The big advantage of surveys is that they are cheap and easy, and anyone with access to Survey Monkey or similar software create one. The biggest problem with surveys is that most people don’t respond or fill them out, and those who do tend to be really happy or really unhappy, provided an inaccurate reading of overall satisfaction of all customers. A 30 percent return rate on a survey is considered good, and most don’t even get that. Some of the other problems with surveys are:
One big advantage of surveys is that it is easy to compare your organization to others in your industry as long as everyone uses the same survey. J.D. Power and other firms like Gallup do surveys of customers for most of the companies in a number of industries such as hotels and hospitality, wireless phone service, Internet providers, automobiles, and others. By using one of these widely accepted surveys you can better set targets for your own performance and see how you compare to peers and industry benchmarks. This information tends to be very useful. However, as I have mentioned numerous times throughout this book, you can’t manage with a measure that is tracked once a year. And no survey tends to capture 80 percent or more of your customers. Even if you had a survey that 100 percent of customers responded to, it is still a survey of someone’s opinions at a given point in time. Further, responding to a survey takes up valuable time of your customers, who probably have other things to do besides help your business improve.
All organizations have customers, whether they are businesses, consumers, students, or patients. Similarly, all organizations do things that aggravate and frustrate their customers—people paying you money for a product or service. If your organization has more than 25 employees, you might want to consider this metric. Another factor to think about is whether your customers will fill out your surveys. Many businesses will not bother filling out surveys from their vendors or suppliers, and some even have policies against it. Even if you are doing a survey and getting back 20 to 30 percent of the ones that are distributed, you are still not measuring the satisfaction of the vast majority of your customers. While customer surveys are good for measuring the people who are really satisfied or dissatisfied, those who do not have strong feelings one way or the other are not being measured. Another criterion for selecting this metric is if there are operational factors that can be easily tracked that you know impact customer satisfaction. For example, waiting time in a doctor’s office, hold time or hang-ups in a call center, or product returns for quality problems are all things that could be measured every day and are important to customers. An organization that might not want to bother with this metric is one that has a product or service that is so unique and popular that customers will put up with all sorts of aggravation in order to get it. However, no matter how unique and popular your product or service is, someone will undoubtedly rip you off and try to come up with something that is better and cheaper. I love my iPhone and am on my third one, but I did take a look at the new Samsung Galaxy this time, which is a very cool product. So my point is that even if you have a unique product or service now, it won’t last, and you might want to measure how easy or hard it is for customers to do business with you.
The two most direct impacts of customer aggravation are loyalty and future revenue. Aggravated customers are not only less likely to be loyal, but they might just decide to spend their money on one of your competitors at the next opportunity. Even a customer who is only slightly aggravated is more likely to be lured away by a competitor and probably won’t fill out your surveys to let you know this. What companies don’t seem to track or be able to predict is how mad they have made customers. Losing a suitcase and recovering it 24 hours later may be a minor inconvenience for one customer, but it could be enough to make another customer never fly that airline again for the rest of his or her life. My friend Rob and his wife were flying to France for an important business contact’s black-tie wedding. The airline lost their suitcase with Rob’s tux and his wife’s dress. Of course, they were dressed in jeans for the flight, and when they arrived in Paris they had no clothes for the wedding. Purchasing new outfits was not an option because there was not time, stores were closed, and it would have cost thousands that would not have been reimbursed by the airline. The suitcase showed up about 12 hours after the wedding, but it didn’t matter at that point. This one event made Rob and his wife so angry at the airline that they will never fly it again. Ten years and several other long trips later, they have maintained their allegiance to Southwest and Virgin. You can bet that they have told this story for years to probably hundreds of people, who have told others, and then it ends up in a book!
One lost customer can result in the loss of thousands in revenue. Think of the impact if my friend Rob happened to be the CEO of a big company with 50,000 employees! CEOs have been known to change policies after such events in a refusal to spend any more company money on vendors that screw up like this. I would guess that the more important a person is, the less likely they are to fill out customer surveys, either. Can’t you just imagine the frustrated airline sales executive trying to explain to his boss how one lost suitcase cost them the entire account? Chances are, without the type of data I am proposing in this chapter, the airline executive would have no way of knowing why the company stopped buying tickets.
I am sure that the airline only knows that it lost X number of bags that day and that X percent were recovered. If that number was in its normal range of bags misrouted, it was a good day according to the airline’s scorecard. What the airline needs to know is, how many people did it make angry today who have given it money for air transportation, and how angry did it make them? Of course, a small minority of those mad people may decide to fill out their survey or write a complaint letter, but most will not.
Angry customers also have an impact on your brand. With the power of social media these days, consumers can tell thousands or more about your horrible service or shoddy product, which can have measurable impact on your brand image, which will in turn directly impact your revenue and profits.
Depending on your approach, the cost and effort required to collect this data is probably medium to high. Of course, the value is also high, as is the integrity of the data since you will be measuring objective variables versus perceptions and opinions. If you deal with consumers as customers and have thousands of them, you may need additional software and hardware to handle the big data that could be generated. However, for now, understand that big data usually means big money. The reason big data might be necessary is that you had to track thousands of transactions for thousands of customers. If your business is a restaurant that serves 100 people a night you need not worry about tracking this, but for a software company with tens of thousands of users and hundreds of corporate customers, you might need to spend some dollars to start keeping track of how many customers are aggravated on a daily basis.
On the other hand, I have had a number of clients start tracking this index with very little cost. Often the factors that go into the customer rage index are already tracked via operational or quality metrics, so it is just a matter of assigning weights and rolling them into an index. There is some upfront cost to pull all this together, but the overall implementation does not require new hardware or software and can be done fairly quickly. The cost and amount of effort totally depend on how many of the factors that go into the index are currently tracked.
It is vitally important for an organization to measure customer satisfaction and to try to predict customer loyalty. It is also important to detect even minor dissatisfaction levels so something can be done to improve the relationship and keep the customer. An important point to remember about this measure is that it is not based on surveys or opinions. Customer perceptions and opinions are gathered up front to construct the index, but the index is comprised of objective factors that aggravate or frustrate customers that can be counted hourly, daily, or at least weekly. The rage or aggravation index is not a survey!
For example, look at FedEx, which was the pioneer in developing a daily metric that tracks how many customers it made mad on a daily basis and how mad it made them. By holding focus groups with customers from a variety of industries and locations, they gathered a long list of things FedEx had done to aggravate them over the years.
Once FedEx narrowed down this list to a reasonable number of problems, it had customers rank-order them from the most maddening to least, assigning 1-to-10 severity ratings. It turns out the 10, or worst thing FedEx could do, was lose a package and never recover it. A minor aggravation rating of a 1 or 2 might be a package that is an hour or two late. Every day, FedEx tracks occurrence of these problems, multiplies the frequency by the severity, and rolls it up into an index that measures customer aggravation levels. It turns out that this index is directly correlated to disloyalty.
Big surprise: If you make customers angry enough, they take their business elsewhere and usually don’t bother filling out your survey.
Milwaukee airline Midwest Express liked the idea of a daily metric that predicted customer loyalty and already had data on many of the factors that frustrate customers. Other than for a crash, the 10 rating is for an airline customer to use when the airline cancels the last flight of the night when you are on your way home, every hotel room in Chicago is sold out, and you have to sleep at O’Hare until you can get a flight the next morning. A minor aggravation for an airline customer might be having to check his or her carry-on luggage because the bins are full or getting placed on hold for 20 minutes by a reservation agent.
DTE Energy, the electric and gas company in Michigan (formerly Detroit Edison), liked the idea of an aggravation index as well and started tracking power outages, billing errors, and other factors on a daily basis. Discover Card in Chicago adopted this metric for a while as well and tracked call center waiting time, average call length, billing questions, handling of fraud, talking to someone in their call center with a foreign accent, and other factors that many people find aggravating. A big frustration for Discover Card customers was trying to use the card and finding out it was not accepted by the merchant. Although this is a big source of frustration, it is impossible to measure since the clerk or person taking the card will simply ask for a different one. Discover Card closely tracks the number of merchants it has and the number of transactions, but it has no way of tracking how many times the customer tries to use his or her Discover Card and is told it is not accepted. When deciding on the factors that get tracked in your own customer rage index, it is important not only to narrow them down to things that really bug customers but also things on which you can collect data. Amazon or my online vitamin company can track how many times it is out of stock on an item. However, Target might not have a way to measure how many times customers look for their brand of dog food and can’t find it.
Going to the hospital can be a maddening experience, but hospitals can do a lot to lessen the amount of frustration and aggravation they cause patients and their families. Some of the variables that can be tracked daily that might go into this index include:
I’m sure you can think of others, but this is just an example list of possible factors that might go into a patient aggravation index. Talking to family members and patients might reveal another list of aggravation factors, such as not answering the phone at the nurses’ station, getting incomplete updates regarding the patient, limited seating in room and waiting areas, and so on.
The biggest downside of a metric like this is that it does not provide data on overall satisfaction levels or situations where you might have surprised and delighted a customer—it only measures screwups.
However, I think the list of advantages far outweighs the limitations:
The basic process is to talk with customers to determine the factors that make them angry and to assign a severity weighting to each event or factor based on the degree to which it aggravates them. Step 1 is to hold a series of focus groups with customers from various market segments and spend about 45 minutes getting them to brainstorm things that your organization or one of your competitors has done to aggravate them or make them angry. The goal is to get them to list at least 50 things without going into stories or details. Write down everything on a whiteboard or flipchart. Step 2 is to get everyone to select their top 10 biggest aggravations, the things that would make them really think about never doing business with your company again or buying your product. Often you will see that there is a great deal of overlap in the top 10. Then have everyone indicate which one of the 50 or so factors made it to their top 10 list to come up with a group top 10 (Step 3). Sometimes there may be some duplication, so these factors can be combined. Once the group top 10 has been identified, Step 4 is to have everyone rank-order the group top 10 list from worst to least aggravating. It helps to start by identifying the 10, or worst thing. In FedEx it is losing the package and never recovering it. In a restaurant it might be getting seriously ill as a result of the food. In a call center it might be getting disconnected after waiting on hold for 45 minutes on your fourth call for the same problem. Focus group participants work to complete their ranking from the most to least serious. This information is then used later to assign importance weights to each factor from 1 to 10. So 15 lost packages gets multiplied by 10 and equals 150. Twenty-eight damaged packages gets multiplied by 9 and gets a 252, and so forth.
The process is then repeated with other focus groups until you start getting consistent data, and then it is repeated every year or two to see if customer priorities have changed. As quality improves with most things, customer expectations become higher, and what might have been a minor aggravation before is now a big deal. It can work the other way as well. Most of us have such low expectations about air travel these days that we just know that it will be a mostly unpleasant experience. Consequently, it may take something major to really get our blood boiling.
The basic formula is to count the number of events that occurred (e.g., power outages, dropped calls, hold time longer than 30 minutes, canceled flights, etc.) and multiply each event or occurrence by the 1–10 severity factor. By counting both the number of occurrences and events and multiplying each one by a weighting factor, you can gauge how angry or frustrated your customers are on a daily basis. Generally, this measure is tracked daily in most organizations that have adopted it.
Some organizations have opted for a more sophisticated analytic that considers three factors:
This tends to make it more compelling data if we know that the power was out for three hours for a big area employer that buys 5 percent of our total power, versus the power going out at night for a few households. The way this works is that the number of negative events gets modified by adding two multipliers. So if we have one canceled flight and it is the last one of the night, that is a frequency of 1 × a weight of 10. The importance of customers (180 passengers with 28 Million Mile Flyers) might be 8. So the one canceled flight gets a total of 10 × 8 = 80 points on our analytic.
Once you have established baseline levels for each of the top 10 negative events, you can begin gathering comparative data and benchmarks to help set targets. You can also use customer input to help set targets. Targets are generally set for each individual negative occurrence, and then you can set a target for the overall index once you get a stable baseline.
The biggest benefit of having data on how many customers you make angry on a daily basis is that you can more accurately predict their loyalty than some survey questions that ask customers about their future loyalty. Another major benefit of this data is that you push responsibility for customer satisfaction and loyalty down through the operational side of your organization. Often people in manufacturing and operations are disconnected from customers and feel no real accountability for their satisfaction. Salespeople or account managers may be responsible for landing new customers, but the product or service delivered is what will determine whether or not they are loyal.