As you well know, marketing your product or service to existing and potential customers is the lifeblood of your success as a business. In the past, before technology took over, businesses had a telephone and personal home address with which to contact their customers. Now, there is email, messaging, Skype, websites, and social media. However, some companies prefer to have one-on-one contact with their customers. Continue reading
Running a successful business usually means a lot of information being fed to every level of the company, and with so many regulations that you have to contend with these days, it could spell trouble. Here are some ways to ease the pressure of too much data. Continue reading
Business Intelligence is the practice of collecting and analyzing mountains of data to determine current business success, opportunities within the current market, and forecast potential opportunities and options that will benefit the business over the long term.
Collecting data for business intelligence purposes can be time consuming and expensive; however, there are important rules for getting business intelligence that allow you to reduce costs and obtain the most relevant data. Continue reading
Information is the prime asset of enterprise, the departure point for all decisions leading to product innovation, marketing strategy, and ongoing competitive advantage. IT generates an abundance of business information; the problem is selecting data most useful to your enterprise purposes and using it to devise suitable long-term objectives. Data driven decision-making is based on big data analytics, which particularizes and coordinates enterprise-derived information towards specific organizational objectives. Analyzing large data-sets for assessment of strategic performance is the current best practice technique of enterprise decision-making. Continue reading
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
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).
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.
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.
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.
Mobile Qualitative Research or MQR, as it is known, is growing in popularity as a go-to market research tool because it’s finally realizing the promise of providing feedback while the customer is using the product. MQR creates feedback in the real world and with the introduction of mobile software analysis tools, researchers can receive and analyze results in real time.
The process is simple and straight forward. After recruiting participants, questions are sent to their mobile device over a period of days or weeks, allowing the participants to react and respond via that same device.
A researcher can assemble and view the results with an online dashboard. They can input new questions to reengage any of the participants in real time, delving more deeply into a particular subject. Imagine doing that with traditional or online surveys.
The primary advantage of Mobile Qualitative Research is the immediacy of feedback. Since participants (and most of us) carry our phones during the day, when at work, out shopping or at a concert or sporting event, participants can text or send photos of their experience at the point of sale, or during the process of entering a crowded stadium.
This immediacy means that the information provided is more accurate because the respondents do not have to recall what they experienced at a later time when they are finally sitting down to go online.
There is no learning curve for MQR participants since they are simply texting or sending photos on mobile devices that they all ready own and are familiar with. This ease of use helps increase the participation rate because of its simplicity and familiarity.
Higher engagement or non-static engagement with the participants is a big plus. Since the information is submitted in real time, a live researcher will know that the data is input at the time the researched event is occurring.
Mobile Qualitative Research = Access
According to the Nielsen group over 265 million Americans have mobile phones, 98% of which are text capable. By the end of 2010, according to the International Telecommunication Union, approximately 5 billion people worldwide had cell phones.
Mobile Qualitative Research allows researchers access to those people that either don’t have time to sit down at a computer or don’t have access to one. Also, using MQR participants don’t have to be herded into a study room or assembled into panels, saving significant costs and time.
The evolution of mobile devices as a business tool has reached a point where they are powerful enough and ubiquitous enough to change how businesses make decisions. Smartphones and tablets have made, time sensitive business data analysis available to executives and decisions makers with a speed, depth and access that was previously unheard of. Now, it is not a matter of waiting for a report, it is simply a matter of swiping fingers across a screen.
In the marketing industry, mobile devices have been used for years to acquire product or consumer data in the locations where the products are used or the consumers congregate. The acquired data is usually uploaded to a data warehouse and managed by an ERP system. Analysts would then clean the data, drill down to the relevant information (based on the questions they are seeking answers for), create a report, summarize it and present it to an executive. The executive uses the analysis to make a decision and then has the analyst or a clerk input the decision into the ERP or database.
However, using the newest generation of mobile business analysis tools, the decision maker can pull up the data, drill down to the information required, make a decision and instantly input that decision into the ERP. They no longer need to be in a board room or even at a desktop computer to obtain the analysis they require.
The speed at which the data and the analysis is available to the decision maker allows them to react to current and real world situations in which the data is critical to making a decision. The cost of the traditional work flow, and the loss of time waiting on the creation of these reports is eliminated.
Will Analysts Have a Place in the Future?
The evolution toward just-in-time delivery of business data analysis does not spell the death knell for professional analysts. Analysts of the future will still be responsible for setting the parameters of the business data analysis. They will still determine how to provide the data in the clearest possible terms so that the executive, on his or her mobile device, can understand and act on what the data is telling them.
Executives and decision makers will still need to rely on the expertise and experience of the analyst to tell them what the analysis means.
Survey data collection is just as important, if not more so, than the reports generated by that data.
Most market researchers, however, are more concerned about what the results of a given survey are saying rather than the data collection tools used to provide those results. Especially with all of the new web-based, do-it-yourself (DIY) data gathering tools popping up recently, there are many different options one can employ when conducting research. I won’t name any of them here because, while they allow for quick and dirty methods of obtaining data, they don’t do a very good job of trending and reporting.
Let’s first look at the quick and dirty aspect. Using DIY tools allow the researcher to quickly develop and deploy a survey on the internet, but without careful planning and long-range thinking, this may be the beginning of a giant mess. These tools do not restrict or recommend how to setup the underlying single file database. There are no established naming conventions for variables, nor are there any data validation methodologies employed by the software. I’m not trying to say that these tools should have these things built-in, because I think it would be too restrictive and lessen their appeal. Instead, this aspect is not being given enough respect by those responsible for setting up the data collection.
While this strategy works for small one-off surveys, it becomes more problematic when the survey is longer, more complex or is conducted multiple times per year (waves) or multiple years in succession.
If your survey involves more than one data gathering effort, then you will want to make sure that the variable naming and definition remain constant across those efforts. Also, you will want to make sure that the data integrity is intact, or that some validation/verification is being performed to make sure you don’t get invalid data. This can either be done on the front-end (during the data gathering effort) or on the back-end (through scripting or some other verification method).
These aspects of survey reporting are something to consider when employing your next data collection effort for market research. Though DIY survey creation is an exciting step for marketers there are still restrictions on using them for large, trending data sets. Whether or not you choose to use DIY tools or something more robust, planning out the data structure and integrity is critical to gaining key consumer insights.
If you’d like to learn more about how to prepare survey data for analysis, please download our whitepaper Ten Essential Prerequisites for Survey Data Analysis.