- Survey Insight Library
- Artificial Intelligence for Market Research
- Machine Learning for Market Research
Next generation Market Research
If you are like us, who used to work in large corporations in Market Research departments, chances are half your day job is fishing for information through emails from the past trying to piece together information to respond to colleagues/your boss’s questions. Whether it is a survey we executed last year or an insight that triggered a new survey project, knowledge is scattered across the organization and it is very hard to keep track of rationale on decision making.
In most organizations, information is exchanged via emails and it is almost impossible to tie them together with specific surveys in an easy way. More often than not, only the researcher who was assigned the particular survey is the only person who can give us the answer we want recalling from his/her memory.
So you would agree with me that there is a need for adopting newer survey management techniques within large enterprises to harness the power of collaboration and synergies between teams and projects. This is why we built extremely easy to use document and comment features within Pinecrow (hyperlink to feature page), to keep track of information as a survey project is conceived and planned, well before the actual execution. This not only avoids back and forth emails, but it also inculcates a habit of adding relevant information among your team members that leads to better collaboration.
The added advantage of this feature is that this trove of growing information can be used to our advantage, for example to launch new projects much more effectively through a Machine learning approach. Think for example, you want to look up the most relevant “Customer satisfaction” information from your past surveys. While it is important to find survey questions that might have targeted this keyword, wouldn’t it be valuable to also find the project information on why/how those surveys were designed to probe customer satisfaction? How about anticipating the next best question to ask using Natural Language processing? Do you agree that this is going to save by not looking a multitude of charts and cross tabs?