Goals of Conducting Linkage Analysis
Customer feedback professionals, as well as other business professionals, are asked to demonstrate the value of programs they have implemented. For the customer feedback professional, they are asked: Does the customer feedback program measure attitudes that are related to real customer behavior? Are the customer feedback metrics we derive from our customer feedback program (VoC) predictive of financial performance? Do customers how report higher loyalty spend more than customers who report lower levels of loyalty? To answer these questions, companies look to a process called linkage analysis.
Linkage analysis examine the linkage between customer feedback metrics derived from customer feedback programs (e.g., customer satisfaction, customer loyalty) and other sources of customer data (i.e., operational metrics and financial business metrics). By linking customer feedback metrics to operational metrics, we can identify the operational factors that influence customer satisfaction/loyalty. By linking customer feedback metrics to financial business metrics, we can identify if our customer feedback metrics are linked to real and measurable business outcomes; we will be able to determine the impact of having a specific percentage point change in customer satisfaction/loyalty scores on financial measures (e.g., revenue, sales, renewal rates).
Linkage Procedure
The procedure for conducting the linkage study is as follows. There are two data sources, each having several potential variables:
1. Customer relationship survey have these key customer metrics
- Retention loyalty
- Advocacy loyalty
- Purchasing loyalty
- Satisfaction with product
- Satisfaction with service
- Satisfaction with support
2. Business metrics include these key business metrics
- Customer tenure
- Customer defection rate
- Number of new customers
- Revenue
- Service contract renewal
- Number of sales transactions
- Number of products purchased
- Frequency of purchases
Once we have these two sources of customer data, we merge them together. The example below represents the linkage at the customer level (For a B2B example, the merging can occur at the Account level). The linkage model is displayed in Figure 1. For each customer (account), we have to pieces of information, customer feedback (x) and a business outcome (y). After this linkage is established, we are able to conduct the analysis.
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Figure 1. Data Model for Study Looking at Customer Satisfaction/Loyalty Linkage with Business Metrics |
Analyses
Several types of analyses can be employed in a linkage study. Three general types of analyses are:
- Factor analysis of the customer survey items: This analysis helps us create indices from the customer surveys. These indices will be used in the analyses. These indices, because they are made up of several survey questions, are more reliable than any single survey question. Therefore, if there is a real relationship between customer attitudes and financial performance, the chances of finding this relationship greatly improves when we use metrics rather than single items.
- Correlational analysis (e.g., Pearson correlations, regression analysis): This class of analyses helps us identify the linear relationship between customer satisfaction/loyalty metrics and financial business metrics.
- Analysis of Variance (ANOVA): This type of analysis helps us identify the potentially non-linear relationships between the customer satisfaction/loyalty metrics and financial business metrics. It is possible that increases in customer satisfaction/loyalty will not translate into improved business metrics until customer satisfaction/loyalty reaches a critical level. When ANOVA is used, the independent variables in the model (x) will be the customer satisfaction/loyalty metrics and the dependent variables will be the financial business metrics (y).
Results
The output of the analyses will illustrate the relationship between customer satisfaction/loyalty and business outcomes. When presenting the results of the analysis, I like to illustrate the relationship in graphical form. I have presented two figures below that show the relationship of customer satisfaction/loyalty metrics and important business outcomes.
Figure 2 illustrates the relationship between customer satisfaction with technical support and downsizes in maintenance renewals. We found that accounts who are dissatisfied with Technical Support in Q3 2001 are more likely to result in downsizes in maintenance renewals for Q4 2001 compared to accounts who are satisfied with Tech Support in Q3 2001.
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Figure 2. Relationship between customer satisfaction with technical support and downsizes in maintenance renewals. |
Figure 3 illustrates the relationship between customer satisfaction with technical account managers (TAMs) and revenue. Accounts that are more satisfied with Technical Account Management (TAM) performance in Q1 2001 result in significantly higher revenue from maintenance renewals in Q4 2001 compared to account that are dissatisfied with TAM performance in Q1 2001.
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| Figure 3. Relationship between customer satisfaction with Technical Account Managers (TAMs) and revenue. |
With these two figures, we see that scores on our customer survey (customer satisfaction with tech support and satisfaction with technical account managers) are, in fact, predictive of future business metrics (maintenance renewals and revenue, respectively).
These linkage analyses helped demonstrate the value of using the results from the customer survey to help us manage customer relationships. We know that, by increasing satisfaction with support-related areas, the company can expect to significantly improve their business performance.
