Retail Reinvented: Harnessing the Power of AI and ML in Promotions
In today’s cut-throat retail landscape, promotions, which account for 10 percent to 45 percent of total revenue, are a double-edged sword. On the one hand, they can potentially boost sales and significantly improve brand visibility. On the other hand, poorly executed promotions can lead to decreased margins, customer frustration, and even brand dilution.
Traditional retail promotions, such as discounts, BOGO (buy one, get one), and seasonal sales, have long been the go-to strategies for attracting customers and increasing sales. However, the effectiveness of these promotions is often gauged through conventional metrics like foot traffic and sales volume, which provide only a limited understanding of the true impact.
Enter artificial intelligence and machine learning. The rise of AI and ML in retail is proving to be transformative, revolutionizing how retailers approach many aspects of the business, including promotions. Given the competitive nature of the industry, AI and ML are quickly moving from a “nice to have” to a necessity.
Moving beyond basic sales data, AI and ML allow the business to dive deeper into a promotion’s secondary effects — information that's often forgotten but invaluable for optimizing effectiveness. By evaluating the baseline forecast (sales without promotions), halo effect (complementary purchases), cannibalization (reduced sales on other, similar products), and pull-forward effect (pantry loading from the future), retailers can better adapt promotions to improve profitability. Harnessing the data is key.
AI/ML: Transforming the Future of Promotions
With the ability to make or break a business, promotion effectiveness using AI/ML enables retailers to make data-driven decisions and maximize return on investment. In fact, retailers using AI/ML-based promotion effectiveness and planning approaches report a 5 percent to 8 percent increase in profit margins and up to a 20 percent increase in sales lift on promotion volume. Furthermore, analyzing promotions in-depth and planning future promotions based on AI/ML findings have delivered a ROI of 200 percent to 300 percent.
Using AI/ML for promotions can help retailers optimize:
- Anticipating customer behavior: Retailers can build models that forecast future purchasing patterns based on historical sales data, customer interactions, and external factors such as weather and holidays. These models can answer questions such as: What products will likely be in high demand during a specific promotion period? Which customer segments are more likely to respond positively to certain offers?
- Effectively allocating resources: Planning, executing and monitoring promotion campaigns can be resource-intensive. AI and ML can streamline this process by analyzing historical campaign data to identify the most effective channels, timing and messaging strategies.
- Human expertise: While AI and ML offer powerful tools for enhancing retail promotion effectiveness, they're most impactful when combined with human expertise. Domain knowledge, creativity and understanding of market dynamics are essential for crafting successful campaigns. AI can provide data-driven insights and automate repetitive tasks, freeing retail professionals to focus on strategic decision-making and creative thinking.
- Adaptation: ML models learn from new data over time, continuously improving their accuracy and effectiveness. This iterative learning process ensures that promotion strategies evolve with changing market dynamics.
Conclusion
The retail landscape is evolving, and AI and ML have ushered in a new era of promotion effectiveness. From predictive analytics to personalized offers, these technologies empower retailers to make data-driven decisions, optimize campaigns, and drive customer engagement. By harnessing the power of AI and ML, retailers can navigate the complexities of the modern market and achieve promotion success like never before while also providing personalized, delightful experiences for their customers.
Majaz Mohammed is vice president, merchandising and supply chain at Tredence, a provider of AI and data analytics services.
Ankit Tyagi is senior manager, pricing and merchandising at Tredence.
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Majaz Mohammed, Vice President, Merchandising and Supply Chain, Tredence
Majaz is an experienced supply chain professional with 15-plus years of experience in helping clients with solving supply chain and operations issues. He is passionate about the intersection of supply chain, analytics and AI/ML technology and is on a mission to infuse AI/ML into supply chains to modernize them and improve customer as well as employee experience while making an impact to top and bottom line. His experience spans across multiple industries - Retail, CPG, Chemicals, Agro-Chemicals, Mining, Automotive and more.

Ankit Tyagi, Senior Manager, Pricing and Merchandising, Tredence
Ankit is a seasoned subject matter expert specializing in pricing, promotion, and assortment strategies. Leveraging his expertise, he harnesses the power of machine learning to tackle complex challenges in these domains, helping businesses optimize their strategy, enhance profitability, and make data-driven decisions. His unique blend of domain knowledge and advanced technology empowers him to deliver valuable insights and drive success in the world of retail and pricing optimization.