Using Crowdsourcing and Image Processing to Automate Data Collection
To measure the consumer packaged goods retail trade, marketing research companies such as Nielsen typically collect data directly from retailers who provide electronic point-of-sale (POS) information from their check-out scanning systems. This is by far the most accurate data available, but its collection is dependent upon retailer cooperation. If some of the retailers in the sample design do not cooperate, there can be some level of bias in the reported data.
We can eliminate that retailer cooperation dependency by collecting data directly from individuals in population-projectable consumer panels, but the size of panels in certain regions of the world makes it difficult sometimes to report data at the granularity clients need to track performance. Increasing sample size is a solution, but it’s not always possible due to the costs of managed panels and the difficulty of recruiting reliable panelists.
With the growing worldwide adoption of new technologies like mobile smartphones, crowdsourcing, and virtual payments, new opportunities now exist to collect purchase information directly from consumers, in large numbers, and do so in a way that is both economical and less burdensome. This paper describes a very promising proof-of-concept initiative in the U.K. that is paving the way toward automation.