Collecting Survey Data vs. Analyzing Survey Data

To grow in today’s tough economic market, every hour and dollar spent needs to yield positive results that can be used to facilitate growth. Collecting survey data and analyzing the data are two different, but very important steps you must take to help identify crucial areas of growth.

Collecting Data

Data collection comes from a variety of sources and interactions, such as face to face interviews, mail surveys, web survey, and offers, and the amount of information that can be collected and compiled can be staggering. However, simply collecting this data is not enough.  While it is a crucial step, it is what you do with the data that is key to growing your business.

Analyzing your Collected Data

In order to utilize this information to target areas of growth; you will need to have a strategic survey analysis plan in place.  Having a wealth of information on hand is one thing, but being able to effectively use the information is another.

Compile & Validate your Information

Before you do anything, compile the data, including any questions, answers, and profiles of participants.  Make sure it is fairly easy to move and group your results. Check to see that all of the questions were understood.  See if participants made comments about particular questions or left specific questions unanswered, and then consider what you will do with this data.

For example, if your survey question asked the participant to provide a rating from 1-5 and you find the question was answered with something other than <blank>, 1, 2, 3, 4, or 5, how you will interpret the findings?

Identify Patterns

Once you have compiled and validated your results, break the data down by grouping it into classes and recording how many data points fall into each class.  This is where you check for patterns based on gender, race, age, religion or any other information you collected from participants, and where your raw data is transformed into valuable information.

Establish Simple Frequency Distributions for Each Survey Question – Frequency distributions demonstrate how many observations on a given variable have a particular attribute.  You may choose to look at gender, income levels, age ranges, and etc. For example, a survey is taken of 50 people.  The frequency distribution might indicate 25 people selected (a); 5 were female and 20 were male. The other 25 people selected (b); 12 were female and 13 were male.  Distributions may be displayed using percentages, or in terms of a bar chart.

Ultimately, the more detailed your analysis, the more valuable the data becomes.

PAI Co-Sponsors Clemson University’s CU-ICAR “Deep Orange” program

PAI and AutoPacific

have joined forces to co-sponsor the Clemson University “Deep Orange” future vehicle development program.

Through the use of PAI’s mTAB™ survey analysis software, Clemson CU-ICAR center students are offered convenient access to the AutoPacific’s syndicated New Vehicle Satisfaction Study.

The partnership of PAI and AutoPacific allows Clemson students to use mTAB™ to “deep dive” and data mine the AutoPacific syndicated survey results, gathering consumer insights and uncovering new product opportunities. This data will be incorporated within the development process of the CU-ICAR department’s 2011 “Deep Orange” prototype vehicle

Clemson University’s International Center for Automotive Research (CU-ICAR) is a 250-acre advanced-technology research campus where university, industry and government organizations collaborate. CU-ICAR offers Masters and PhD programs in Automotive Engineering and is conducting leading-edge applied research in critical areas such as advanced product development strategies, sustainable mobility, intelligent manufacturing systems and advanced materials.

CU-ICAR has industrial-scale laboratories and testing equipment in world-class facilities available for commercial use as well as a comprehensive computational center, dedicated to solving clients industrial problems, and backed by a massive high-performance computing infrastructure.

Turning Survey Data into Actionable Insights

What is most important to your customer base, and what drives their overall satisfaction? Undoubtedly, it can be difficult to know what customers are looking for from your business. Many factors go into whether or not a prospect will browse your products, make a purchase and, most importantly, return for future business. With proper survey development and survey data analysis, you can accurately uncover these key drivers.

To identify what drives customer satisfaction, the first step is to ask your customers. The information gathered from surveys can pinpoint where to focus and will help with the strategic planning process.

Keep the following objectives in mind as you create the questions for your surveys:

  • Find the Top Attributes. A variety of elements could be important to any given customer, but with a wide-spread survey you can discover which are important to the largest number of people. These top attributes will be the key drivers that you’ll want to focus on improving and maintaining. If you run a shop, you may find the most people desiring “Pleasant atmosphere,” “Good return policy,” or “Large selection.” If you’re an online company it may instead be “Easy to navigate” or “Availability and helpfulness of customer service.”
  • Get a Rating. Once you know what it is that your customers deem important, you’ll also need to know how well you’re doing in each aspect. Have your customers rate how well they find your service in each attribute. The most important ones to focus on are the top-rated elements that your customers have found lacking from your business. That way you don’t have to pour money and resources into every aspect of your company. Instead, you can target specific points that your customers actively desire, and potentially even move money away from the unwanted attributes.
  • Calculate Importance. While everyone in your survey may say that they want “Good customer service,” that may not be of greatest importance to them. It may not, for instance, affect whether or not they return to your business. Make sure that your survey asks customers to list desired attributes by their importance, so that you have the most specific information available.

Driver analysis surveys can be very simple or complex; however, analyzing the results is an intricate process.  As a result, many organizations struggle to understand how to turn survey data into actionable insights. You don’t need to spend a fortune trying to identify what makes your business lucrative and desirable to customers. The availability of survey data analysis software has made it possible to quickly visualize data in a useful, easy to understand way. These tools offer an affordable solution to glean the most important data from the people who use your services and make well informed business decisions.

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.

PAI co-sponsoring Atlanta Research Club event – July 18, 2011

The Research Club was started 6 years ago in London for the purposes of networking like-minded research professionals in the field of marketing research and strategic planning.

PAI is proudly co-sponsoring the Atlanta Research Club event to be held on the evening of July 18th. Timing of the event corresponds with the 2nd International Market Research in the Mobile World conference which PAI will be attending.

WHY ATTEND?: Relax and have some fun! Anyone associated with the Market Research industry is invited. No presentations, no hard sell, just have a good time and meet as many of your peers as possible. Beverages and canapés are provided by the event sponsors.

WHEN IS IT? July 18, 2011 6:30pm – 9:30pm

WHERE IS IT? Los Reyes Restaurant, 777 Town Park Lane, Kennesaw, GA 30144

HOW DO I RSVP? Space is limited! Please take a moment to register here as soon as conveniently possible.

Hope you will attend as our guest! We look forward to meeting you at the event.

Enhancing Your Net Promoter Score Survey Analysis

Imagine that you are a retailer tracking your net promoter score (NPS).  Your NPS survey is administered by way of an invitation printed on your in-store POS receipt.

The point of tracking NPS is to identify how to make improvements in your service and products.  Changes in your process can then be validated by continual tracking of the NPS metric.

So how can we best identify opportunities for improvement from the NPS tracking graphic below?

We can simply observe that the NPS score has gone up or down relative to our last observation.  Assuming at least one open-ended question in addition to our net promoter survey question, we can review the open-ended or “unstructured” data from customers providing low (or high) NPS scores.

Text analytics tools, for example tools providing classification and sentiment analysis of unstructured survey responses, along with search term and tag cloud drill down tools, can significantly enhance the understanding of the unstructured survey responses.

Additional structured data, coupled with the appropriate tools for the analysis of structured and unstructured survey questions, will significantly enhance our analysis and thereby greatly increase the value of our net promoter program.

POS receipts typically include a transaction identifier that can be used to link a wealth of structured data to the survey results.  Listed below is a partial listing of possibilities:

  • Store Location
  • Transaction date/time
  • POS terminal and sales associate
  • Inventory of items purchased
  • Items purchased on sale or promotional items
  • Payment type
  • Total amount of purchase (total check)
  • (assuming you have a frequent buyer program) buyer profile

Without adding questions to our NPS survey, we can now break down scores by region, district, zone and store, by day-part, by payment type, by check amount, by store department and potentially by frequent vs. infrequent shoppers.

If you would like to review your NPS program with industry experts  please sign up for a web meeting or simply visit our web-site to learn more about our survey data basing and analysis services.