A World Where Data is not Wasted
Looking into the crystal ball to try and find the answers to tomorrow's questions. This is the challenge for market researchers, and at times it can feel like an impossible one. There is no unobstructed window into the future, but there are ways to make a well-informed prediction.
Predictive Analytics is a branch of data processing that makes this task possible. By using what you know to develop an idea of what you don't, data scientists can forecast emerging trends and behaviors, making future business decisions that much more informed. In this method, machine learning techniques are used to identify patterns in existing data sets using pre-determined variables, where more focused, quality data means more accurate results.
Building a Complete Picture
Segmentation is a key feature of market research, and ultimately one of the first steps in preparing data for predictive analysis. After developing a clear business objective, the next step in data collection is to build a complete profile of panelists covering geographic, demographic, psychographic, and behavioral information. In the traditional sense, this means asking a lot of questions. Advances in mobile market research, however, mean that efficiency doesn't need to be sacrificed to build a complete picture. Designing questionnaires with the ultimate goal of implementing predictive analysis can be both comprehensive and streamlined to ensure that no data goes to waste during the research process.
Extending the Usability of Information allows researchers to build a respondent profile without asking redundant questions that often lead to survey fatigue. Just as data processors make inferences from existing data, effective surveys consider previous responses when guiding respondents through the research journey. In short, connectivity should always be a priority. New answers should always lead to new questions, and questionnaires should be customized to each respondent to reflect this. The goal in survey design is to gather a wide range of detail while providing a unique experience for each respondent, and ultimately, bringing the research to a precise conclusion.
Survey Automation can also be implemented to build context around a respondent through the use of geo-fenced activities, customized notifications, and activity monitoring. Knowing when, where, and how often a panelist sends feedback can speak volumes about the story behind the data. Automation features support information usability by reducing the workload of both the survey designer and the survey respondent.
Creating Big Data sets doesn't have to mean creating endless questionnaires. Predictive analytics considerations should be made when planning market research projects, in order to capture data that compliments the historical structured and real-time data used in making predictive models. Carefully considering the target data and all of its potential applications gives greater flexibility to data processing teams, while also allowing researchers to design more intuitive, lean questionnaires. Adopting an analytics mindset early in the research process makes it all the more possible to gain insight into the questions you'll be asking tomorrow.