WIMTACH’s Workshop Series: The Retail Analytics session assists participants in developing their python analysis skillset
The retail industry has emerged as the new dominant force in the world economy. In 2021, the retail industry generated over $674 billion in sales just in Canada (which will only increase in the coming years). However, as the retail industry expanded so quickly, it saturated the market, enabling only those technologically advanced to thrive. This unravelling caused many retailers, unwilling to adopt new technological solutions, to become unprofitable or even close their doors. Yet many succeeded and thrived, even during the Covid-19 pandemic. Sreekumar Pillai covered this and many other secrets of Retail Analytics using Python during his weekly workshop. The workshop was conducted to educate retail enthusiasts looking for new ways to help their businesses thrive and Information Technology specialists interested in learning Python secrets that enable retail analytics.
So, why did some retail businesses thrive during the Covid-19 pandemic while others had to close down? Sreekumar said that the main factors in the retail industry that help and harm companies on a regular basis are “their digital capabilities and scale.” As the scale of a firm changes organically, only the digital aspects of a firm can be altered. Yet some retail businesses still hold back, fearful of spending too much. Sreekumar has worked on multiple projects assisting Retail organizations in implementing retail analytics. One of those projects was a partnership with Alphacor, where the Centre for Explainable Data Analytics (CEDA), in collaboration with WIMTACH, developed a revolutionary application to enable all key stakeholders to access the current energy usage. Such analytics implementation enabled Alphacor “to be one step ahead of the curve by anticipating client’s needs and being up to date with technological advances in the industry so that this software can remain competitive.”
Although such an implementation of retail analytics is extremely effective, there are other, more classical types of retail analytics. Sreekumar said, “Smart merchandising is one of the most effective types of retail analytics.” As most purchases are emotional rather than logical, factors like attractive packaging and careful placement on shelves significantly increase the odds of a sale. Other common practices in retail analytics, like fraud detection and prevention, customer-driven marketing, and integrated forecasting, enable organizations to thrive by providing access to vital information. Moreover, when used together, such tactics create retail empires because the products sold by retailers are essentially the same, while analytics are different.
To provide further experience in creating retail analytics programs, Sreekumar spoke about how Python can be implemented into retail analytics. “Python is the key behind the retail analytics success,” he said. To provide a testing ground for the upcoming lab, Sreekumar introduced Google Collab, a free-to-use platform dedicated to running and testing python code for beginners and advanced programmers. With assistance from Sreekumar, all participants participated in the lab and were able to execute the assigned code sequences. Such training enabled many retail specialists to get their first experience in Python, enabling them to further advance their knowledge of this extremely versatile tool. Others, who were already accustomed to Python, had a chance to acquire new techniques and specialized skills, enabling further growth in retail analytics.
This workshop is scheduled to repeat every week to provide participants with more practice and feedback.
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