Blind Faith Hits a Wall

We wrote a paper some time ago based on the old Chinese saying that “A man who doesn’t smile should not open a shop’. For those of you interested you can find the paper under ‘Resources’ on the Optimize Consulting web site. In brief the paper was all about the importance of customer service and the link to profitability but in reality, how can we establish that link to ROI?
Conventional wisdom amongst business people would agree that there is value in good service. An abundance of literature exists supporting the notion that service can affect retention, spending, advocacy and other customer activities that make a company more profitable.
The practical reality is that good service is expensive. We often come across clients that have the statement “to be the best” in terms of customer service and yet rarely, if ever, do they ask themselves the question if they can actually afford to be “the best”.
Good customer service requires research, training, measurement and the payout of incentives. Because it costs so much, companies struggle with the question of what their return on investment should be. A company should always ask itself if the money dedicated to improving service could be more profitably spent in some other way – profit after all creates sustainability for the organization, its employees, suppliers and customers.
The question is a fair one. Simply assuming that good service is a good investment is not actually a very sensible assumption. Strategic investment opportunities should be weighed against each other, with expected risks and returns assessed to determine the best choices and trade-offs. Unfortunately, few companies have had success calculating the ROI of customer service, making it difficult for them to determine whether their money will be, or has been, well spent.
Approaches to calculating service ROI appear to fall into two major camps: “Blind Faith” and “Analysis Paralysis”.
The Blind Faith approach begins with the unchallenged belief that good service always leads to higher profits. Companies launch service initiatives, making grand promises to their customers as they whip their staff into a frenzy of service activity. They intone ritual phrases, like, “We’re dedicated to excellence,” and “The customer is number one.” They invest heavily in the effort, confident that the profits will flow.
In the end, the outcome that they had hoped for seldom appears. Customers may be more satisfied, but the expected rise in profitability rarely occurs. There may be profit changes, up or down, but it is nigh on impossible to figure out how much effect service quality actually had on the change.
At this point many companies experience a crisis in faith and revert to their old practices such as cost-cutting, reductions in staff, new ad campaigns. Poorer but wiser, they look back at their crusade and wonder how they could have been so naïve.
The Analysis Paralysis cult takes a more mechanistic approach. This is where we see efforts to build predictive models that explain the links between service attributes, customer satisfaction and profitability. They use statistical techniques to uncover correlations and coefficients and co-variation, revealing that a twelve-second reduction in average wait times will result in a one-point rise in customer satisfaction, which will turn into a half-cent increase in per-transaction revenue at a cost of a quarter of a penny, etc., etc.
Such models can, in fact, be valuable as a means for understanding the associations among different service and profit factors. They can also provide insight into how service attributes interact with each other to influence customer perceptions. A major drawback, however, is that these models tend to have too many moving parts to function as a practical, day-to-day business tool. In addition, they tend to give the appearance of being far more precise than they actually are.
But perhaps there is an alternative. Consider for a moment the ability for a company to ask itself “What, specifically, do we want customers to do more of or less of?” Attitudes (such as satisfaction) and feelings (such as delight) aren’t included – only measurable, observable behaviors, such as, “use our service more often,” “call our support line less often,” “purchase more items on an average visit to the store,” and “return merchandise less frequently.”
The next step is to reduce the list by eliminating any items that cannot be influenced through service interactions. Working backwards, the company next makes a second list composed of specific, measurable service activities that are likely to affect desired customer behaviors. This list should only include items for which a realistic, cause-and-effect scenario between service behavior and customer behavior can be articulated. The company asks itself, “What can employees (or machines or web sites) do more of or less of, or do differently, to influence how customers act?” If it can’t be measured, if it can’t be trained, coached or programmed or if it has no likely effect on measurable customer behaviors that effect profit, it is removed it from the list.
This process of deconstruction next moves to the subject of training. What specific knowledge and skills are needed to provide the service that will affect desired customer behaviors? Then, consider incentives and measurement. What rewards will be most effective at reinforcing the use of those skills? What metrics need to be gathered to trigger rewards?
Each list is challenged to ensure that it applies only to the items on the previous list. In this way, the picture is never cluttered with irrelevant or ambiguous elements. Because every item on every list is concrete and measurable, the people who are accountable for delivering service and making it pay will know precisely what is expected of them.
The next step is to link the first list (customer behaviors) to costs and revenues. To do this the company calculates the financial effect of an incremental change in each item. For example, what would be the effect on revenue of increasing the average customer purchase by one dollar? What would be the effect on costs if the volume of complaints to call centres were reduced by five percentage points? It quickly becomes clear that even a small change in some customer behaviors can have a substantial financial impact. It also becomes clear which service changes will have the biggest effect.
At this point the company has identified the customer behaviors it wants to change, the general effect of each behavior on revenue or cost, and the dollar value of an incremental change in each behavior. In addition, it has identified the service activities that are likely to influence changes in customer behaviors, and a strategy for promoting those activities through training, measurement and rewards.
A recipe for success? We think so.