5 Ways AI Helps Retailers Get Ready for the Post-Pandemic Economy

As vaccine distribution moves us closer to a post-pandemic life, businesses are still navigating the economic uncertainty. Retail leaders understand that consumer buying patterns have changed in ways that are likely to persist once the virus is under control. Retailers know they’ll need to adapt to stay a step ahead of competitors and increase profits in the months and years to come.

Forward-thinking company leaders see this time as an opportunity to invest in digital transformation now so they can optimize operations going forward. Leaders are looking for ways to implement better revenue growth management practices that leverage data and advanced technology like artificial intelligence (AI) and machine learning so they can get ahead — and stay ahead — in the future.

With advanced technology that allows them to integrate data sources and create digital twin frameworks to simulate and predict consumer behavior, retail leaders can scale profitably across brands, categories, countries and business units. Those that fully embrace advanced technology can improve margins and manage growth with AI-generated recommendations to drive five vital retail functions:

1. Pricing: One significant pain point for retailers is the inability to visualize pricing trends across categories and channels. Pricing for some items has been based on seasonal factors, but with advanced analytics, retailers can get a high-level view of trends, hypertarget customers using real-time data, and localize pricing to maximize profits.
2. Promotions: AI-generated recommendations let retailers drive category expansion, improve forecasting, and optimize market share. For example, digital twin simulations can predict consumer behavior and help retailers more precisely target customers, eliminating ineffective promotions and maximizing incremental sales while improving margins.
3. Assortments: Retailers need a better way to make assortment decisions at regional and store levels, and AI can provide SKU recommendations that free retailers from old-school calendar-centric resets. With advanced analytics, retailers can spot opportunities and execute demand-based assortment strategies, responding quickly to customer-driven trends.
4. Spending: Intelligent spending optimization models can help retailers improve bottom-line results while reducing waste and increasing investment in profitable activities. AI can provide recommendations that allow retailers to align spending with shopper behavior while identifying ways to cut costs, including automation of required activities for greater efficiency.
5. Trade promotion: Retailers can adopt an AI-driven trade promotion intelligence (TPI) strategy that is faster, smarter and more comprehensive than the standard trade promotion optimization (TPO) approach. With advanced analytics, retailers can receive recommendations that consider a broader range of real-time consumer data, and that lets them develop more accurate forecasts and create effective optimization plans.

These five key retail functions demonstrate how adopting AI can address major pain points, improve performance across retail operations, and transform the business. However, AI’s potential isn’t confined to these areas alone. With digital transformation that fully leverages available data and technology and applies it to all relevant use cases, retailers can solve a range of business challenges, including staffing optimization based on recommendations that take store-level data like foot traffic into account.

With AI-driven recommendations and machine learning features that ensure analytics get smarter over time, retailers can understand new buyer journeys across digital assets and brick-and-mortar operations. These new capabilities will enable the retailers that embrace them to not only navigate uncertainty during and after the pandemic, but to gain a long-term competitive advantage.

Dr. Anil Kaul is the co-founder and CEO of Absolutdata, a company specializing in big data analytics, marketing analytics and customer analytics.