Is Data Tangible or Intangible?

Did you know that electronic information is tangible? From the apps you use, the games on your phone, right down to every message you send – all of it appears to invisibly float away and live somewhere far off in the ethers, but actually, most of it will land with a thump in our earthly domain.

Because of our impression that information is invisible, we can end up taking the resources it requires for granted. Data centers or server farms dot the globe, and actually come with considerably large carbon footprints, because of not only the power the require to run them, but also to keep them cool. In the United States Continue reading

Leveraging Data Analytics for Competitive Advantage

When seeking to gain a competitive advantage, data analytics can really fall in to only one of three areas: accurate, misleading or irrelevant. What about inaccurate, you ask? Inaccurate data is misleading and therefore inaccuracy is also not relevant.

Customer data is captured by either passive (click rates, POS data, etc.) or active (customer feedback, surveys responses, etc.) means and is used extensively in business to gain a competitive advantage. Keep in mind that the larger the sample pool and the longer period of time the data is gathered, the more accurate an analysis will be. This article focuses on the benefits of leveraging data analytics for competitive advantage rather than on the types of data gathering techniques.

Types of analytic solutions and models:

Segmentation: divide customers into homogenous groups.
Classification: including decision trees, logistic regression, etc for predicting customer behavior
Forecasting: used to predict future metrics based on historical data, e.g. sales forecasting.
Basket Market Analysis: Customer purchase data used as a basis for segmentation.
Explanatory Modeling: Used to determine why something happens.
Predictive Modeling: Used to classify customers, or prospects
Decision Modeling: Used to guide decision making for a particular situation.
Customer Value Analytics: in conjunction with Segmentation and classification, it is used to identify customer type and priority.

Benefits of Data Analytics

In terms of survey analysis, the following is a list of benefits to using data analytics for competitive advantage along with the analytical solutions and models used:

  • Tailored offerings to fulfill specific customer needs.
    • Segmentation, classification, forecasting, predictive modeling, decision modeling 
  • Develop new products or services or determine product and service features that are important to customers
    • Segmentation, classification, forecasting, predictive modeling, decision modeling, market basket analysis 
  • Better customer relationship management.
    • Customer value analytics, classification, explanatory modeling, customer experience analytics, Exception analysis
  • Find, attract and retain the best customers
    • Customer value analytics, classification, explanatory modeling
  • Distinguish preferred from marginal customers.
    • Customer value analytics, classification, explanatory modeling 
  • Customize marketing plans to specific markets.
    • Segmentation, classification, forecasting, predictive modeling, decision modeling, explanatory modeling
  • Identify new market opportunities.
    • Segmentation, classification, forecasting, predictive modeling, decision modeling, explanatory modeling
  • Develop insight into changing customer requirements,
    • Customer value analytics, classification, explanatory modeling
  • Target pricing
    • Customer value analytics, classification, explanatory modeling, predictive modeling, market basket analysis 
  • Determine which customers are open to cross-selling.
    • Segmentation, classification, forecasting, predictive modeling, decision modeling, market basket analysis

Steve Jobs’ effect on the market research community

Can Mr. Jobs be credited with inventing the idea of market research?

Well, no!
But he can surely gain credit for shaping the future of how it is conducted! For so long, if you wanted to find out what people thought of your product, the only way was to get out on the street and ask them. Of course, we then moved on to mailing out questionnaires and interviewing by phone, but each of these requires a significant time investment on the part of the interviewee.

With the digital age upon us, things have moved on rapidly and peoples thirst for information seems never ending. High speed connections are everywhere and you are never far from a screen of some kind.

With this technology explosion, researchers have worked hard to keep up, fielding surveys by cell phone based text messages, emails, web pages and any other means they can conjure up. Consumers are encouraged to participate in research efforts through links on retail receipts, direct TV/radio advertising and most recently through the use of ‘QR Codes’ that are placed in magazines, newspapers, on product packaging, stickers and even clothing, encouraging the inquisitive among us to pick up their smart phone, scan the code and follow the link to find out more!

So what, you say, does all this have to do with Steve Jobs? Well, it was only last week that it was noted that “around 92% of Fortune 500 companies are currently testing or using iPads as a corporate solution”. And to make that statistic even more impressive, the iPad has only been on the market for just over 18 months!

Whether it be in the local mall, small town tourist office or at a specialist research event, tablets are the go-to device of the moment for data collection.  Apps are springing up to make this easier than ever and with their portability and ease of use, this trend will only increase.

The unquenchable thirst for knowledge that I mentioned earlier, now seems to be driving more and more people to not only want to collect data via tablets, but also to want the ability to delve in to the data further, to find the nuggets of information hidden within it.  Developers are reacting quickly to this need and in the very near future, it is safe to assume that the number of tablet based survey analysis tools will grow drastically.

And all any of us can really say is: Thanks Steve!

To learn more about how to prepare your survey data for analysis, feel free to download our whitepaper Ten Essential Prerequisites for Survey Data Analysis.

Cross Tabs and other marketing research techniques

Business is tough. And a company needs everything it can get in order to get a leg up on the competition. As a result, businesses are relying more and more on market research to better determine the direction their company will go in. The task of market research analysis, then, is to provide these companies with relevant, accurate, reliable, valid, and current information, often in the form of graphs, charts, or tables. Thankfully there is a wide variety of analysis techniques and formats that can be used to glean this information. Below is a list of a few market research techniques that help analysts compile information.

Frequency Distribution tells you how many people chose a particular response to any single question.

Survey Cross-Tab Analysis allows you to see how responses to one question work in conjunction and/or comparison to another. For example, you might want to know what percentage of purchasers of your product also purchased a competing product. This is a powerful method giving a maximum amount of flexibility to the analyst.

Average by Category lets you compare the average value response for different, specifically defined groups. For example, you could compare the average age for men and women, or for full time employees and part time employees.

Cross Tab Means combines the two methods above creating a more complex measurement. For example, you could see how overall satisfaction is rated by men who own a Toyota.

Segmentation lets you create groups of customers who share similar traits or behaviors. This allows marketers to zero in on specific customer groups and market directly to them.

Gap Analysis allows a business to see how large a gap there is between what it is currently offering and what its customers want. For example, a survey might ask customers of an auto repair chain to rate four different things: service, quality, value, and reliability. Survey results show that “service” is consistently rated lower than the other factors, so this is the area that offers the greatest opportunity for improvement.

From simple frequency distributions to the more complex gap analysis to the extraordinarily powerful cross tabulation, these methods allow analysts to glean important information about their products and customers that can help them guide the company to create new and better products and serve their customers’ needs better.

Pivot Tables – what are the alternatives?

Creating concise, informative summaries out of huge lists of raw data is a common task and spreadsheets of old have long troubled our weary eyes and tired minds with endless rows and columns of unserviceable data. Digging deep into the data with a standard spreadsheet required knowledge of spreadsheet formulas, math skills, and a tedious mind. The powers that be created the pivot table to replace this unnecessary work, forecasters everywhere rejoiced and were glad.

Data spreadsheets can contain a lot of information and sometimes it may be difficult to obtain summarized information in a simple fashion. Let’s say you’ve compiled a long list of data – for example, sales figures. A pivot table is a spreadsheet tool that gathers cross tabulation data and allows the user to select exclusively desired information from the columns and rows such as individual products or a sales date. Not only will a pivot table highlight desired information, but it can easily “pivot” variables around its axis to gain a different perspective on the data you are working with. The spreadsheet is now in the power of your hands.

The pivot table you will be working with will look and function similarly to the spreadsheet template, but there are many more functions behind those columns and rows. Once data has been populated in the pivot table spreadsheet, it is time to run the data and view the numbers. Sums, averages, standard deviations, and counts are just a few functions the pivot table can produce instantly! Pivot tables make the data seem easy to understand and they can be used within any level of data mining: nominal, ordinal, interval or ratio. The pivot table will become a good friend in helping prepare for reports since its functionality in a spreadsheet allots for more research time, and perhaps, resting those weary eyes and tired minds.

Essentially, a pivot table is a cross-tabulation report – an analysis type that survey research analysts have long known the benefits of. The few differences between the pivot table and a cross-tabulation report are seen in the pivot table’s deficiency of research power. The pivot table establishes an interdependent relationship between the two tables of values but does not identify casual relationships. Also, the pivot table’s power is indicative to the survey size. For example, a 20 question survey with a sample population of 100 can be effectively analyzed with pivot tables within a spreadsheet but when sample population and survey size increases over time or between markets then a more powerful, specialized survey analysis tool is required.

Which Data Mining Application to use?

It seems that no matter what industry it is, everyone is trying to find the best means to analyze and draw actionable conclusions from their data. Data mining has grown to be the cornerstone of modern-day business operations. By clinical definition, data mining is defined as “the process of extracting patterns from data.” This definition is far from precise, but it does a great job of illustrating how tough the analysis process can be. In many cases, it is labor intense and time consuming, as if you were actually using a pick and axe.

So which data mining application should you choose? The most powerful? The most user-friendly interface? The most extensive features?
The best data mining application is the application that best fits both your analytical needs and the skill set of your employees. Analytical needs can vary based on the level of analysis you desire and the type of data being utilized. For example, the needs of analysts working with social media data, financial reporting, and survey research data are all different. Applications may provide differences in analysis features (statistics, verbatim reporting, etc.), file size limits, and source data flexibility.

Simultaneously, your employees need to be able to use the application effectively. One of the chief concerns in implementing any new system in the workplace is the amount of time required to adopt and productively use the software. Systems with user-friendly interfaces an

d intuitive functionality can ease the adoption process, while complicated programs may inhibit worker productivity, and in some cases, result in poor or erroneous analysis. And, as employees turn to telecommuting and team-based work, applications that can incorporate cloud co
In short, a multitude of software packages are available for data mining. Each have their own strengths and weaknesses, but all that really matters is which package best suits your objectives. And in an environment where speed, accuracy, and insights are key to success, why not let the software do the heavy lifting?mputing or collaborative capabilities have an advantage going forward.