How Retail Shifted from Business Intelligence to Data Science Posted on : Apr 17 - 2018

Understanding the evolution from BI to Data Science is helping large retailers aggressively price their products.

Technological change happens fast and usually just as we have gotten used to one working method, another one is just around the corner getting ready to take over. In this case, we’re talking about the shift from business intelligence to data science and how it has affected the retail industry.

Business intelligence is still a mighty advantage for any retail business to have when seeking to grow, stabilize and remove risks. It is also becoming cheaper to develop with more affordable technology becoming accessible and more people available with the skills to handle it. However, in comparison to data science, its benefits are limited. While business intelligence is useful at showing you what you are already aware of, such as where profits were lost and what risks need to be reduced, data science can flag up ‘invisible’ problems you had no idea about and provide strategies for growth that were hiding in plain sight. Data science companies allow retail businesses to look forward and predict future trends, while business intelligence consultants can only analyze data of past events.

This is incredibly important for those who work in retail, an industry that changes shape very frequently, and where customer satisfaction is highly regarded.

Comparing the shift between the two is a bit like comparing the shift between VHS and DVD roughly 20 years ago. The latter succeeded the former, and we may possibly see the same thing again in the next few years with future methods of data analysis.

Here are four examples of the shift from business intelligence to data science in retail:

Find Your Perfect Customer – Timberland

Timberland is one of the finest examples of a company breathing new life into itself with the help of data science. A few years ago, Timberland, a now very well-known retailer of outdoor clothing and footwear, was on death’s door until it was acquired by the VF Corporation. From that point onwards, Timberland became a dramatically more data-driven company.

It moved away from business intelligence approaches and started to actively seek a new target customer with the help of data science – looking forward towards new data and not backwards at past mistakes. It did this by conducting a large-scale customer study in eight countries over the course of two years. Timberland found that its ideal customer was ‘urban residents with a casual interest in outdoor activities’ and with a new and clear direction to follow, managed to increase its profits significantly, not only saving it from collapse but transforming it into a trendsetting retailer. View More