Business Linkage Analysis: VOC – Constituency Metrics

Business linkage analysis is the process of linking disparate business data sources together for the purposes of understanding the relationships between them (See Figure 1). Constituency linkage analysis allows us to better understand how employees and partner relationships impact the health of the customer relationships. The service profit chain depicted in Figure 2 (I added a partner variable and expanded customer loyalty) supports the idea that employee and partner management is key to ensuring customer loyalty and business growth. As this model illustrates, business growth is dependent on customer loyalty (and perceived customer value and satisfaction), which is, in turn impacted by employee satisfaction/loyalty and partner satisfaction/loyalty. In fact, empirical studies to support this model employ linkage analysis as a way of identifying and quantifying the linkages among the constituencies.

Demonstrating the statistical relationship between customer feedback metrics and constituency metrics is useful for three related reasons:

1. Understand the impact of employee and partner experience on the customer experience: Constituency linkage analysis helps you identify which employee/partner metrics are statistically linked to customer satisfaction.
2. Improve the health of the customer relationship by improving the health of the employee and partner relationship. Managing customer relationships does not occur in a vacuum. Understanding how these other constituencies impact customer satisfaction and loyalty helps senior executives allocate the right resources across the entire ecosystem to drive business growth. For a company to grow, they need to manage all different types of relationships (employee, partner, customer); a problem in one could impact the rest.
3. Help build a customer centric culture. Constituency linkage results can help executives communicate the importance of the entire ecosystem in driving customer satisfaction and loyalty. Evangelizing how important the employees and partners are in helping ensure customers receive a great customer experience can be supported with these linkage studies.

Constituency and Customer Feedback Metrics

There are many types of constituency metrics that are commonly measured. Any metric tracked by human resources (HR) are candidates for employee metrics that we can use in linkage analysis. Additionally, many partner metrics (e.g., certification status, revenue) are candidates for partner metrics. Some example metrics used in constituency linkage analysis include:

In constituency linkage studies, we can use customer feedback metrics from either a relationship-based or a transaction-based survey, as long as customer metrics can be aggregated at the appropriate unit of analysis (typically at the employee or partner level).   So, for any given constituency, we can have two kinds of information, 1) their employee/partner metric (sat, loyalty, HR) and 2) customer’s satisfaction/loyalty metric.

Linking Constituency Metrics to Customer Metrics

Once we have these two sources of business data, we organize the data at the employee/partner level (each employee has a score on the constituency metric and a customer feedback metric. Linking an employee metric to a given employee is straightforward. There is a 1 to 1 ratio of employee to employee metric (Employee 1 has Employee 1 Metric; Employee 2 has Employee 2 Metric).

Considering the customer feedback metric, a given employee could have more than one customer response associated to him/her. There is a 1 to many ratio of employees to customer feedback responses. The customer metric for each employee, then, would be the average of the customer responses for that employee. In large B2B enterprise companies, a specific employee (Account Managers, Technical Account Managers, Consulting Services) can be assigned to specific Accounts. Therefore, each Account Manager could have multiple customer feedback responses associated to him/her; The customer metric for this employee would be the average rating across all his/her responses. In a B2C environment, call center agents can be linked to several, specific customer interactions; each employee’s customer metric would be the average rating across all his/her interactions.

The bottom line is that employees/partners can have feedback from multiple customers. If you are able, obtaining all relevant customer datapoints for each employee/partner results in more reliable measures of customers’ attitudes. So, if an employee had customer satisfaction ratings from 10 customers, his customer metric would be the average of those 10 customers.  The data model in Figure 3 represents the linkage at the employee level. For each employee, we have two pieces of information, constituency metric (x) and customer feedback (y – average over the different customers the employee served).

Results

The output of the analyses will illustrate the relationship between the constituency metrics and the customer metric. When presenting the results of the analysis, I like to illustrate the relationship in graphical form the segment the constituency metric into homogeneous segments (e.g., low employee satisfaction / high employee satisfaction) and compare the customer metric across each employee group. Below are three figures that show the relationship constituency metrics and customer metrics.

In an enterprise software company, the Technical Account Managers (TAMs) are responsible for implementing the software into the company’s infrastructure. Figure 4 illustrates the results of a linkage study that examined the how Technical Account Managers’ attitudes toward the company impacts customers’ satisfaction with the implementation. We found that TAMs that were highly satisfied with their company also had customers who were more satisfied with their implementation. It appears that, to ensure customers are satisfied with their implementation, the company needs to ensure their TAMs are satisfied.

Figure 5 illustrates the impact of training on customer satisfaction. Employing a linkage study, this company was able to link training metrics and customer satisfaction metrics for specific TAMs.  The company was spending much money on employee training and wanted to determine its effectiveness on improving customer satisfaction. As you can see in Figure 5, TAMs who completed 4 or more courses for a given quarter had customers who were more satisfied with their performance than TAMs who completed 1-3 courses.

In the same enterprise software company, business partners (system integrators) were used to help integrate the software into joint customers’ accounts. To understand how partner relationships impact joint customer relationships, the company wanted to examines the linkage between partner and customer metrics. Because a given Account had only one system integrator, we could easily link partner metrics and customer metrics via partners (system integrators). Figure 6 illustrates this relationship. The company found that customers were much more satisfied with the implementation when the system integrator report high loyalty to the company.

The results of these analyses helped senior executives evangelize the importance of employees and partners in delivering great customer service. The senior executives were able to determine the value of employee training on customer satisfaction and set appropriate training goals that ensured customers were satisfied with the implementation of their solution. The partner-customer linkage study supported the need for a formal partner relationship program. The company implemented a more formal partner program in which partner survey results were used to improve partner satisfaction.

Summary

Constituency linkage analysis helps show how customer loyalty is a function of the entire ecosystem; successfully managing customer relationships includes effective employee relationship management and partner relationship management.  In the examples, the linkage analyses showed us that employee and partner metrics are leading indicators of customer satisfaction and loyalty.