Survey data analysis: Drill beneath the Dashboard

Corporations rely on dashboards, which have become the defacto tool for monitoring all aspects of the business enterprise. This includes critical consumer insights metrics such as net promoter score and customer satisfaction, which are normally derived from survey programs. While dashboards are convenient and easy to read, they are not a replacement for a market research analyst’s “deep dive” understanding of the survey results.

Dashboards offer limited drill down functionality by presenting a closed end list of pre-wired data classifications, such as the region / district / zone roll up or heavy / medium / light users, of a product or service. Ultimately the dashboard service allows you to view the data in any way that the closed end “box” permits.

As a consequence, dashboards offer a very convenient, easy to use and graphically pleasing “30,000 foot” view of survey program results.

While dashboards allow senior management to observe at a glance that the train hasn’t derailed, they’re not capable of revealing new insights and greater understanding of the data.

As research professionals, we’re tasked with taking a harder look at the results of our survey programs. We’re responsible for providing the insight and discovery that cannot be obtained through dashboard tools. We need to know how to ask meaningful questions of our survey results, and to have the tools on hand to easily and conveniently obtain the answers to our questions.

Who is going to tell management “why” the train derailed after it’s reported by the dashboard?

The process of drilling into the data by asking meaningful questions, which leads to new and more refined questions, ultimately results in new insight and discovery.

It is our job, as research professionals, to continually remind senior management of our value proposition by providing the insight and understanding that is buried within our survey program results, which can only be obtained by “swimming with the data” via the drill down process.

If you would like to learn more about the process of analyzing your survey results, please call on us to help you get started.

Avoid these 4 Common Errors when Analyzing Survey Data

Analyzing survey data is an important function in advising business decision makers. Logically, errors made in the analysis can lead to incorrect conclusions which in turn may lead to unanticipated and often undesirable outcomes.

Consider these 4 common errors when analyzing survey data.


1. The word “Most”
It may seem so basic but misuse of the word most is a common mistake when analyzing survey data. Take this example: 45% of people thought that drinking soft drinks every day is bad for your health. 30% thought that drinking more than one soft drink a day is bad for your health and the rest had not thought about it at all.

In this instance it is safe to say that the most frequent response was that drinking soft drinks every day is bad for your health. However, it would be incorrect to say that most people thought that drinking soft drinks is bad for your health because statistically that amount, in this context, is not greater than 50%.

2. Causation vs correlation
Most survey data is the result of correlational research as opposed to experimental research. Unlike, experimental research, which manipulates a variable to measure its effect on other variables, (hence determining causation), correlational research measures the relationship between different variable to measure its correlation (hence the name).

Understanding the difference between causation and correlation determines the conclusions one can draw from a given data set. For example, if we take some sample data and estimate that 30% of males and 60% of females were using the internet last week we can’t say the difference is because of their sex. We can only say that internet use is associated with their gender. The reason for the difference may be caused by something else entirely.

3. Avoid quoting percentages only
Percentages are a familiar and practical way to look at data results but be careful of not including the underlying base on which the percentages are determined. This is especially important when reporting changes in percentages, particularly if the statistical sample is too small.

4. Focusing on the average
Relying solely on averages can be misleading. Regression toward the mean may indicate the sample period was too short to be meaningful. Or, the reason behind an average is often more important than the number itself. An average result, without context, is often meaningless in supporting an evaluation of the underlying process you are analyzing.

Don’t be a cliché. Increase Sales with Market Research and Analysis

When seeking to increase sales, you can avoid the unpleasant consequences stemming from misguided or uninformed sales efforts that result in the oft-quoted clichés, “spinning your wheels” or “acting in haste only to repent at leisure,” by investing a little time and money into market research and analysis.

Knowledge is Power – Forewarned is Forearmed

When it comes to sales and marketing, these idioms make as much sense today as ever. There’s no substitute for current information when it comes to increasing sales.  Good market research and analysis focuses your efforts thereby avoiding costly mistakes.

Here are a few ways market research and analysis can help increase sales:

Define Your Market

Knowing who to sell to and why you should sell to them is powerful information that eliminates lost time and money from “throwing enough mud at the wall to see what sticks” or following “harebrained ideas” that are not backed up by real data obtained from research.If you know in advance why your best customers are your best customers, you would then know to market to prospects with similar demographics.

Clarify Your Message

You may love your company tag line or advertising slogan and it may even be popular and memorable.  While the former will get you nothing (the market cares not for what the CEO loves), and the latter may increase good will or top of mind awareness, but does your message actually do anything to increase sales?  With good research, you can define your message to capture attention and keep it through the close of the sale.

If you know in advance what aspects of your product are most appealing to your customers, you could target your message by highlighting those features and benefits.   Similarly, if you know in advance what aspects of your product or service are least appealing, you could devise solutions to overcome objections to those features or (assuming available time and resources), modify those features to be more appealing.

Define Your Market Offer

The classic 4P model; product, price, place and promotion, make up your market offer.  Does it not make better sense to base your decisions on current market research and analysis rather than “tried and true” methods? How you’ve done it before may have been tried, but possibly is no longer true.
So, by utilizing current market research and analysis, you can avoid “barking up the wrong tree” and avoid “eating humble pie” to become the “bee’s knees” of your company’s sales efforts.

Leveraging Data Analytics for Competitive Advantage

When seeking to gain a competitive advantage, data analytics can really fall in to only one of three areas: accurate, misleading or irrelevant. What about inaccurate, you ask? Inaccurate data is misleading and therefore inaccuracy is also not relevant.

Customer data is captured by either passive (click rates, POS data, etc.) or active (customer feedback, surveys responses, etc.) means and is used extensively in business to gain a competitive advantage. Keep in mind that the larger the sample pool and the longer period of time the data is gathered, the more accurate an analysis will be. This article focuses on the benefits of leveraging data analytics for competitive advantage rather than on the types of data gathering techniques.

Types of analytic solutions and models:

Segmentation: divide customers into homogenous groups.
Classification: including decision trees, logistic regression, etc for predicting customer behavior
Forecasting: used to predict future metrics based on historical data, e.g. sales forecasting.
Basket Market Analysis: Customer purchase data used as a basis for segmentation.
Explanatory Modeling: Used to determine why something happens.
Predictive Modeling: Used to classify customers, or prospects
Decision Modeling: Used to guide decision making for a particular situation.
Customer Value Analytics: in conjunction with Segmentation and classification, it is used to identify customer type and priority.

Benefits of Data Analytics

In terms of survey analysis, the following is a list of benefits to using data analytics for competitive advantage along with the analytical solutions and models used:

  • Tailored offerings to fulfill specific customer needs.
    • Segmentation, classification, forecasting, predictive modeling, decision modeling 
  • Develop new products or services or determine product and service features that are important to customers
    • Segmentation, classification, forecasting, predictive modeling, decision modeling, market basket analysis 
  • Better customer relationship management.
    • Customer value analytics, classification, explanatory modeling, customer experience analytics, Exception analysis
  • Find, attract and retain the best customers
    • Customer value analytics, classification, explanatory modeling
  • Distinguish preferred from marginal customers.
    • Customer value analytics, classification, explanatory modeling 
  • Customize marketing plans to specific markets.
    • Segmentation, classification, forecasting, predictive modeling, decision modeling, explanatory modeling
  • Identify new market opportunities.
    • Segmentation, classification, forecasting, predictive modeling, decision modeling, explanatory modeling
  • Develop insight into changing customer requirements,
    • Customer value analytics, classification, explanatory modeling
  • Target pricing
    • Customer value analytics, classification, explanatory modeling, predictive modeling, market basket analysis 
  • Determine which customers are open to cross-selling.
    • Segmentation, classification, forecasting, predictive modeling, decision modeling, market basket analysis

Steve Jobs’ effect on the market research community

Can Mr. Jobs be credited with inventing the idea of market research?

Well, no!
But he can surely gain credit for shaping the future of how it is conducted! For so long, if you wanted to find out what people thought of your product, the only way was to get out on the street and ask them. Of course, we then moved on to mailing out questionnaires and interviewing by phone, but each of these requires a significant time investment on the part of the interviewee.

With the digital age upon us, things have moved on rapidly and peoples thirst for information seems never ending. High speed connections are everywhere and you are never far from a screen of some kind.

With this technology explosion, researchers have worked hard to keep up, fielding surveys by cell phone based text messages, emails, web pages and any other means they can conjure up. Consumers are encouraged to participate in research efforts through links on retail receipts, direct TV/radio advertising and most recently through the use of ‘QR Codes’ that are placed in magazines, newspapers, on product packaging, stickers and even clothing, encouraging the inquisitive among us to pick up their smart phone, scan the code and follow the link to find out more!

So what, you say, does all this have to do with Steve Jobs? Well, it was only last week that it was noted that “around 92% of Fortune 500 companies are currently testing or using iPads as a corporate solution”. And to make that statistic even more impressive, the iPad has only been on the market for just over 18 months!

Whether it be in the local mall, small town tourist office or at a specialist research event, tablets are the go-to device of the moment for data collection.  Apps are springing up to make this easier than ever and with their portability and ease of use, this trend will only increase.

The unquenchable thirst for knowledge that I mentioned earlier, now seems to be driving more and more people to not only want to collect data via tablets, but also to want the ability to delve in to the data further, to find the nuggets of information hidden within it.  Developers are reacting quickly to this need and in the very near future, it is safe to assume that the number of tablet based survey analysis tools will grow drastically.

And all any of us can really say is: Thanks Steve!

To learn more about how to prepare your survey data for analysis, feel free to download our whitepaper Ten Essential Prerequisites for Survey Data Analysis.