Organizing and Interpreting Complex Sets of Survey Data

Large surveys make the task of data analysis much more complex. In order to uncover meaningful information from your large survey efforts, you will need to define a logical process for organizing and interpreting survey data at the start. While there are numerous techniques you could employ, the following offers a series of reliable techniques for working with complex sets of survey data. 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

GM Unhappy with Facebook Ad ROI

The most anticipated tech stock to date debuted after weeks of speculation as to where the final numbers of its IPO meter would land. Coming out of the social media sector; Facebook’s decision to become a public company may have been the biggest news of the year. Unfortunately for the advertising giant, disappointing news about one of the largest U.S. advertisers is making a big splash in the media world as well: automaker General Motors has decided to withdraw its $10 million spend on Facebook ads due to subpar results of their click-through metrics and marketing data analysis.

The burning questions all revolve around the single word ‘Why?’ While there has been lots of speculation as to why GM experienced lackluster results from Facebook ads, we wanted to share some of the key points that we feel merit a closer look.

1. Is it Facebook, or did the ads themselves contribute to the sub-par click-through performance?

Although the nature of GM’s ads likely had a part to play in the lack of success, Facebook doesn’t get off scot-free. According to recent studies, nearly 60% of Facebook users say they never click on ads or sponsored content. Eye-tracking research shows a decrease in visitor interest in advertisements that are shown in the Timeline feature rolled out a few months ago.

2. Is click-through the best measure of Facebook ad performance?

Some important details left out of all the media reports is what other, if any, key performance indicators was GM using to measure the success of Facebook ads. Click-through rates can be a bit tricky when it comes to social media; which is a notably different advertising platform than Search. Ad click-through rate needs to be considered alongside other metrics, depending on the marketing objectives. Other valuable metrics may include the number of Facebook fans or engagement rates.

3. Is GM more disappointed with the lack of new ad innovations for Facebook advertisers?

Facebook has been known to place more emphasis on the platform’s user experience over advertising innovations. The social media giant has yet to prove that creating a solid advertising model is high on their list of priorities. In fact, Facebook currently does not even support advertising on smartphones or tablets, one of the fastest growing segments for reaching consumers.

4. Did GM’s multi-agency approach to Facebook advertising play a factor in poor performance?

Facebook claims the lackluster ad performance could be due, in part, to GM’s multiple agency approach. While GM budgeted $10 million to Facebook ads, they spent $30 million on agency costs. Multiple agencies working within a single channel can lead to major inefficiencies and a disconnect in strategy. If we had more behind the scenes details on how GM’s Facebook strategy materialized, I’m relatively certain we’d discover some level of culpability here.

GM’s decision to cut ad expenditures doesn’t appear to be a knock specifically on their confidence in Facebook. The auto giant plans to continue focusing on its free Facebook presence and continue engaging consumers. Perhaps Facebook should be charging for site privileges for companies who do not pay any advertising costs. Maybe we will see new Facebook user categories in the future: free or paid.

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.

An Intro to the American Customer Satisfaction Index

What do airlines, large banks and telecom corporations have in common? They are among the least-liked companies in America.  How do we know? The American Customer Satisfaction Index (ACSI) tells us so. It’s the only uniform national measure of satisfaction with goods and services across a representative spectrum of industries and the public sector. The ACSI utilizes patented methodology to identify factors driving customer response and applies a formula to determine the cause-and-effect relationship between those factors and satisfaction, brand loyalty and overall financial health of a company.

ACSI data allows companies to reach informed decisions about current products and services and also make projections about changes under consideration. It’s a tool for managers to improve satisfaction and build customer loyalty and a means to evaluate competitors. ACSI scores also help investors evaluate the present and future potential of a company. Historically, stocks of companies with high ACSI scores outperform lower-scoring firms.

Developed by researchers at the University of Michigan and first published in 1994, the ACSI releases full results on a quarterly basis with monthly updates. The survey rates satisfaction with 225 companies in 47 consumer industries and more than 200 programs and services provided by federal agencies.  Data about customer satisfaction is gathered from random telephone and email interviews with 250 customers. To generate ACSI results, over 70,000 interviews are conducted each year. Consumers respond to questions about a company by rating three factors on a 1 to 10 scale: Overall satisfaction, fulfillment of expectation and relative comparison to an ideal product or service. Companies are chosen for scoring based on total sales and position within their industry. As company fortunes wax and wane, some are deleted from the survey and others added.

In addition to rating individual companies, the Index generates overall scores for 43 industries, 10 economic sectors plus a comprehensive national customer satisfaction score—now considered a significant metric for the health of the economy at large.

The scores from the American Customer Satisfaction Index are awaited by companies, economists, investors and government agencies alike. Some of the general conclusions gleaned from the results include:

  • Variations in customer satisfaction indicate the mood of consumers and accurately predict their readiness to buy products or services.
  • Since consumer spending makes up the majority of the national gross domestic product (GDP), spikes or dips in ACSI scores serve as an early warning to fluctuations in GDP.
  • Quality, not price, is the primary factor generating customer satisfaction in most industries scored by the ACSI.
  • High-profile mergers, acquisitions, large layoffs and other internal uncertainties degrade a company’s customer satisfaction score.
  • Service industries are generally positioned for lower ACSI scores than the manufacturing sector.

Around the world, many countries are implementing surveys based on the ACSI model. In the future, ACSI methodology may evolve from a one-nation metric to a global quantification. As national economies expand into worldwide markets, international data on consumer satisfaction and a company’s—or a country’s— relative success in fulfilling it will prove vital.

The Challenge of Data Overload

The world seems so much bigger today than it ever was.  According to reports, more than 1.2 zettabytes of digital information was created in 2010. The availability and importance of data is increasing at staggering rates; yet, at the same time, it costs billions of dollars to control. Companies that have placed little investment into data analysis resources struggle with data overload–unable to take advantage of the information available.

Overcoming Data Overload

Most companies have no problem admitting to the paralyzing effects of having too much data. Business leaders face major challenges in the decision-making process when data overload exists. In an effort to minimize the caustic effects of data overload, it’s important to define which facts are critical to move the business forward.  Without set parameters around analyzing data, business leaders have no means of knowing what data is valuable and what data can be ignored.

The following suggestions can help make data analysis more manageable:

  • Determine your company’s information needs on a daily, weekly, or monthly basis.
  • Select the KPIs that matter most to your business.
  • Identify specific financial drivers  – such as customer satisfaction and loyalty.
  • Make information available in a visually appealing format.
  • Ensure that your analytic tools can leverage all available information.

While these are simple and effective steps to improving how your company utilizes data, they do not replace the need for high quality data analysis tools and professionals.

Is Your Business Ready for Big Data?

Big Data could mean Big Bucks for the companies that know how to gather it and use it. However, since the discovery that big data can help drive up revenue the number of organizations that actually use it are few and far between.

What is Big Data?

As our world becomes more technology driven, large companies have been at the forefront of implementing new technologies and seen data management become an important part of business. High definition video, Tweets, Facebook, tags, SMS and intelligent chips do more than process information and accelerate communication, they also leave a digital trail. This trail, when analyzed properly, can be used to track what consumers consider important.

What Do You Do With Big Data?

This is the key question that many businesses are struggling with. But fear not, the business world has given us the Data Scientist. This new specialist position is not merely an IT guru or analyst, but leaders who forge a collaboration of sorts that combines the creativity of marketing aspects with the science of the numbers to turn out actionable data to drive new marketing strategies.

Why Aren’t More Businesses Using Data Scientists?

Indicative of technology; the idea of Data Scientists has spread fast and created a demand. The number of Data Science experts is not nearly great enough to meet the demands. Mashable reported in the largest-ever global survey of the data science community that:

  • 63% of data science professionals believe demand for data science will outpace the supply of talent over the next five years.
  • Lack of training (32%) and resources (32%) were identified as the two biggest obstacles to Data Science adoption.
  • Only 1/3 of respondents report they are confident in their ability to make business decisions based on new data.
  • 38% of business intelligence analysts and data scientists strongly agree that their company uses data to learn more about customers.

Results suggest that Data Science, as a field is still in its infancy and elusive to many companies. Some of which are oblivious to the growing necessity for Data Scientists.

No Matter the size of your organization, the utilization of Data Scientists could drive a huge boost in your bottom line. Being able to turn your tracking information into actionable data that results in profits is quickly becoming the new frontier of business. Implementing the Data Scientist concept to help streamline your marketing plan will not only generate mere income but it will also change the structure of today’s businesses.

Customer Experience & Data Analytics Take Center Stage

Improving customer experience has been reported as one of the top priorities for businesses in 2012. Some companies are even adding a new C-level position to their rosters, Chief Customer Officer (CCO).

Data Analytics

A big driver of this focus on customer experience can be attributed to the gold mine of information gleaned from data analytics. There is so much information available about consumers’ likes and dislikes that companies can analyze the data to build a better experience.

While all this information is great, companies must be cautious in how they extract and use the data. If companies extract only the information which validates their decision making, the customer takes a backseat and all the progress from this data analytics will be lost.

Customer Needs

The bottom line is that the customer should always be the top priority for the business. Yes, businesses should be making solid, innovative products. However, if the customer isn’t interested in buying the product, then the product will have no audience. Before you consider launching a new product, you should listen to what your customers are saying and identify their needs.

Brand Experience

In addition to offering solutions to customer needs, it’s important to ensure the customer has a great experience with the brand. If the customer, again and again, has a bad experience in your store, or with your product, or with your sales force, you can bet that your product will have limited reach. The customer experience should be top-of-mind from production to manufacturing, and from distribution to sales & support-it is paramount in cementing a good relationship between the consumer and your brand.

Determining Demand with Statistical Demand Analysis

Today we’re going to do some thinking and learn about statistical demand analysis.  Why is statistical demand analysis important? Because estimating product demand is essential to reduce the risks inherent with pricing, production and inventory decisions. A failure to accurately estimate demand can lead to over or under production, and if priced incorrectly can negatively affect profits.

How is demand determined?

We’ll talk about two types of demand: Consumer demand and Market or Industry demand.

  • Data on consumer demand is usually obtained through consumer surveys by asking how much they would purchase at a given price point (predictive) or how much they have purchased in the past (retrospective).
  • Market Demand is looked at through a series of price changes over time and analyzing the subsequent change in purchasing activity.  Finally, and the point we will focus on, using naturally observed data on price and quantity and subjecting them to statistical demand estimation.

Statistical Demand Analysis using Liner Regression Model

Recall that regression analysis is a statistical technique for estimating the relationship between variables.  In this case, Demand as a result of price vs quantity sold, city, production, consumer demographics, etc.  Demand, being the dependent variable, is shown in relation to and linked to various parameters (aka assumptions) explaining the relationship.  This is shown by the following linear regression model:

Q = a + bP + cM + dPR +u
Q = quantity demanded (observed) and is the dependent variable
P,PR,M = price, price of related goods, income (observed) = independent variables.
u = random error (not observed)
a,b,c,d, = parameters – unknown and estimated.

Using regression analysis the analyst first obtains a set of survey data (regression data) for Q, P, Pr and M.  Next an estimation of a, b, c, and d is used.  These estimations are difficult and rely on experience, industry knowledge and internal data. When done, you test for significance of the parameter values and using the estimated values of the parameters for analysis, are able to predict the future value of the dependent variable, e.g. demand.

There are three types of regression data: Time series, Cross section and Panel data.  Time series is an observation made of different time periods while Cross section is for different observations at the same point in time.  Panel data is a combination of both.  These are the Data for Q, P, Pr and M.

Specify the Demand Model

Here we’re talking about the unknown or estimated parameters. It is up to the analyst to determine if she will rely on consumer behavior, price, income, price of related goods, size of market, tastes, price expectations etc.  Then, choosing a functional form, usually a choice between liner form and power function.

Linear form looks like this:

Qx – a + bPx + cPr + dM + eN + u

The parameters in this linear function tell the change in Qx for 1-unit change in the associated variable.  It looks like this”

b = ?Qx/?Pr ; c = ?Qx/?Pr; d = ?Qx/?M; e = ?Qx/?N

Determining demand is not an exact science and the accuracy of any prediction is based, in large part, on the experience, knowledge and industry expertise of the analyst.  This is true because of the assumptions the analyst must make in determining the unobserved and unknown variables present in the linear regression analysis.

Linear regression analysis for statical demand gives a more accurate picture of future demand rather than relying on crude projections based on past sales.

We did not cover the power function which is used frequently if the analyst believes demand is non linear.  If anyone can tell me how the power function would look, feel free to comment.  See you next time in Making Molehills out of Mountains University, Market Analysis 101.