Do your customers know what they want or not?

As a business, there are many questions you ask yourself and your employees. Some include: What are customers looking for? What do we have to offer them? Where is the best place to target potential customers, etc…however, one question that you probably haven’t thought much about is “Do your customers know what they want or not?” Continue reading

Customer data: the way you get it is everything

As a business, knowing your customer and what theyAnalyzing Survey Data want is crucial to the continued success of your company. It gives you an insight into what to add, change, or take away from your product or service to ensure your customers are getting exactly what they expect and want from your company. Collecting customer data is the best way to do it, but it has to be done right. Here’s how to get customer data the right way. Continue reading

More Accessible Data in 2013

In the last decade, technology has exploded. Every aspect of our life has changed, evolved, and is continuing to expand and expound on quality and convenience. From bigger flat-panel televisions to smaller computers and tablets, the trend seems to continue with big marketing data trends for 2013; the motto being more accessible, more mobile, and more powerful.

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Intuition, a good idea & the right data: the secret sauce of marketing success

Acquiring good customer data is onlyAnalyzing Survey Data one ingredient in “the sauce” for successful marketing, but you need to realize that it is just the base ingredient. Through surveys and demographic data, you can gain the foundation that will allow you to make informed decisions on which direction to go with your marketing strategies. However, savvy entrepreneurs and other business professionals understand that they need to trust their instincts and creativity to develop a successful and effective marketing strategy. Continue reading

Signs that you don’t really know who your customers are

You have the perfect product Under pressureor service that you know everyone needs, but sales just aren’t were they should be. Chances are you don’t really know who your customers are. When bringing a new product or service to the market, or attempting to expand your market share, you need to carefully consider a deluge of demographic data to ensure profitability. Continue reading

Lead with Data Driven Decisions

Lead DataInformation is the prime asset of enterprise, the departure point for all decisions leading to product innovation, marketing strategy, and ongoing competitive advantage.  IT generates an abundance of business information; the problem is selecting data most useful to your enterprise purposes and using it to devise suitable long-term objectives.  Data driven decision-making is based on big data analytics, which particularizes and coordinates enterprise-derived information towards specific organizational objectives. Analyzing large data-sets for assessment of strategic performance is the current best practice technique of enterprise decision-making. Continue reading

Making Use of Qualitative Research

When it comes to business, qualitative research revolves around exploring trends and understanding various issues relating to marketability of a firm’s products or services.

Any sound business strategy should be backed by thorough market research. Otherwise, just guessing may not help market research managers identify the demand for a firm’s products or services. While guesswork can sometimes help you to establish the market demand for your products, you might not be so lucky at all times. Continue reading

Why Mobile Qualitative Research is Growing Strong

Mobile Qualitative Research or MQR, as it is known, is growing in popularity as a go-to market research tool because it’s finally realizing the promise of providing feedback while the customer is using the product.  MQR creates feedback in the real world and with the introduction of mobile software analysis tools, researchers can receive and analyze results in real time.

MQR Process

The process is simple and straight forward.  After recruiting participants, questions are sent to their mobile device over a period of days or weeks, allowing the participants to react and respond via that same device.

A researcher can assemble and view the results with an online dashboard.  They can input new questions to reengage any of the participants in real time, delving more deeply into a particular subject.  Imagine doing that with traditional or online surveys.

MQR Benefits:

  • Immediacy
  • Familiarity
  • Engagement

The primary advantage of Mobile Qualitative Research is the immediacy of feedback.  Since participants (and most of us) carry our phones during the day, when at work, out shopping or at a concert or sporting event, participants can text or send photos of their experience at the point of sale, or during the process of entering a crowded stadium.

This immediacy means that the information provided is more accurate because the respondents do not have to recall what they experienced at a later time when they are finally sitting down to go online.

There is no learning curve for MQR participants since they are simply texting or sending photos on mobile devices that they all ready own and are familiar with.  This ease of use helps increase the participation rate because of its simplicity and familiarity.

Higher engagement or non-static engagement with the participants is a big plus.  Since the information is submitted in real time, a live researcher will know that the data is input at the time the researched event is occurring.

Mobile Qualitative Research = Access

According to the Nielsen group over 265 million Americans have mobile phones, 98% of which are text capable.  By the end of 2010, according to the International Telecommunication Union, approximately 5 billion people worldwide had cell phones.

Mobile Qualitative Research allows researchers access to those people that either don’t have time to sit down at a computer or don’t have access to one.  Also, using MQR participants don’t have to be herded into a study room or assembled into panels, saving significant costs and time.

Qualitative Research – Tips on how to use Unstructured Research Data

In my previous blog article ‘mTAB Understands Qualitative Research Needs!’  I discussed the need, or advantages, to utilizing qualitative, or unstructured, data when reviewing research results; now I want to help you learn how to implement this suggestion.

I have always visualized structured response data in my imagination as a city with sky-rise buildings and lots of traffic; there are mathematical equations creating a functioning city and it works as well as we need it to. In my imaginary city I visualize unstructured data as a reservoir of gold in the underground tunnels that the city is built on. Burgeoning cities have a tendency to expand, and expansion will generally follow the basis of the original model, but improvements are made by utilizing the underground reservoir.

Yes, fanciful, I know. Keep following me, structured response data gives you an answer to exactly what you’ve asked, but how did you decide what to ask? Have you based your survey response questions on your desires, or your consumer’s desires? Unstructured data allows for responses that are unsuspected and can lead to great improvements.

Let’s use an example: Car manufacturing. The survey asks for demographics, model preferences, etc. Generally an unstructured data response will be positioned as a means of gathering more information about a previous structured data question; e.g. ‘why did you answer yes to this question?’ This is brilliant because the survey is looking to dig deeper into reasoning, however, the full potential of unstructured data is not being utilized.

To harness the power of unstructured data one must think outside of their normal understandings. If you work in the automobile industry you immediately take for granted the need for automobiles. This assumption is the basis for the rest of your survey. Now, if you realize your previously held assumptions about your field and bring them into question, you may find that reservoir! Survey respondents don’t want to write a book while filling out your survey, so you’ve got to choose your open-ended non structured questions wisely. Some examples may be: why do you drive a car instead of taking mass transit? If money was not an issue what car would you buy, and why? What is the most important aspect of a car in your opinion? If you’ve ever considered fuel-alternative transportation, what factors concerned you? Are there any questions that you would want to ask us?

Asking your customers questions that you haven’t assumed answers to will result in responses that you couldn’t have anticipated, and insight you may have overlooked. By incorporating unstructured data responses in your survey you may just find the seedling of an idea that will greatly improve your product, and also give your customers the satisfaction of knowing that you’re interested in their thoughts.

Please be sure to follow my blog posts regarding qualitative survey analysis.  Keep an eye out as well for text analytic posts that we’re planning to run in the near future.

mTAB Understands Qualitative Research Needs!

I am an administrative analyst at Productive Access, the creators of mTAB™. I hold a masters degree in Sociology, and used qualitative analysis to write my thesis.  With this background I understand the statistics behind survey data analysis and the use of cross-tabulation to analyze survey data, but neither technique is really my forte.

I had always considered mTAB™ a program that gives priority to quantitative data.  Generally marketing departments, and analysts, are looking to crunch the numbers of their demographics in order to fully understand the popularity of their products, so qualitative analysis isn’t in high demand. Some executives, however, recognize the importance and need for qualitative research.  Data tabulations can only go so far; as a means of truly understanding what data is saying, innovative analysts utilize qualitative analysis techniques using survey verbatim questions, otherwise known as unstructured data.

 

 

 

 

 

 

Ultimately quantitative and qualitative data work best as a team. For example a particular brand of car might not be selling well in certain demographics. Some may look at this quantitative finding and decide to scrap their model. This is where qualitative research really shines; looking into what is said by those unsatisfied consumers may highlight a product need that would have been otherwise overlooked. In sociology we were always made very aware of this phenomenon.

My professors always said, “Correlation does not equal causation”. This sentiment is true in all research. Correlations can be spurious and based on another factor. For example: rainbows seem to be positively correlated with a healthy garden. Seeing this data I may ask my employees to create a rainbow machine so my tomatoes will grow. You may think this sounds silly, but many marketing departments and analysts look at the correlations of data and don’t look into the other factors. Rainbows happen after it rains. Rain helps gardens grow. Rainbows have a spurious relationship to healthy gardens.

Luckily Productive Access understands the need for both quantitative and qualitative research, and has worked hard to incorporate a system that will help the mTAB™ user better understand the implications of their survey’s unstructured data: tag clouds.  I was extremely pleased when I saw this function added to our latest release of mTAB™. Now when an analyst wants to further understand their data they can look to their verbatim responses.

As a researcher I understand that qualitative data is really rather daunting; the researcher may have no idea where to start, and the paragraphs of responses are not easily sorted into crosstabs that show percentages. With the addition of the Tag Clouds in mTAB™ you now have an immediate indication of the most prevalent words used within the verbatim cells. When using tag clouds in mTAB™ a dialogue box appears showing the most commonly occurring words from the Verbatim Report and indicates the frequency of occurrence by size and boldness.  The largest and boldest being the most frequent. Now with one look you can tell what issue is most prevalent within the group you are analyzing.

There is a great webinar on the PAI web site that illustrates how to incorporate a qualitative analysis of unstructured survey data within the framework of qualitative cross-tabulation survey analysis.   This short video clearly illustrates how mTAB’s tag cloud feature benefits the analysis of surveys containing both structured and unstructured data.

I love the new Tag Clouds in mTAB™, and am happy to be with a company that persistently works to improve their program for all users.

If you would like to find out how the PAI team could help you combine the analysis of your survey structured and unstructured data, please register here for a no-obligation consultation.