Skip to main content

Is There Such a Thing as Too Much Data Collection?

Data collection in retail used to be limited by the data compiling and processing capacity of the business conducting the operation, whether it was a retailer, a sales and marketing company, or the merchandising department of a CPG manufacturer.

Some surveys, for instance, had to be limited to especially selected markets or even stores in order to get a sample of shoppers similar to the product's target audience so data transcription and processing wouldn't slow down critical go-to-market decisions.

Mobile data collection software has revolutionized this scenario. With mobile data collection software, field reps can perform surveys, product tastings, etc., at the point-of-sale on their own mobile devices, and the information collected will be instantly available for management to analyze and use as part of their decision-making process.

Therefore, by doing away with the limitations of data transcription and processing, mobile data collection software opens the door for better strategic decision-making based on a much broader, more representative of the target market, sample size.

VisitBasis mobile data collection software is an ideal solution for business conducting in-store activities such as surveys, product tastings, audits, planogram checks, among others, that require customized forms along with real-time access and analysis of the data being collected.

Easy to use and implement, VisitBasis streamlines data collection for retail businesses of all sizes and is fully cross-platform, working both on iOS and Android smartphones and tablets.

Want to see how VisitBasis mobile data collection software works? Sign up for a free trial or schedule an online demo!



Comments

Popular posts from this blog

Machine Learning and Image Recognition in Retail

References to machine learning seem to be everywhere these days. From new university-level courses to magazine articles and social media posts, daily we encounter new information on artificial intelligence and machine learning. But how can those new technologies be applied to improve retail operations? One of the main ways that machine learning can help retail is when it comes to quantifying data that previously came as individual pieces of information while surveying stores, such as images. Image recognition in retail is one of the best examples of the use of machine learning in merchandising applications.  Machine learning allows images to be fed by programmers into a mobile merchandising system in order to train a neural network to identify product faces on outlet shelves. Then, field staff can simply take pictures of the store shelves on their mobile merchandising apps and the system will be able to identify - with accuracy that reaches 95% - the number of faces of t...