An Intro to the American Customer Satisfaction Index

What do airlines, large banks and telecom corporations have in common? They are among the least-liked companies in America.  How do we know? The American Customer Satisfaction Index (ACSI) tells us so. It’s the only uniform national measure of satisfaction with goods and services across a representative spectrum of industries and the public sector. The ACSI utilizes patented methodology to identify factors driving customer response and applies a formula to determine the cause-and-effect relationship between those factors and satisfaction, brand loyalty and overall financial health of a company.

ACSI data allows companies to reach informed decisions about current products and services and also make projections about changes under consideration. It’s a tool for managers to improve satisfaction and build customer loyalty and a means to evaluate competitors. ACSI scores also help investors evaluate the present and future potential of a company. Historically, stocks of companies with high ACSI scores outperform lower-scoring firms.

Developed by researchers at the University of Michigan and first published in 1994, the ACSI releases full results on a quarterly basis with monthly updates. The survey rates satisfaction with 225 companies in 47 consumer industries and more than 200 programs and services provided by federal agencies.  Data about customer satisfaction is gathered from random telephone and email interviews with 250 customers. To generate ACSI results, over 70,000 interviews are conducted each year. Consumers respond to questions about a company by rating three factors on a 1 to 10 scale: Overall satisfaction, fulfillment of expectation and relative comparison to an ideal product or service. Companies are chosen for scoring based on total sales and position within their industry. As company fortunes wax and wane, some are deleted from the survey and others added.

In addition to rating individual companies, the Index generates overall scores for 43 industries, 10 economic sectors plus a comprehensive national customer satisfaction score—now considered a significant metric for the health of the economy at large.

The scores from the American Customer Satisfaction Index are awaited by companies, economists, investors and government agencies alike. Some of the general conclusions gleaned from the results include:

  • Variations in customer satisfaction indicate the mood of consumers and accurately predict their readiness to buy products or services.
  • Since consumer spending makes up the majority of the national gross domestic product (GDP), spikes or dips in ACSI scores serve as an early warning to fluctuations in GDP.
  • Quality, not price, is the primary factor generating customer satisfaction in most industries scored by the ACSI.
  • High-profile mergers, acquisitions, large layoffs and other internal uncertainties degrade a company’s customer satisfaction score.
  • Service industries are generally positioned for lower ACSI scores than the manufacturing sector.

Around the world, many countries are implementing surveys based on the ACSI model. In the future, ACSI methodology may evolve from a one-nation metric to a global quantification. As national economies expand into worldwide markets, international data on consumer satisfaction and a company’s—or a country’s— relative success in fulfilling it will prove vital.

Correlation and Regression Analysis – A Primer

Welcome back to Making Molehills out of Mountains University. For years data analytics have been my passion. I have spent years looking at human behavior and applying statistical analysis techniques to answer two primary business questions every CEO has, “Should I do X” and “If I do X what will happen?”  There is a third question they often ask, “I did X, what happened? It was not what I expected.” But that’s usually asked when something like New Coke flops, uh, I mean, doesn’t meet expectations.

My favorite tool, admitting my bias, is the mTab suite of analysis tools.  In the past ten years, mTab has become the standard in the automotive industry and has contributed, in my considerable professional opinion, to have a profound effect on the industry’s recovery.  After all, they’re now producing cars people are excited to buy.

Sorry, I digress. This is the 2nd class in Market Research Data Analysis 101. I teach in plain English, or as plain as possible considering the subject matter. In later classes we can do the math.  So, put away your smart phones, get out your tablets and learn something.

Today I introduce you to the lovely world of Correlation and Regression analysis which are two of the most commonly used techniques for determining the relationship between two quantitative variables.

Correlation Analysis

Assuming you’ve collected your data the first step is to create a scatter diagram.  Variable 1 is the X-axis and the other is the Y axis. The resulting diagram indicates the linear relationship between the two variables.  The closer they are to a straight line the stronger the relationship.  The linear relationship is defined as positive, negative or null and is expressed by a correlation coefficient or +1, -1, or 0.

A positive relationship means that a change in one variable has a positive effect (increase marketing budget = increase in sales). The converse is true for a negative relationship (increase in price = decrease in sales).

Coefficient = 0                              =+1                    Between 0 & -1

Seems straightforward. But, remember, we are not talking about causation here.  There may be a third variable that accounts for the relationship (e.g. Tax refund check came through at the time of increased marketing).

Regression Analysis

Now that you know there is a relationship between two variables what do you do with that?  As future high falutin analysts you’ll want to predict the Key Drivers and report them to your CEO.  She’ll want to know, “If I decrease price will I sell more product?”

Enter linear and non-linear regression.  Simply put, if a change in X (independent variable) equals a consistent change in Y (dependent variable), then the relationship is linear.  If the change in Y is inconsistent then the relationship is nonlinear. For Regression analysis there is an assumption of linearity.  IF the scatter diagram indicates a nonlinear relationship there are mathematical techniques that can be used to obtain linearity.

Assuming price and units sold is a linear relationship, using standard regression analysis techniques, the analyst should be able to predict the number of units sold at a particular price point.  This also assumes, for the sake of this exercise, that the relationship is positive and the correlation coefficient is +1 or close to +1.  The stronger the coefficient the better predictive quality of the data under regression.

I know.  I said, no math. But you should be able to handle this:

Y= a+bX

A and b are the intercept and slop  (unknown constants).

In this case, X = Price and Y = units sold.  As the equation suggests a change in Y will equal a change in X.

Careful!  If you write the equation backwards, X= c+dY then you might tell your CEO that price is affected by the number of people buying cars and not the other way around!

What? You say that if I sell more cars I can lower the price due to cost efficiencies in production?  Of course, that is true, but that does not change the reality that, without an external action, price does not change by itself as production increases. But quantity sold can change as price is changed without any additional action.

Conclusion

That’s it for today.  There is a whole lot more to study regarding correlation and regression but we’ll save that for another day.  Now that you know that correlation and regression are impressive tools for identifying relationships between variables and for determining the strength of that relationship, go get some data, create a scatter graph, do a little algebra and impress your boss how knowledgeable you are as an analyst.

The Difference Between Causation and Correlation Research

Correlational research can tell you who buys your products, but it may or may not tell you why. For example: Let’s say that you are trying to sell instant meals. If your research tells you that working mothers buy more instant meals, you cannot draw the conclusion that being a working mother causes people to purchase instant meals. The purchase of instant meals could be due to a third factor such as being too busy to cook, having extra money, or trying to control calorie intake.

More complex correlational research studies can help you narrow down the causation, but you still cannot draw a conclusion from them with absolute certainly. In fact, the more factors you add, the more confused you may become. Let’s say you discover that only single working mothers purchase instant meals and that married working mothers rely on their spouses to cook. Should you try to market instant meals to the spouses of working mothers, or could there be yet another factor involved that is still eluding you?

Knowing for sure

If you really want to know whether being a working mother causes people to purchase more instant meals, you need to conduct a controlled experiment. This means that you would need to have a large pool of subjects, randomly assign them to be working mothers, and monitor their purchasing habits. Unfortunately, we cannot ethically force people to be or not to be working mothers. This is a lifestyle choice, and an experiment where we let the subjects choose their own treatment would not be an experiment at all.

A good way to dig deeper in your research without dealing with ethical issues is to ask an open-ended question. It may take you more time to read through all of the responses, but with the right software you can make the process much faster. You can find out how often a certain keyword or phrase is used to determine why people purchase instant meals. If a common response is, “I am a working mother,” you can be reasonably certain that being a working mother causes people to purchase instant meals. While this is still technically a correlational research study, it can give you more useful information than a survey in which you only allow simple, discrete responses.

4 Main Types of Segmentation in Market Research Analysis

Segmentation is the process of dividing potential markets or consumers into specific groups.  Market research analysis using segmentation is a basic component of any marketing effort. It provides a basis upon which business decision makers maximize profitability by focusing their company’s efforts and resources on those market segments most favorable to their goals.

There are four main types of segmentation used in market research analysis: a priori, usage, attitudinal and need.

a priori (most commonly used)

a priori is defined as relating to knowledge that proceeds from theoretical deduction rather than from observation or experience. For purposes of market research analysis this means making certain assumptions about different groups that are generally accepted as pertaining to that group.  For example, deducing that adults over 50 are not as tech savvy as twenty somethings is a safe assumption based on the reasoning that high tech devices were not as widely available to the older generation than they are to the younger. However, be careful to check your assumptions since they can change over time. In 30 years, that statement may no longer be true.

Usage Segmentation (also used frequently)

Usage segmentation is completed either by decile or pereto analysis. The former splits the groups into ten equal parts and the latter distributes according to the top 20% and the remaining 80%. Usage segmentation helps to drill down more deeply then a priori because it indicates which priori group is the heaviest user of your product.

Attitudinal (Cluster analysis)

Using cluster analysis to create customer psychological profiles is difficult because it is limited by the input data used.  Demographic data is the least helpful, whereas preference data (scaling) is better suited toward this type of analysis.

However, once a usage segmentation is created, it’s exceptionally helpful to know the motivating factors behind the purchasing decisions of the heaviest users of your product.

Needs Based Segmentation

Needs based segmentation is the concept that the market can be divided based on customer need.  This type of analysis is used to develop products that sell rather than trying to sell products a business developed.

Needs based segmentation uses conjoint analysis to separate the groups according to functional performance.  Using cluster analysis, it’s goal is to determine the driving forces behind the performance data.

Knowing which segmentation to use is often as critical as the analysis itself because it is driven by cost and the stated business goals of the decision makers.

Correspondence Analysis – what does it all mean?

One of the most commonly used forms of Brand mapping is correspondence analysis. Brand maps are often used to illustrate customers’ images of the market by placing products and attributes together on a map together on a graph. This allows close interpretation of company perceptions with a variety of product and service attributes that are closest to them on the map. If products are placed close to each other on the graph, it means they have a similar image or profile in the market. This helps to inform strategy, like where to take the client’s product in the future.

Some questions that can be answered with a correspondence analysis are:

- What attributes does the brand own?
- What attributes do competitors own?
- Are there gaps in the market that may be filled by the client brand?
- How should the brand be positioned to be both relevant to the market and differentiated from the competition?

Correspondence analysis is an exploratory data analytic technique that allows rows and columns of a cross-tabulation (two-way and multi-way) to be displayed as points in two-dimensional space. The information from the cross-tab, as seen in the example below, is usually collected using simple multi-coded grid questions or semantic rating scales (typically 5 or 10 point scales).

It reduces a complicated set of data to a graphical display that is immediately and easily interpretable. The graph contains a point for each row and each column of the cross-tab. Rows with similar patterns of counts produce points that are close together, and columns with similar patterns of counts also produce points close together.

Running a correspondence analysis on the above cross-tab yields the following graphical display:

In this example, McDonald’s is more synonymous with Low Prices. Pizza Hut and El Pollo Loco can be associated with Overall satisfaction, as well as Taste of the Food and Quality of Food for the Money.

Correspondence analysis is a valuable tool that can be applied to many situations.