Big Data Market Research and Strategy
What is Big Data?
The term “Big Data” refers to the process of identifying, gathering, analyzing and interpreting very large amounts of data to enable more meaningful and actionable decisions. While there may in fact be more data created every second, it is the ability to analyze it faster and in more ways that is gaining attention. Expanding “cloud” and computer storage capacity have also contributed to its recent popularity.
- Much as Moore’s Law applies to computer chip capacity, it is now possible to store, manage and more quickly process extremely large volumes of data due to technological improvements, while doing so at greatly reduced costs.
- As such, many organizations have integrated traditional market research skills with IT programming talent to analyze extremely large data sets in an effort to uncover patterns, trends, and correlations relating to human behaviors and interactions.
- Due to the widespread use of social media such as FaceBook, twitter, and LinkedIn, along with video sharing tools like Youtube, Instagram and Pinterest, the volume of textual and visual information is growing exponentially on a global basis. Many countries have these same websites along with their own versions plus many mobile device apps.
- Much of Big Data is “unstructured” in nature, in a sense like qualitative data as found in responses to open-ended questions or focus groups.On the other hand, data obtained from sources such as web traffic visits, clicks, and financial transactions is numeric and “structured”. By being quantifiable, such data is easier to analyze.
- With increasingly more consumer and business demographics, opinions, preferences, and behaviors being collected, it is possible to construct more meaningful pictures and conclusions out of disparate bits of data using sophisticated software programs and statistical tools.
How do you obtain Big Data?
- Data about customers can be obtained from many sources. Examples include their web logs (i.e. of website activities), customer service interactions, subscription and registration forms, surveys, blogs, and social media mentions.
- Thus, by integrating an email address from one place, some demographics from another, along with geographic location, job title and function, family size and many other items from diverse sources, you may be able to compile a relatively detailed profile of an individual.
- Is your company or brand name being discussed? What words or terms tend to be associated with them? Do they have positive or negative connotation? What about the same for your competition? If you can get such “unstructured” data and quantify it you can add this to your database.
What can be done with Big Data?
- Once you have a better profile of your customer, it can be combined withother factors such asfrequency and quantity of purchases, pricing variations, advertising content and media placement, time of day or week, regional location and more.
- A relationship may be uncovered that ties things together and provides insight into not only the “what” but the “why” of an event. For example, you may learn not only how many times someone visited your website, clicked on ad or made a purchase, but also discover why one person liked something while another did not.
- With this knowledge, it is then possible to improve the probability that a particular audience will be exposed to and read targeted content (e.g. advertising messages), and act in a more predictable and desired way.
Is Big Data for You?
- If you can pose a question like this that relates to a business objective where the answer would help you make a better decision, then Big Data could be for you. So for instance, are you trying to attract new customers, sell more to existing ones, find ways to reduce costs, improve customer service?
- It is much better to have objectives or hypotheses to test before delving into Big Data. Although an examination of many variables may eventually find some that correlate, e.g. one’s height and their choice of credit card, there maybe little or no value to knowing this.Thus, without a plan, one can easily expend (and waste) huge resources.
How should you use Big Data?
The goal is to “make sense out of nonsense” as well as to avoid “analysis paralysis” (in which so much time is spent looking at data that decision making is delayed).
More important than having the data is determining what to look for.Traditional statistical analysis can help to identify which variables are most likely to be associated with (and cause) certain outcomes.
- So by uncovering and focusing on keypast and current behaviors of customers, it may be possible to target and tailor more appropriate and meaningful messages or advertisements to them that will affect future actions such as purchasing or recommending your products.
- This exercise often requires extreme computing power and software programs, plus people trained in their use, to draw proper conclusions from immense data sets. Thus, when the need arises, consulting with a third party that specializes in working with such data is a good idea.
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