Improve Your Customer Surveys

It is vitally important for you to take every aspect of your business seriously. If you don’t, the product you provide will decline, your customers will look for other companies and you’ll eventually fall out of business.

Because of this, it is important to know every inch of the company. Every aspect that is both visible to the customers and visible to only those working for the business. Although there are internal checks you can perform, the best way to understand how outsiders perceive the business is through customer surveys. Continue reading

Consumer Insight from Panel Research

If knowledge is power, than survey data analysis may be one of the most powerful weapons for marketers and market research groups. Whether we’re talking about social or consumer market research, doing this work through the use of panels really helps cull the field and find out what people are thinking, feeling, and how they would react to decisions and product launches from different groups. Continue reading

Making Informed New Product Decisions with Survey Analysis

Market research and survey analysis provide businesses with the information they need to ensure new products are developed and positioned to satisfy their target audience. The results of researching, surveying and analyzing the market are extremely important in developing products that fill a need and capture the attention of your consumers. Continue reading

Market Research Data Barriers worth Combating

As discussed in previous posts on big data and data overload, an effective data management and analysis plan will make all the difference between actionable insights and information stockpiling.  Data is vital and all businesses depend on it to make the right decisions going forward. However, there comes a time when the data becomes too much and you are faced with data overload.

If you are in market research, you are constantly faced with the challenge of focusing in on the right segments of data. It can be hard to sort through the 2.5 quintillion bytes of data that is created each day by Internet users alone. Therefore it is important that you understand big data barriers.

First, there are three main attributes to describe big data:

  • Volume: The amount of information on the Internet is immense; from people posting links to their favorite blogs to customers writing reviews. This data is freely available for you to turn into valuable information.
  • Velocity: The rate at which data enters the information highway is phenomenal. You have to be constantly updating trends and reevaluating assumptions.
  • Variety: Data comes from many sources and consists of both structured and unstructured data.

If you do not have an effective plan in place to manage and analyze data, it can quickly escalate into data overload. Some common barriers experienced in market research include:

  • Data paralysis: It is easy for a business to be overwhelmed by all the data that they have at their disposal. Without an analytics program, data becomes impossible to act on. The data is left unchecked and being a burden more than a gift.
  • Expense: Efficient software, hardware and human resources are needed to make the best use of data. For some businesses, there might not be enough resources to allow for the proper use of data.
  • Data privacy: You might be able to gather the data, however, you might be concerned about how you can utilize it. Due to the sensitivity of certain types of data, your company may worry about litigation proceedings from the same consumers you are trying to please.
  • Real-time: Data is added to the Internet in fractions of a millisecond. It can be difficult for your business to keep up with the ever changing scope.

Despite the many challenges that come with data, the benefits of having an effective data management and analysis plan pay off. You will gain the visibility into your competitors, marketplace, and consumers–the visibility needed to position your business as a leader in innovation and consumer approval.

Useful Facts about Factor Analysis

While some may have heard of the technique “factor analysis” many remain unclear about exactly what it is. How does factor analysis figure into decision making? How is it applied? What is it used for?  What are “latent factors”? While these terms may sound complicated or otherwise cumbersome the fact is that the ideas behind factor analysis are rather straightforward.

What Does Factor Analysis Do?

We are all living, breathing statistics. That may sound cold, but it’s a fact. We are all consumers of media, news, information, products, and services. Because all people really are to statisticians is statistics, it’s helpful to be able to strip away unnecessary information. Take people; as complex and individual and amazing though they are; and try to reduce them down to the lowest common denominator. In doing this the statistical “dimensionality” is reduced. Rather than complex octagons we are now simple boxes.

Latent Factors

Another important thing which this technique addresses are the “latent factors.” What does that mean? Well anything latent; a feeling, an impression, a hunch; is something that can’t be measured. So when you’re doing market research there are things you can know; name, age, gender, ethnicity, income level, occupation; and things you can’t know; passions, intelligence, motivating factors, upbringing. Still with factor analysis we’re able to group these individuals accordingly by the responses they give. As some have keenly observed “the observable data doesn’t create the underlying factor; the underlying factor creates the observable data.”

How Does it Help

One thing which many people conducting surveys do is ask questions which have no real merit unless observed repeatedly. If you asked 100 people in a restaurant with 35 tables and 100 chairs and 1 waitress how their experience was; likely all of them would say terrible. However if the one waitress was covering for 30 others, then the data you got for this one particular day about this one particular waitress would not stick in any real way. Diners experience on the next day when the place was fully staffed would be far different.

As the body giving the survey you want to be dealing with facts. Cold, hard, facts; things which can be correctly transposed to different settings with the same result. A plane takes off from tremendous speed; a helicopter takes off from a stationary position. These are things which cannot be debated. If you’re trying to group results together, factor analysis can really help. You are dealing with measureable items, your latent observation has been borne out and you can group these respondents accordingly.

Return Shoppers

You can also get measurable results if you’re asking the right questions. Using this technique you should be able to accurately peg a customer’s likelihood that they will purchase a product or service after using it once. If this technique was found to be accurate over an extended period of time it would be immeasurably valuable to all businesses who are trying to discover a products survivability in the marketplace. Using a tool like factor analysis to determine which products to push on full force and which to abandon would vastly change the landscape of the marketplace.

Of course whether or not this remains true over the longer haul; whether this techniques implementation has any lasting chance of thinking before and ahead of consumers, is still up for debate. Still with so much potentially riding on factor analysis’ success or failure, it’s something we all should try and get our heads around sooner rather than later. Nobody wants to be behind the 8-ball on the next big, sure thing!

3 Ways to Measure Customer Loyalty

Customer loyalty is a tricky sentiment to track.  It is difficult to measure customer loyalty because proof of loyalty, the state of being loyal, is most often shown after an action occurred that indicates a person’s loyalty.  However, past action often indicates but doesn’t guarantee future loyalty.

So how do you measure something that hasn’t happened yet?  You can look for patterns when analyzing responses to survey questions designed to measure specific indicators that, when taken in context by the analyst, have varying degrees of certainty as to future action.  Bob Hayes, author of Measuring Customer Satisfaction and Loyalty, breaks it down into 3 measurements: Retention, Advocacy and Purchasing.

Retention as an Indication of Loyalty

Retention is a reflection of a customer’s willingness to remain with a particular company’s service or products and is useful to measure customer loyalty.  Questions designed to determine loyalty are often based on the “How likely are you…” model to predicate future behavior.  Among wireless or other service provider companies, Retention is most often asked by the question, “How likely are you to switch?”  This question is an indication of the relationship the customer has with the company and may be an indicator of overall satisfaction. Although, the smart analyst should be aware that the question alone, without corroborating evidence, may be an indication of a deeper dissatisfaction with the competition rather than satisfaction with their current company.

This least of all evils attitude is often found in service industries such as cable/internet providers, wireless companies and banking.  To be helpful, retention questions should be supported by an investigation of the second measure, Advocacy.

Measure Customer Loyalty by Measuring  Advocacy

“How likely are you to recommend…?”  or How likely are you to purchase other products from us?” and ” How satisfied are you with…?” are typical advocacy questions.  They are related to retention because the assumption is that a customer that is a cheerleader for or satisfied with your organization is likely to remain with you.  They relate to the customer’s perception of the company’s image that they are doing something right.  Determining what that “right” something is requires additional investigation.  It may be related to a single experience or simply to an overall – but general – impression.

There is overlap between Advocacy and Retention but they are distinctly different.  Advocacy requires less action on the part of the customer, because to advocate does not mean purchasing.   Whereas Retention requires the costumer to engage with your company through the basic transaction of making an additional purchase (or renewing a service) which in itself is a strong indication of customer satisfaction.

However, the strongest indication of customer satisfaction is related to Purchasing.

(RE)Purchasing is a strong Customer Satisfaction Indicator

Purchasing questions like, “How likely are you to (continue)(increase)(purchase different) products from X Company?” are the best indicators of growth through customer loyalty.  They seek to determine if the amount spent per existing customer will increase or decrease based on additional purchases within or across product lines.  It is distinguished from the retention question of how likely are you to switch because a switching question may me a repeat of the same revenue (0 growth) rather than an increase in spending (positive growth)

Use All 3 To Measure Customer Loyalty

All three customer satisfaction indicators are closely related in that they measure costumer intent.  negative responses to these types of questions usually indicate a loss of that customer.  Either they will re-up, purchase additional products or feel good about your company/product/services — or they won’t or don’t.  It is fairly straight forward to develop relevant survey questions to receive the data.

What becomes difficult is providing the context for analyzing the survey data into a meaningful construct that can be used by decision makers.  That is the job of the analyst to rely on his or her experience, knowledge and expertise to put the data into perspective.

Mind Your Measurement Scales in Market Research

Welcome to the Making Molehills out of Mountains University (MMoM U) Market Research Data Analysis 101 or MARDA 1 as we like to call it in the halls of academia.  Today we discuss the four different types of scales used in measuring behavior.  Open your books and let’s get started…

The four scales, in order of ascending power are:

  • Nominal
  • Ordinal
  • Interval and
  • Ratio

Nominal Scale

Nominal is derived from the Latin nominalis meaning “pertaining to names”.  But, seriously, who cares? That tells us nothing except how much academics love showing off.  The Nominal Scale is the lowest measurement and is used to categorize data without order.  For your market research data analysis exercise a typical nominal scale is derived from simple Yes/No questions.

How the nominal scale (and all these scales) is used statistically is for the next lecture.  For now, just know the behavior measured has no order and no distance between data points. It is simply “You like? Yes or no?”

Ordinal Scale

From the Latin ordinalis, meaning “showing order”… Enough of that.  An Ordinal Scale is simply a ranking.  Rate your preference from 1 to 5.  Careful!  There’s no distance measurement between each point.  A person may like sample A a lot, sample B a little, and C not at all and you would never know.  Here we have gross order only, learning that the subject likes A best, then B, then C.  Determining relative positional preference is a matter for the next scale.

Interval

Ah, the Interval Scale.  It’s the standard scale in market research data analysis.  Here is the 7 point scale from Dissatisfied to Satisfied, from Would Never Shop Again to Would Always Shop,  etc.  The key element in an Interval Scale is the assumption that data points are equidistant.  I realize savvy market analysts might say, “Hold on Professor. What about logarithmic metrics where the points are not equidistant?” To which I say, “Correct! but the distances are strictly defined depending on the metric used, so don’t get ahead of yourself. This is MARDA 101.”

For now, understand that with the Interval Scale, we can interpret the difference between orders of preference.  Now we can glean that Subject 1 Loves A, Somewhat Likes B and Sorta Kinda Doesn’t Like C.

Subject 2 Somewhat Likes A , Sorta Kinda Doesn’t Like B and Hates C.  Both subjects ranked the samples A, B, & C on an Ordinal Scale but for very different reasons as discovered by using the Interval Scale. Got it?  Good.

Moving on.

Ratio

Similar to the Interval Scale it’s not often used in social research.  Like Interval, it has equal units but it’s defining characteristic is the true zero point.  Ratio, at its simplest, is a measurement of length. Even though you cannot measure 0 length; a negative length is impossible, hence, the true zero point.

To sum up, I leave you with the the chart below, indicating various measures for each scale.


Difference
Direction of Difference
 Amount of Difference
Absolute Zero
 Nominal  X
 Ordinal  X  X
 Interval  X  X X
 Ratio  X  X X X




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.

Extend Market Research with Technology

For your business to flourish, it should have the right amount of customers to purchase the product or service offered. Analyzing your customer base, then defining your marketing research technology efforts to those specific customers is essential, in addition to having a transparent view of how your expected customer base will enhance your business possibilities for success. By delineating your target consumer, you will:

  • identify the potential customer base for your business
  • refine your business strategy and products to meet your customer needs
  • aim your marketing ideas to touch your more hopeful prospects
  • establish your marketing messages suitably

With its capacity to extend to consumers at various touch points and acquire nearly instantaneous results, there is no secret to why market research has taken to the Internet so well. Even though market researchers have a broad range of options for gathering data, acquiring reliable research and efficient analysis instruments has become even more crucial than before. It is no revelation that market research technology will resume its evolution. Current and potential trends are directed towards social media and user engendered feedback where you can evaluate what consumers are saying compared to only watching them.

Your ability to acclimatize to the new market research technology trends will be a vital component in remaining competitive and providing the products and services that consumers are looking for. Resources of technology interrelated with telepresence, neuroscience, eye tracking, and inherent association analysis will as well influence marketing research moving ahead. For instance, companies have utilized telepresence to locate isolated and hard to reach respondents. In addition, voice-activated video with audio, concurrently share laptop screens, and organization to consult with multiple locations at once with a technology that produces a distant participation milieu.

A dilemma with the various types of market research technology is that it cannot solve the question of why one brand has a stronger presence than another one. This type of innate question must still be answered. Neuroscience has of late been utilized to answer market research questions in ways that social media and traditional methods have been limited. However, prognostic analytics, eye tracking, association dimensions, and neuroscience technology motivated systems will on no account substitute for speaking to customers to comprehend what they truly feel and consider when presented with brands and various other manufactured rudiments.

Closing the Information Loop with Actionable Data

Analyzing survey data and turning it into actionable data is the key to maintaining your current customers and adding to that customer base. But what is actionable data? Basically, actionable data is your survey results input into an improvement program to modify your business and make it better.

In order to effectively transform your piles of information into actionable data and make analyzing survey data work, you need to look at the actual questions you ask on your survey. If you don’t ask the right type of questions your data could be misleading and as a result, you will misuse it with no benefit to your company.

Close Ended Questions

These are simple clear questions that ask a survey taker to rate specific aspects of the products, services and experiences they engaged with. They respond on a numbered scale or check boxes of different graduated measurement to reply to the questions. This type of questioning has two useful advantages.

  • Easy Analyzing: Analyzing the survey results is very simple, since all the data is coming to you as a check mark, number or yes/ no answers. The guess work is taken out of the analysis and ambiguity is kept to a minimum. The simple responses make calculating the results and transposing them into percentile information or mathematical equations very easy.
  • Easy to Take: The actual survey itself is easier for your subject to navigate through. Clicking on responses they identify with is much easier than writing down specific detailed narratives. If it’s easy more people will take it and that will give you more data to use in your growth process.

Open Ended Questions

Open ended questions ask your survey takers to describe something in detail. While analyzing survey data, the more detail oriented the survey answers are, the more focused and exact our improvement efforts can be. The problem is that open ended questions rarely get answered and if they do it’s with one or two words. Not really the detail you were looking for and very misleading if you use it. Open ended questions should be used sparingly and only as an addendum or supplement to the closed question parts.

Filtering and Branching

Two survey construction techniques that, when used correctly, will make analyzing survey data easier by pre-sorting and categorizing certain elements of the survey as determined by you.

  • Filtering: In the survey, it serves the useful purpose of weeding out the subjects that you don’t need results from and lets you focus on subjects whose opinions you want. When it comes to analyzing, you can implement filters like location, age, or whatever to help with the process. This allows you not waste peoples time (including yours) and will increase the amount of useful data.
  • Branching: Similar to filtering, branching just divides up your subjects and sends them down different survey roads, if you will. This allows you to view specific results easily and categorize your raw data. Branching serves as a pre-sorter that will streamline you process for analyzing survey data.

Conduct Interviews: Yes, more surveys. I know it sounds weird but if you think about it, it makes sense. Interviews effectively expand the scope of your resulting data. The pre- and post-survey interview helps to make sure the subjects understand the topic or field the survey is concerned with and they add dimension to the scope of the main survey.

Analyzing survey data and turning it into usable, actionable data will positively effect your business. Invest the resources into well thought out data gathering and analysis programs and reap the rewards.