Auto manufactures gain deeper insight from survey data

Recently Maritz Research conducted a cross industry survey of customer satisfaction. Industries surveyed ranged from banks and credit cards to insurance claims; restaurants, wireless and Internet to Television services. In the auto industry, contrary to popular belief, the report showed that customer service is above average as related to their experience when purchasing a vehicle and/or having their vehicle serviced by the dealer. According to the report, 3 out of 4 customers reported their experience as satisfied to extremely satisfied.

Why does this matter?

The automotive industry has used mTab for the past fifteen years to improve their customer service experience by receiving feedback and data related to customer transactions.  Over the years the data analyzed through mTab was used to identify problems and provide a business justification for change.

The Automotive Sales Experience

When purchasing an automobile, the report showed, 75.6% of respondents were satisfied to extremely satisfied with the vehicle delivery process and with the experience that the auto dealer kept its promises.  The lowest rating, 65.4%, came from customer experiences with the financing and paperwork.  Because automotive dealers, continued the report, are not satisfied with simply being above average, the information gathered from the report can be used to improve the weakest areas of the customer purchase experience.

The Automotive Service Experience

Shattering preconceived notions about auto dealer repair services this area accomplished an even higher rating than the sales experience.  With an average rating of 75.48%, with an impressive 77.5% indicating they were satisfied with the quality of the repair and that the vehicle was fixed the first time.  Basically, 3 out 4 customers were satisfied that the dealer was honest in its dealings with the customer.  The lowest rating, 70.3% had to do with the time to complete the repair or service.

Why is the automotive dealership industry above average?

In part because they decided 15 years ago to pay attention.  The tool they chose to do that with is mTab.  Because of its sophisticated engine and ability to analyze and present viable research data in easy to read and fully customizable formats, mTab was an essential element to their success.

What to take away from the survey?  Dealerships appear to keep their word and remain honest.  For the past fifteen years these qualities were tracked and reinforced throughout the industry in large part by the data analyzed and presented by mTab suite of software analysis and presentation products.

The Business Impact of Data Visualization

What is Data Visualization?

Data visualization is the study of data being communicated visually through graphical means. Bar charts, graphs and maps are illustrations of basic data visualizations that have been utilized for quite a few years. Over time, visualizations have become more intricate to the extent of having animations that illustrate changing data over a period of time. There is virtually no limit to the translation of information into an image.

As the visualization designer, you can ascertain which visual components, such as color and shape, symbolize and communicate particular data points.  For example, images could be 2D or 3D, dynamic or fixed, or more importantly they could allow user interaction.

Visualizations provide a unique way to communicate trends and correlations that can ultimately lead to important marketing breakthroughs. Visualizations that represent a large amount of information allow you to see patterns that may normally be hidden in unconnected data sets.

Using Data Visualization to Immediately Observe Key Points

Data visualization does not occur in isolation. Therefore it is important that you understand the context that you are communicating. When planning your communication, make sure it meets these key points:

  1. Precision. Could it be accurate with regard to proper quantitative assessment?
  2. Versatility. Can the creation conform to assist numerous wants?
  3. Aesthetically Pleasing. The data visualization must not insult the reader’s senses; a great example of this is moire patterns.
  4. Efficient.  Is your visualization simple to understand?  Will the viewer “get it” with ease?
  5. Effective. You need to be sure you display the information; evaluate regardless of whether you need to increase or even reduce data-ink percentage; additionally think about brase non-data-ink as well as brase repetitive data-ink.

The Importance of Making Data More Usable

In recent years, a variety of methods have been proposed for analyzing the consequences of data effectiveness.  During a study conducted by the University of Texas, researchers found that if a company increased the accessibility of data for the user, by only 10%, the result could increase revenue drastically and create new jobs. The researchers analyzed accessibility, mobility, quality, and usability to come up with these findings. Overall, improving your company’s data usability will likely benefit business and should not be overlooked.

What is Data Analysis?

What is data analysis exactly? Try to define the phrase, data analysis and you’ll get different answers depending on the discipline or industry of the person you are asking.

A scientist or sociologist may define it as a body of methods used to help describe facts (based on observation), detect patterns, develop explanations or test a hypothesis.

A marketing manager may define it more closely to the Wikipedia article, “Analysis of data is a process of inspecting, cleaning, transforming, and modeling data with the goal of highlighting useful information, suggesting conclusions, and supporting decision making.”

Ask a mathematician or a statistician and they may well tell you that to define data analysis one must first define statistics, which, may be defined as a set of methods used to collect, analyze present and interpret data.

So again we ask the question, “What is Data Analysis?”  We define it as the process of taking raw information (data) and creating something understandable and useful, that either supports or rejects a specified goal.  In other words, when the CEO asks, “Should I or can I do what I want to do?”; analysis of the available data will support either a yes or no answer.

Analyzing Data For The Real World

Although raw data, expressed as a numerical result, describes values that relate to questions, in the real world the analysis of this data is not about the numbers.  Rather it is about translating those numbers into something meaningful that relates to a real world question.

Your analysis may indicate trends or show an average, median or mean. It can identify groups or sub groups with common characteristics or differences among those groups.  Data analysis can show the rate of growth or the rate of decline.  When translating these numbers into an answer for a real world question, a skilled analyst can present information in such a way as to significantly influence a decision maker to move in a certain direction.  However, using the same data, it is possible to make a strong case for going in the opposite direction. This apparent contradiction is not found within the data; rather it is found within the motivation, background and bias of the analyst and/or within the political and economic constraints of the analyst’s organization or client.

Analysis Presents a Position

Ultimately, data analysis is used to present an argument.  Should the company promote product A?  Should it raise prices?  Can we sell more of product A if we introduce change B? These types of ‘Should we’, ‘Can we’ questions require the support of data that has been collected, analyzed and presented in as unbiased a manner as possible.  The data that is collected is rarely, if ever, a Should we, Can we question.  Most often the Should we, Can we questions are answered in response to data collected relating to Who, What, When, Where or How questions. (Who buys hair spray?  What is the median income of our clients? Where do they shop? When do they shop? How do they shop? etc.)

Bias and Mistake

It’s up to the analyst to interpret and present the data from the Who, What, When, Where and How questions to the decision maker in such a way as for the decision maker to answer the Should we, Can we questions.  For the reasons stated above, presenting a wholly unbiased interpretation is nearly impossible. Often it is not the process of analysis that is at fault but rather the conclusions drawn from the information (or how they are presented) that lead to the failure of a product launch or ad campaign.  A primary culprit leading to erroneous conclusions is mistaking correlation for causation.  The classic example: roosters do not cause the sun to rise!    There are, however, numerous techniques and tools analysts use to minimize errors but those require additional explanation not suited for this post.

What is Data Analysis Then?

So we ask one more time, “What is Data Analysis?”  I would argue that data analysis is simply the interpretation and presentation of the validity or invalidity of data as it relates to a question posited by a decision maker who needs to understand and rely on the information presented by the analyst in order to take action on that question.

In other words, data analysis is the telling of the data’s story. It is a story that began with the questions, “Should we or Can we do what we want to do” and ends with the answer, “That depends on your interpretation”.

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.

Survey data analysis – using graphic visualization as a tool

We’d previously reflected on the opposing roles of visualization and presentation graphics; let’s now examine how visualization graphics can help us analyze survey data.

Visualization graphics can be used to quickly identify outliers within large quantities of data. Our brains are wired to recognize graphic differences in shape, magnitude, and direction more readily than we can recognize the equivalent differences within a table of numbers.

“Outliers” occur when the data visually rises above or below the average or the “noise” within the results. Outliers can serve as the source of the stories that an analyst constructs to offer understanding and explanation of the survey results. As an analyst you should be asking yourself “why?” when you observe an outlier.

The more data you include within your visualization, the greater your odds of observing outliers. There is nothing wrong with creating a visual “rats nest” of lines or bars as part of the visual analysis. Your objective is to skim the edges of the data, ignoring the bulk of data that represents the average; you are visually filtering out the noise to identify the data observations that stand out from the fray.

The radar chart below is a good example of a visualization of a large number of data points (156) summarizing hundreds of thousands of survey responses. Using this radar chart format, we can identify interesting outliers at a glance, much more conveniently than we could by studying a table or even a bar chart representation of this same data.


Here we are observing purchase decision importance survey questions results between six different brands rated by the survey respondents.

At a glance we note the red line, represented of Brand E, displays considerable lower ratings than all other brands (i.e. “the noise”) for the respondent’s consideration of manufacturer’s reputation, prestige of the product, prior experience with the manufacturer and technical innovations. Brand E may be a relatively new brand in the marketplace as purchasers did not consider reputation, prior experience or prestige as important criteria within their decision process.

Alternatively, the blue line represented by Brand A sits above the noise for attribute fun to drive, but below the noise for attributes seating capacity and cargo space. Brand A may represent a manufacturer of sporty products that emphasize fun over practicality.

Using this visualization, we have quickly identified outliers and have constructed hypothesis that we can then test and explore with our survey analysis drill down tools.

In future posts, we will illustrate how to summarize the information we’ve gleaned from our visualization into a presentation graphic display, allowing us to communicate the story of our data to our customers.

Visualization techniques – graphic visualization vs. presentation

You’ve just completed fielding your latest survey research project, now you need to analyze the data and communicate the results.  As a complement to cross tabulation and statistical analysis of the survey data, you’ll likely incorporate graphics within your analysis and reporting process.

In fact, you should consider incorporating graphics in two different ways; using graphics as a visualization tool to assist with your analysis, and graphics as a presentation tool to clearly communicate the results of your analysis.

These two separate functions or “graphical roles” require different types of graphics as well as different methods of viewing or analyzing the graphics.

Graphic of my LinkedIn network – useful as a visualization tool, but not as a presentation tool.

Visualization graphics typically incorporate a relatively large number of data points, enabling the analyst to view respondent segments, trends or outliers in a manner that may not be obvious from the viewpoint of tabular reports.

Presentation on the other hand, embodies the the art of communication, and presentation graphics are therefore designed to quickly and persuasively depict a key point or conclusion from the analysis of the survey results

Visualization graphics are typically “noisy”, containing lots of information and capturing several dimensions including multiple axes, quadrants, or variations of text or data point sizes.  Visualization graphics typically require careful study and consideration to identify their underlying meaning.

On the other hand, presentation graphics need to immediately convey the point that the graphic is making.  They are purposefully succinct and focused, and as such presentation graphics typically avoid multiple dimensions.  Good presentation graphics preclude the need for supporting explanatory text to convey their message.

The experience analyst will be thinking of presentation graphics at every step of the data analysis process, including while utilizing visualization graphics as an analysis tool.  The point of the analyst’s effort is to ultimately communicate the results of the analysis to busy decision makers in a manner they can readily comprehend.

Stay tuned to this blog as we explore new and interesting ways to use graphics to both analyze and present the results of survey data.

If you’d like to learn more about how to prepare survey data for analysis, please download our whitepaper  “10 essential prerequisites for survey data analysis”.