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.