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
There’s little question that investing in product research and subsequent development has the ability to reap dividends for a company, which is why it’s a tactic utilized by so many businesses. Nonetheless, you’re probably intimately aware of the difficulties inherent in trying to obtain definite results, because of concerns with consumer forthrightness, your own poorly-worded questions for a particular demographic, and simply finding the optimal time to pitch surveys to the busy customer. Thankfully, there are some things you can do to facilitate market research data analysis, so that the results return something you can actually work with, to better your product or service. Continue reading
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
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.
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:
- Interval and
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?”
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.
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.
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.
||Direction of Difference
|| Amount of Difference
The widespread adoption of the internet, and now smartphones, has revolutionized all industries with very few exceptions. If you have been in the market research industry for the past few decades, you know first hand how drastically these technological advances have changed the way researchers conduct business.
In the mid to late 80s, gathering survey data by phone and mail had become the standard over surveying consumers door-to-door. Today, however, the declining popularity of landlines and traditional mail has led researchers to place more focus on online alternatives. With its ability to reach consumers at multiple touch points and near instant results, it’s easy to understand why the internet has won data collection dominance. While market researchers have a wide selection of options for collecting data, having reliable research and effective analysis tools has become more vital than ever.
We know that market research technology will continue to evolve. Present and future trends point to social media and user generated feedback where we can analyze what consumers are saying rather than just observing them. The ability to adapt to these new trends will be an important factor in staying competitive and delivering the products and services that consumers want and need.
In spite of all the changes in market research over the past two decades, the objectives remain the same: glean insights from consumers, and respond in a manner that will increase sales and market share.
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.
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?
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.
t is one of the most profitable methods for selling goods and services. Once a company has attracted a consumer to ‘buy’ and ‘try’, a satisfied customer is the best means to retain that business for the foreseeable future.
There are many reasons to keep customers satisfied; most importantly to earn their business again. All marketers know that it costs much less to convert an existing satisfied customer into a repeat sale than to ‘conquest’ a new sale from another brand. If you browse enough web sites, you will see that…
- Acquiring new business can be 5 times more expensive than retaining customers.
- Increasing customer retention by just 2% can translate into a 10% cost reduction
- Some retailers indicate their top 15% of ‘loyal’ customers comprise 50% of their sales revenue.
Those are some valuable customers and the statistics speak to why a high customer retention rate is so important. Substantial sums of money are spent on survey data and questionnaires because a Satisfied Customer = A Valuable Customer !
So, how likely is your company to retain customers in the future? Past and current behavior is the best predictor of future trends. It is of the utmost importance for marketing analysts, brand managers and advertisers to understand both the current retention rate and defection from the analysis of ownership and/or survey data. The Customer Retention rate is often referred to as Loyalty. Loyalty is represented by the percent of current owners that repurchase the same brand. Those who don’t repurchase the same brand are defectors and they represent lost business.
Also of importance to brand health is the measurement of conquest. Instead of looking at the current owners and “where the business went” (retained or lost), a look at new customer’s behavior will supply a measurement of where the business came from, or where new sales were ‘conquested’ from a competitive brand. Conquest is also important when considering a brand’s customer retention rate.
The graph below shows overall brand health by plotting the brand customer retention rate (loyalty of current owners) against the conquest rate (new sales from a competitive brand).
Brand A is the healthiest of brands given its strength in customer retention of current owners while attracting new buyers from the competition.
Brand B is successful in attracting brand switchers, but needs to work on current owner’s satisfaction to ensure a higher customer retention rate.
Brand C is relying on customer retention, but is in danger of slipping into ‘Decline’ if a downward trend in loyalty occurs.
Brand D is the weakest relative to its competitors and needs to identify a strategy to move its position.
Where is your brand’s health in the customer retention and conquest relationship? Is your survey data capturing these elements? What does it look like in terms of specific demographics or geography? How does it compare over time?
PAI would welcome the opportunity to demonstrate how PAI’s mTAB™ service would benefit your understanding of customer retention rates, defection and conquest. Please visit the PAI website to schedule a no-obligation review of mTAB for an analysis of consumer behavior data.