Retail Technology Future is Here: Are Your Systems Ready?

a close up of a blur

The pandemic restrictions relief has led to another creative boom in retail. Retailers of any size and scale have turned their focus toward improving in-store experiences by combining the best of online and offline worlds: finding creative ways to leverage1st-party data, applying Machine Learning, Artificial Intelligence, and Augmented Realityall sorts of technology advancement to wow and engage yearning buyers.

And while one may think that the main revolution is taking place amidst the shopping floors, there's even more brewing behind the scenes. An average retailer's software portfolio has become significantly broader: Enterprise Resource Planning systems, Point of Sale software, advanced payment processing, and price optimization solutions.

Besides implementing advanced systems, retailers strive to make their stores the place of an optimal buy. However, amidst the ever-changing retail market and saturating competition, it does not always mean offering the lowest price that usually can turn the bottom-line business metrics into a complete disaster. In contrast, an optimal price maximizes the total profit of the seller from an optimal product for a specific need. Achieving such a mix often becomes possible with the implementation of advanced pricing and optimization technology.

Besides growing business bottom-line metrics, price optimization and automation solutions dramatically improve teams' efficiency by saving time, labor and minimizing the chances for human error. Yet, despite the apparent advantages and retailers' enthusiasm about them, widespread adoption is only looming on the horizon. Often enough, retailers' systems and spatial product databases are not ready for integration with advanced solutions.

So how should retailers get ready to join the unrolling revolution? The following steps are turnkey for effective preparation and smooth technology adoption:

First and foremost, retailers should develop and design a comprehensive product category tree in a way that precisely reflects each product's characteristics and make it easily searchable online or in-store. Traditionally, a retailer should develop and establish a standardized system of product categories and attributes, e.g., brand, packaging, flavor, age group, for further easy segmentation and product category management. A well-structured product category tree, data completeness, and availability are good signs of proper product management within an organization.

Specify category roles. The main category tree should be divided into subcategories. When done right, it makes it super easy to define category roles: routine (the category that is most associated with a retailer's brand), seasonal (the product category that buyers usually come for during a particular season), impulse (represented by the products that are typically bought impulsively and are mostly displayed at the checkout area), traffic boosters and others. Here, one should monitor market trends and study buyers' perceptions for a pinpoint definition of a category's role in the portfolio.

Regularly check category health. As with any other dynamic element of a retail business, categories require regular health check-ups. They provide a retailer with a clear understanding of the product group's validity to the current tendencies on the market, profit-generating potential, ability to satisfy consumer demand, and overall positive growth potential. The analysis can also reveal the weak spots within a category that require an immediate response from a retailer. As an example, it may be an adjustment in product placement or a change of suppliers.

Set success metrics. The category analysis and goals definition are inseparable from success indicators. According to the retailer's specifics and business goals, arrangements with the suppliers, and the established cooperation terms, the success metrics should be consistent on every business level (e.g., revenue, sales in pieces, gross profit, profit in %, market share, number of customers, inventory, customer retention).

Develop a category strategy. Consequently, this stage implies a thorough development of a roadmap that would include all the steps necessary to achieve the category goals. The focus areas may vary greatly: assortment matrix review, turnover boost, enhancing marketing communication, optimizing supply chains. Offline retailers could also find it highly effective to rearrange product shelves and go creative with in-store equipment (e.g., interactive stands, colorful stalls to better display products and grab the attention of buyers idly strolling around the shopping floors).

Develop category tactics. Having defined the category tree, goals, and success metrics for each product group, retailers can now decide on further investment into an advanced solution for price, promo, merchandising, supply, and assortment management. Not limited to successful management capabilities, retail technology can also help with developing assortment tactics and mechanics.

Shape an implementation plan. Unlike all the activities mentioned above, this stage brings retailers closer to the implementation in practice. But before diving deep, the teams should discuss and approve the resources, timeline, and potential investment, including the fees associated with any process optimization software.

And finally the last but not least, agree on the frequency of metrics and success review, its format, goals achievement, and tactics adjustment.


The good news is that retail is getting increasingly tech-savvy and innovative. A full array of technology advancements has not only taken over shopping centers but made roots within retailers' organizations. However, as it becomes obvious that in-store innovations can not directly impact revenue and gross profit, retailers should take time and invest efforts into getting their systems ready for adoption of the next-gen retail automation and price optimization software.

About the Author

Yulia Beregova is a pricing solution architect with more than 10 years of practical experience in Marketing Research and Analytics at Competera.