As a business, you know the importance of surveys and how they can help analyze the success of your marketing plans, as well as product usage and placement. There are several ways to go about optimizing and analyzing survey data to ensure you’re getting the best information. Continue reading
You have the perfect product or 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
Every good customer survey should be utilized with proper timing. This is a critical factor which ultimately determines how effective your survey is. The issue of timing should include both when to do it and how often, as well.
In reality, you should be committed to gaining an understanding of what your customers think about your products and services. You should be excited to get insight into what to adapt in order to get more customer satisfaction. However, presenting a customer survey too soon is like a half-baked cake. Continue reading
When customers want to feel effective, they can be motivated to take customer research surveys. The easier it is to do this, the more likely they will be to fully comply. The more compelling the invitation to participate, the more likely it is you will get the results you need.
Just as surveys give information that improves results, surveys that follow specific guidelines have more chance for capturing the necessary information. Design template choices can be pivotal for specific results. As with most things, the simpler it is to comply, the more likely it is you will get your needs met.
Market research and analysis of large volumes of data are necessary when it comes to analyzing and determining the right market segment, potential demand, and potential areas of competition, product development requirements and all other facets of the business marketing portfolio. One of the most common tools used to deal with the vast amounts of data is Factor Analysis. Continue reading
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.
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.
What is Data Visualization?
Data visualization is the study of data being communicated visually through graphical means. Bar charts, graphs and maps are illustrations of basic data visualizations that have been utilized for quite a few years. Over time, visualizations have become more intricate to the extent of having animations that illustrate changing data over a period of time. There is virtually no limit to the translation of information into an image.
As the visualization designer, you can ascertain which visual components, such as color and shape, symbolize and communicate particular data points. For example, images could be 2D or 3D, dynamic or fixed, or more importantly they could allow user interaction.
Visualizations provide a unique way to communicate trends and correlations that can ultimately lead to important marketing breakthroughs. Visualizations that represent a large amount of information allow you to see patterns that may normally be hidden in unconnected data sets.
Using Data Visualization to Immediately Observe Key Points
Data visualization does not occur in isolation. Therefore it is important that you understand the context that you are communicating. When planning your communication, make sure it meets these key points:
- Precision. Could it be accurate with regard to proper quantitative assessment?
- Versatility. Can the creation conform to assist numerous wants?
- Aesthetically Pleasing. The data visualization must not insult the reader’s senses; a great example of this is moire patterns.
- Efficient. Is your visualization simple to understand? Will the viewer “get it” with ease?
- Effective. You need to be sure you display the information; evaluate regardless of whether you need to increase or even reduce data-ink percentage; additionally think about brase non-data-ink as well as brase repetitive data-ink.
The Importance of Making Data More Usable
In recent years, a variety of methods have been proposed for analyzing the consequences of data effectiveness. During a study conducted by the University of Texas, researchers found that if a company increased the accessibility of data for the user, by only 10%, the result could increase revenue drastically and create new jobs. The researchers analyzed accessibility, mobility, quality, and usability to come up with these findings. Overall, improving your company’s data usability will likely benefit business and should not be overlooked.
This article is the first in a three part series exploring the twin concepts of customer engagement and the engaged customer. Some CEOs, especially in the SaaS arena describe an engaged customer and a non-engaged customer like describing the difference between a Van Gogh and overstock office art , “I don’t know much about art, but I do know what I like.”
What is an Engaged Customer?
An engaged customer is engaged by your company on two fronts: (i) before, up to, and including the point of sale and (ii) during the life cycle (use) of the product or service. Recent research has shown that an engaged customer exhibits three specific traits that, if identified, promoted and supported within your organization, translates directly to a better bottom line.
Common Attributes of an Engaged Customer
A hallmark of an engaged customer, loyalty is an essential trait for survivability in the current slower paced and more costly market place. It costs less to maintain (for repeat business) a current customer than to identify, attract and convert a new one. The slower pace refers to the lower rate of business transactions rather than the frenzied pace of product development.
Customer interaction refers to the customer’s relationship with your company. Interactions occur on line and off line, before and after the sale.The type, quality and frequency of these interactions have a huge impact on maintaining an engaged customer.
Providing a means for customers to provide feedback and input about your product or service creates a sense of involvement in the process of determining what the consumer wants. If your average customer’s input has an identifiable impact on your product offering, they are more likely to buy the product or update once it is available.
The Point Is…
The methods of engagement vary but most commonly involve an interaction before there is a problem or complaint. On line; product reviews, product ratings, surveys and live chat are recent customer engagement examples. Check-in calls to customers, special events at retail outlets, unique purchasing opportunities and loyalty point programs are examples of off line engagement.
Regardless of the method, the goal of engagement is to create a deep connection with customers. Your customer’s involved relationship and emotional brand attachment are competitive advantages that are difficult for competitors to overcome. This leads to the question, “How do you measure customer engagement?” That is the topic of the 2nd article in this series.
A scientist or sociologist may define it as a body of methods used to help describe facts (based on observation), detect patterns, develop explanations or test a hypothesis.
A marketing manager may define it more closely to the Wikipedia article, “Analysis of data is a process of inspecting, cleaning, transforming, and modeling data with the goal of highlighting useful information, suggesting conclusions, and supporting decision making.”
Ask a mathematician or a statistician and they may well tell you that to define data analysis one must first define statistics, which, may be defined as a set of methods used to collect, analyze present and interpret data.
So again we ask the question, “What is Data Analysis?” We define it as the process of taking raw information (data) and creating something understandable and useful, that either supports or rejects a specified goal. In other words, when the CEO asks, “Should I or can I do what I want to do?”; analysis of the available data will support either a yes or no answer.
Analyzing Data For The Real World
Although raw data, expressed as a numerical result, describes values that relate to questions, in the real world the analysis of this data is not about the numbers. Rather it is about translating those numbers into something meaningful that relates to a real world question.
Your analysis may indicate trends or show an average, median or mean. It can identify groups or sub groups with common characteristics or differences among those groups. Data analysis can show the rate of growth or the rate of decline. When translating these numbers into an answer for a real world question, a skilled analyst can present information in such a way as to significantly influence a decision maker to move in a certain direction. However, using the same data, it is possible to make a strong case for going in the opposite direction. This apparent contradiction is not found within the data; rather it is found within the motivation, background and bias of the analyst and/or within the political and economic constraints of the analyst’s organization or client.
Analysis Presents a Position
Ultimately, data analysis is used to present an argument. Should the company promote product A? Should it raise prices? Can we sell more of product A if we introduce change B? These types of ‘Should we’, ‘Can we’ questions require the support of data that has been collected, analyzed and presented in as unbiased a manner as possible. The data that is collected is rarely, if ever, a Should we, Can we question. Most often the Should we, Can we questions are answered in response to data collected relating to Who, What, When, Where or How questions. (Who buys hair spray? What is the median income of our clients? Where do they shop? When do they shop? How do they shop? etc.)
Bias and Mistake
It’s up to the analyst to interpret and present the data from the Who, What, When, Where and How questions to the decision maker in such a way as for the decision maker to answer the Should we, Can we questions. For the reasons stated above, presenting a wholly unbiased interpretation is nearly impossible. Often it is not the process of analysis that is at fault but rather the conclusions drawn from the information (or how they are presented) that lead to the failure of a product launch or ad campaign. A primary culprit leading to erroneous conclusions is mistaking correlation for causation. The classic example: roosters do not cause the sun to rise! There are, however, numerous techniques and tools analysts use to minimize errors but those require additional explanation not suited for this post.
What is Data Analysis Then?
So we ask one more time, “What is Data Analysis?” I would argue that data analysis is simply the interpretation and presentation of the validity or invalidity of data as it relates to a question posited by a decision maker who needs to understand and rely on the information presented by the analyst in order to take action on that question.
In other words, data analysis is the telling of the data’s story. It is a story that began with the questions, “Should we or Can we do what we want to do” and ends with the answer, “That depends on your interpretation”.