
The marketplace has shifted in the last decade with the ability of consumers to configure the product they want. This white paper explains the basics of configuration, an approach that mimics the real world of customer driven product design to obtain insight into consumer decision-making. Read Article »
Product Configuration: Evidence for Effectiveness
by Rajan Sambandam & Pankaj Kumar, TRC
This white paper looks at the examples from one product configuration study, the kinds of information that can be derived and the possibilities provided by statistical analysis. Read Article »
New Product Research: A Dynamic Approach to Feature Prioritization
by Pankaj Kumar, Westley Ritz and Rajan Sambandam of TRC
Feature prioritization is a very common new product research problem. Over the last few years, the most popular technique has been Max-Diff. However, as the number of features increases it becomes difficult to use. Bracket is a tournament-based approach that produces Max-Diff like results and can easily prioritize fifty or more features. Read Article »
Doing More with Less: Getting Greater Value from Mobile Quant
by TRC
What “more with less” means with respect to mobile MR, and examples from traditional online studies to challenge existing assumptions about what will and will not work on a mobile device. Read Article »
How to measure the value of a brand?
by TRC
Knowing how to price your product that you can optimize your ROI is key. This video explains various ways to measure the value of a brand and talks about a discrete choice conjoint technique as a perfect approach to measuring the value of a brand. Read Article »
Product Configuration with Michael Sosnowski
by TRC
Consider a person who wants to buy a personal computer. The customer can select exactly the combination desired, subject to a price constraint. Would it be possible to use such a process for research? Read Article »
How to Improve Your Market Segmentation
by TRC
Bob Hull from TRC talks about a market research technique for market segmentation and ways of improving them. Read Article »
Rich Raquet Market Research Consulting
by TRC
Rich Raquet is introducing TRC, a research & analytics firm, specializing in new product research, conjoint, segmentation, brand equity, sat & loyalty. Read Article »
