A Guide to ROI in Customer Service


Most 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, word-of-mouth endorsements and other customer activities that make a company more profitable. However, there are also many examples of companies with excellent service that chronically suffer from poor financial performance.

Good service is expensive. It requires research, training, measurement and the payout of incentives to managers and employees. Because it costs so much, companies struggle with the question of what their return on investment should be. Some even ask whether the investment is worth making at all. Could the money dedicated to improving service be more profitably spent in some other way?

The question is a fair one. Simply assuming that good service is a good investment is not very businesslike. Investment opportunities should be weighed against each other, with expected risks and returns assessed to determine the best choices. 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 “Rube Goldberg”.  Rube Goldberg was a Pulitzer Prize winning cartoonist, famous for his outrageous “inventions”, which performed simple tasks in complicated ways.  He produced cartoons similar to this:

Faith in Service

The Blind Faith approach begins with the unchallenged belief that good service always leads to higher profits. Companies launch service crusades, making grand promises to their customers as they whip their staff into a frenzy of friendly service activity. They intone ritual phrases, like, “We’re dedicated to excellence,” and “The customer is number one.” And they contribute a substantial amount of money to the effort, confident that it is all going to a good cause.

In the end, the miracle 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 devilishly difficult to figure out how much effect service quality had on the change.

At this point many companies experience a crisis in faith and revert to their old practices: 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 naive.

The Service Machine

The Rube Goldberg camp takes a more mechanistic approach. These folks don their white lab coats and attempt 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. Many companies have spent considerable effort and money constructing such models, only to find that their applicability is marginal and their useful lifespan limited.

The Third Way

There is another approach  –  a third way that is simple, practical and intuitive. It does not purport to be as precise as the Rube Goldberg predictive models, nor does it treat service as sacrosanct, as with the Blind Faith followers. Rather, the Third Way takes the view that service isn’t profitable because it’s good, it’s good because it’s profitable.

The Third Way begins with the company making a list of customer behaviors that directly affect revenues or costs. The company asks 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 plausibly be influenced through service interactions. (Note that service interactions do not necessarily involve employees. ATMs, web sites and unmanned kiosks are all, from a customer’s point of view, service providers.) 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. Again, attitudes and feelings are not included. 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 (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, 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 winnowed 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 effect on costs if the volume of complaints to call centers 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.

The company has thus far 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. All of these steps can be accomplished in a day with a few managers and pot of strong coffee. But from this point on, the process gets a bit trickier.

The major element missing from the formula is magnitude. How much change can the company expect to create? Can complaints be reduced by 1%, 5%, 10%? Will average purchase amounts increase by 50 cents? Ten dollars?

Also missing is the interaction among different variables. For example, aggressive up-selling may lead to a 10% increase in the average transaction amount, but it could also lead to a 2% increase in customer turnover, which might counteract the benefit.

The only way to answer these questions is to experiment. The company must identify the most promising service investments and test them on a small scale. Service units (stores, call centers, etc.) should be compared, using test and control groups. The groups should be small enough to keep the experiment manageable, but large enough to wash out the influence of temporary, outside factors, such as bad weather or a blow-out sale by a competing store.

The experimentation does not stop with one test. The process is iterative, allowing the company to fine-tune its tactics and find the optimal mix of service activities that result in the highest return on investment. With patience, a reliable formula for ROI will emerge and the company can decide which service improvements it should invest in  –  or whether it should invest in service improvements at all.

To return to an earlier statement: The Third Way takes the view that service isn’t profitable because it’s good, it’s good because it’s profitable. This doesn’t mean there is no benefit to customers. On the contrary, the types of behaviors desired of customers will only come about if they are satisfied, loyal and occasionally delighted. The point is, companies cannot make the world a better place for customers unless they show a profit. By defining good service in terms of its effect on the bottom line, everybody wins.

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About Eric Larse

Eric Larse is co-founder of Seattle-based Kinesis CEM, LLC, which helps clients plan and execute their customer experience strategies through the intelligent use of customer satisfaction surveys and mystery shopping, linked with training and incentive programs. Visit Kinesis at: www.kinesis-cem.com

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