Ideas for Leaders #507

When Does Downsizing Hurt Customer Satisfaction?

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Key Concept

Past research based on B2B industries show an intuitive link between downsizing the workforce and lower customer satisfaction. A new study focused on B2C industries reveals that the link is more complex, depending on factors such as organizational slack, labour productivity, and the emphasis on innovation. The study does confirm that downsizing reduces customer satisfaction, which then reduces financial results.

Idea Summary

Research has shown that downsizing will often have a direct negative impact on customer satisfaction, and this negative impact results in a decline in business performance. This causal chain seems clear and intuitive. New research confirms the assumption that downsizing hurts customer satisfaction, and as a result, reduces the financial performance of the company. However, it also reveals that the relationship between downsizing and customer satisfaction is more complicated than previously argued. By expanding beyond the business-to-business industries in past research to focus on business-to-consumer industries, the research demonstrates that different variables can influence whether downsizing has a net negative (through lower customer satisfaction) or positive (through lower costs) impact.

These variables fall into two categories: related to resources or related to customer information processing.

One resource variable studied was organizational slack, which refers to the ratio of resources available to resources required. For example, if a company has more employees than they need to effectively produce their products, downsizing does not result in less customer satisfaction — and in fact, if downsizing reduces costs, customers will be more satisfied.

However, if there is little organizational slack, the opposite occurs: downsizing stretches resources that are already stretched, and the impact, through poor customer service, low inventory or other issues, results in less customer satisfaction.

Another variable is high labour productivity, which is usually associated with employees who feel highly involved and loyal to the company. Even for those who survive downsizings, the layoffs feel like a betrayal of employee loyalty, and as a result employees are likely to become more disengaged. Customers feel the impact of such disengagement.

A third variable related to resources is R&D intensity, which refers to the importance of innovation to the company’s success. Downsizings will systematically inhibit innovation — employees who fear for their jobs are less likely to take chances or veer from what’s been done in the past, for example — and eventually this lack of innovation will lead to less satisfied customers.

Variables related to customer information processing concern the information that customers seek before making their purchasing decisions. One such variable, for example, is brand consciousness. If customers are loyal to a brand, they are more likely to forgive some of the consequences of downsizing, such as low inventory or even running out of stock. Thus, high brand consciousness reduces the negative impact of downsizing on customer satisfaction.

Another variable related to customer information processing is risk importance, that is, the negative consequences of making a bad choice. Risk importance will cause customers to do more research, perhaps discovering the history of downsizings in the company or perhaps noticing deteriorations in quality. Risk importance increases the negative impact of downsizing.

Another customer-related variable, the probability of error, concerns customer perceptions that they are likely to make a mistake in their product choices. This implies that customers aren’t really in a position to evaluate the quality of a product, and are thus less likely to notice quality deterioration. Unlike high risk importance, high probability of error decreases the negative impact of downsizing.

A number of other resource and consumer variables were studied, including (but not limited to) product categories that customers consider hedonistic or businesses for which customer service was important. The analysis showed no significant impact on customer satisfaction in these and other instances.

Business Application

The supporters of downsizing declare that companies become more productive, resulting in lower costs to customers. Opponents of downsizing argue that downsizing reduces the efficiency and effectiveness of companies (e.g. worse customer service, less innovation), which reduces customer satisfaction and, subsequently, impacts the bottom line.

The new research argues that neither generality is correct, and as a result, offers important guidelines for managers when considering the positive and negative impact of potential downsizing on their organizations. The study confirms that downsizing can have a significant negative impact on customer satisfaction, which is in turn reflected negatively on the bottom line. However, this negative impact occurs if companies have little organizational slack, a workforce that is highly productive, and/or is driven by innovation. Downsizing will also have a negative impact on customer satisfaction in product categories with low brand consciousness, low probability for consumer error or high-risk importance.

Awareness of these variables can help companies reduce the negative impact of downsizing their workforces. For example, in product categories viewed as risky purchases, marketing managers can launch initiatives (such as satisfaction guaranteed) during a downsizing. Likewise, managers can also emphasize brand communication during a downsizing, since high brand consciousness reduces the downsizing fallout.

The bottom line: consider these variables carefully. Depending on the resource and consumer-related features of your company and its products, downsizing can carry hidden costs, in terms of lower customer satisfaction, that can undermine or destroy any cost-saving benefits. 

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Authors

Institutions

Source

Idea conceived

  • August 2014

Idea posted

  • April 2015

DOI number

10.13007/507

Subject

Real Time Analytics