How Data & AI Transform Everyday Choices | Dr Anshu Jalora | TEDxDTSS College of Law

Key Concepts

• Dynamic Pricing: A pricing strategy where prices are adjusted in real-time based on demand, supply, and other market factors.
• Price Elasticity: The measure of how sensitive the demand for a product or service is to changes in its price.
• Agentic AI: Artificial intelligence systems that can act autonomously and learn from their environment and interactions.
• Reinforcement Learning: A type of machine learning where an agent learns to make decisions by taking actions in an environment to maximize a reward.
• Congestion Pricing: A form of dynamic pricing applied to roads or public transport to manage traffic flow and reduce congestion.

Dynamic Pricing: Beyond the Myth of Corporate Greed

This talk aims to demystify dynamic pricing, challenging the common perception that it's solely a tool for companies to extract more money from consumers. Instead, the speaker argues that dynamic pricing, when implemented strategically, can lead to a more efficient allocation of resources, positively impact human behavior, and ultimately make the world a better place.

The Misconception of Dynamic Pricing

The common association with dynamic pricing involves scenarios like surge pricing for ride-sharing services (Uber, Ola) or increased flight ticket costs. This leads to the belief that companies are simply exploiting high demand and limited inventory to maximize profits. While balancing demand and supply is a component, the speaker emphasizes that dynamic pricing's true power lies in its ability to influence behavior.

The Transformation of the Taxi Industry

Pre-Ola/Uber Era:

• Taxi services were digitized (online booking, phone calls) but operated on a fixed pricing model.
• Primary users were for specific, high-value use cases like airport travel, meetings, and intercity journeys.
• Daily commuters (office-goers, college students) largely relied on personal vehicles or public transport due to the high cost of taxis. The speaker notes personal experience of not using taxis during four years of engineering due to expense.
• Taxi utilization was low, with drivers often idle during off-peak hours.

The Ola/Uber Revolution:

• These companies analyzed the travel market and identified underutilized taxi services.
• Strategy:
  • Lowering prices during off-peak hours: Made taxis more attractive for a wider audience, including daily commuters.
  • Increasing prices during peak hours (dynamic pricing): Leveraged the willingness of office-goers to pay more to avoid traffic congestion and parking hassles.
• Impact:
  • Shift in Consumption Patterns: Daily commuter usage of taxi services surged from less than 5% to over 50%.
  • Changed Ownership Models: Increased convenience of taxi services led some to opt out of car ownership.
  • Win-Win Scenario:
    • Taxi Drivers: Earnings increased significantly, from approximately 500-600 rupees per day to around 3,000 rupees.
    • Taxi Riders: Average price per kilometer decreased compared to the pre-dynamic pricing era.
    • Government: Increased tax revenue due to the larger overall market size and higher earnings across the ecosystem.

Beyond Simple Price Elasticity: The Need for Agentic AI

The speaker critiques the traditional approach of relying solely on price elasticity for pricing decisions.

• Limitations of Price Elasticity:
  • It's an old concept.
  • It typically considers a single customer base, whereas modern businesses interact with multiple customer segments simultaneously.
  • A single price elasticity number is insufficient for complex pricing strategies.
• The Modern Approach: Agentic AI and Reinforcement Learning:
  • Companies need to move beyond static pricing and static elasticity calculations.
  • Agentic AI systems are crucial for:
    • Continuously monitoring customer responses to prices.
    • Identifying which customer segments are responding favorably or unfavorably.
    • Determining which pricing strategies are effective and which are not.
  • This is achieved through Reinforcement Learning, where the system learns and adapts its pricing mechanisms autonomously based on real-time data and stakeholder behavior. This creates a "self-learning pricing mechanism."

Uncovering Hidden Gems with Data-Driven Dynamic Pricing

The application of agentic AI in dynamic pricing can reveal unexpected insights and opportunities.

• Example: Bus vs. Flight Pricing in India:
  • Agentic AI and reinforcement learning identified specific customer segments who prioritize travel time during limited windows.
  • These customers were willing to pay a premium for bus services, even exceeding what they would pay for flights, due to their specific needs and preferences. This highlights how dynamic pricing can uncover niche markets and willingness to pay that traditional analysis might miss.

The Future Impact of Dynamic Pricing

Dynamic pricing is poised to influence various aspects of our lives:

• Traffic Congestion Reduction:
  • Congestion Pricing: Already being implemented in some cities, this involves dynamic pricing for road usage to manage traffic flow.
• Meeting Aspirations and Enhancing Access:
  • Dynamic pricing can make high-end services, like flights and luxury accommodations, accessible to a broader audience at certain times.
  • This allows individuals and families to fulfill aspirations for vacations and experiences that might be unaffordable at regular prices.
• Personalized Experiences:
  • The speaker draws a parallel to personalized discounts and giftings, suggesting dynamic pricing will contribute to more tailored offerings.

Conclusion: A New Perspective on Price Tags

The speaker concludes by urging the audience to shift their perspective on price tags. Instead of simply judging whether a price is "right" or "wrong," they should consider what the price is communicating about value, demand, and opportunity. Dynamic pricing, when viewed through this lens, is not a predatory practice but a sophisticated tool for optimizing markets and improving outcomes for all stakeholders.