5 ways data analysis can help your business

Data analysis is the analysis of raw data in an effort to extract useful information that can lead to better decision-making in your business. In a way, it is the process of connecting the dots between different apparently disparate data sets. Along with its cousin Big Data, it has recently become a buzzword, especially in the world of marketing. While it promises great things, for most small businesses it can often remain somewhat mystical and misunderstood.

While big data is something that may not be relevant to most small businesses (due to its size and limited resources), there is no reason why the principles of good DA cannot be implemented in a smaller business. Here are 5 ways your business can benefit from data analysis.

1 - Data analysis and customer behavior

Small businesses may believe that the intimacy and personalization that their small size allows them to bring to their customer relationships cannot be replicated by large companies, and that this somehow provides a point of competitive differentiation. However, what we are beginning to see is that those larger corporations can replicate some of those characteristics in their customer relationships, by using data analysis techniques to artificially create a sense of intimacy and personalization.

In fact, most of the focus of data analysis tends to be on customer behavior. What patterns do your customers show and how can that knowledge help you sell more or more to them? Anyone who has tried advertising on Facebook will have seen an example of this process in action, as they can target their advertising to a specific user segment, as defined by the data Facebook has captured on them: geographic and demographic areas. of interest, online behaviors, etc.

For most retail companies, point of sale data will be critical to their data analysis exercises. A simple example might be to identify buyer categories (perhaps defined by store frequency and average store spending) and identify other characteristics associated with those categories: age, day or time of store, suburb, type of store method. payment etc. This data type can lead to better targeted marketing strategies that can better target the right buyers with the right messages.

2 - Know where to draw the line

Just because you can better target your customers through data Retail analytics doesn't mean you always have to. Sometimes ethical, practical, or reputational concerns may cause you to reconsider acting on the information you have discovered. For example, US-based retailer Gilt Groupe took the data analysis process perhaps too far, sending emails to its "we are your size" members. The campaign turned out to be counterproductive, as the company received complaints from customers for whom the idea that their body size was recorded in a database somewhere was an invasion of their privacy. Not only this, but many had grown in size during their membership period, and did not appreciate being reminded!

A better example of how to use information well was when Gilt adjusted the frequency of emails to its members based on their age and engagement categories, in a compromise between trying to increase sales by increasing messages and minimize rates. low.