The New Retail Reality: Embracing AI-Driven Dynamic Pricing

In an economic landscape filled with challenges, dynamic pricing emerges as a vital strategy for retailers aiming to maintain and enhance their market presence. According to McKinsey, optimizing pricing decisions could significantly impact profits, indicating that a minor 1 percent price increase might lead to an 8.7 percent profit boost.

This modern approach to pricing, deeply rooted in the historical practices of barter and negotiation, is gaining attention as consumer behaviors evolve. The digital age has made pricing more transparent, urging retailers to adopt sophisticated algorithms for price optimization.

Overcoming Legacy System Limitations

Legacy solutions, however, aren't capable of effectively implementing dynamic pricing due to several limitations inherent in their design and architecture. The shift towards dynamic pricing is hindered by the limitations of legacy systems, which include:

1. Siloed Data Integration: The difficulty in amalgamating real-time data from diverse sources critically affects pricing adjustments.
2. Batch Processing: Legacy systems' reliance on periodic updates is incompatible with the need for instantaneous price adjustments.
3. Scalability Challenges: The computational demands of dynamic pricing can overwhelm the capacities of outdated systems.
4. Competition Benchmarking: Traditional systems fail to actively monitor competitors' pricing strategies.
5. Integration Difficulties: The inability to seamlessly integrate analytical tools for pricing decisions further complicates dynamic pricing efforts.

There are multiple other challenges, such as a lack of personalization, real-time insights, response time and integration issues, that do add pain to using legacy systems for dynamic pricing.

For example, one of the large grocery retailers relies on a legacy pricing system with fixed pricing for its products. In this scenario, the prices of items remain constant for extended periods or change only during traditional sale events. This legacy system would cost the retailer in the following areas: missed revenue opportunities, ineffective clearance sales, lack of personalization, inefficient inventory management, and competition disadvantage with a list of intangible losses that go unaccounted for.

These challenges can lead to missed opportunities and competitive disadvantages for retailers reliant on outdated systems.

AI/ML-Powered Dynamic Pricing Advantages

The adoption of artificial intelligence/machine learning technologies offers transformative benefits for dynamic pricing, including:

1. Real-Time Insights: Leveraging AI/ML allows for rapid analysis of vast datasets, enabling immediate pricing decisions.
2. Personalization: AI/ML facilitates the customization of prices individually, enhancing customer engagement.
3. Adaptability: These technologies swiftly respond to market changes, ensuring pricing strategies remain relevant.
4. Automated Decision Making: Reducing manual oversight increases efficiency and accuracy in pricing.
5. Experimentation: AI/ML supports testing various pricing strategies to determine the most effective approaches.
6. Comprehensive Optimization: Beyond revenue, AI/ML can optimize inventory levels and customer loyalty.

Implementing AI/ML improves financial outcomes and enhances operational efficiency and customer satisfaction.

AI/ML algorithms facilitate immediate business impact such as:

• improved topline (2 percent ~ 3 percent improvement);
• intact bottom line (5 percent ~ 7 percent improvement);
• markdown cadence (15 percent ~ 20 percent savings in capture rate);
• dynamic elasticity;
• reduced empty shelves and shrinkages (11 percent ~ 13 percent savings); and
• better inventory planning.

Practical Applications of AI/ML in Dynamic Pricing

AI/ML enhances dynamic pricing through improved forecast accuracy, enabling effective inventory management and strategic price adjustments based on competitive analysis. This approach ensures retailers can adapt to market demands and maintain competitive pricing.

Transformative Impact of AI/ML on Retail Pricing

Integrating AI and ML into pricing strategies equips retailers with the tools necessary for navigating the complexities of today’s market dynamics. This technology-driven approach ensures agile and data-informed pricing strategies, offering a significant competitive advantage. AI/ML-driven dynamic pricing is crucial for retailers striving to achieve optimal profitability and customer satisfaction in a fluctuating market landscape.

By embracing AI/ML for dynamic pricing, retailers can redefine their pricing strategies, achieving precision and adaptability once thought impossible. This is not just a strategic move — it's imperative for success in the contemporary retail environment, ensuring sustainability and growth in the digital era.

Majaz Mohammed is senior director, supply chain at Tredence. Ankit Tyagi is senior manager, supply chain at Tredence. Tredence is a data science company that offers industry-specific data analytics solutions.