Dynamic and Personalized Pricing: How Retail's Digital Revolution is Reshaping Trade

For much of history pricing was a customized process and haggling was an essential part of it.  Sellers would set a price based on market dynamics and their perceptions of individual buyers.  Negotiation was a critical skill for both sides and thus the exchange of goods was inefficient and seen as unfair.  In the mid 1800s John Wanamaker, a Philadelphian department store owner, popularized the setting of uniform pricing.  This change was seen as transparent, fair, and efficient and has persisted until the modern day where technology is again transforming our relationship with pricing.  Now armed with large amounts of market and consumer data retailers are able to set pricing algorithmically.  This is leading to a rise in both personalized and dynamic pricing in both online and retail settings. 

This shift is not just about a changing price tag; it's a fundamental transformation of how retailers operate. This article will break down these complex pricing models, explore how they are being implemented in both online and brick-and-mortar stores, and examine their profound effect on the trade promotions that are so vital to the consumer goods industry.

I. The Core Concepts: Dynamic vs. Personalized Pricing

Before we dive into the implications, it's crucial to understand the difference between two sometimes-confused pricing concepts.

• Dynamic Pricing is when a single price for a product fluctuates in real-time for all customers. These price changes are based on external, market-wide factors, such as:
  
  • Supply and Demand: Prices increase during peak demand and decrease during lulls. Think of airline tickets or ride-sharing surge pricing.
  • Competitor Activity: Retailers can instantly adjust prices to match or undercut a competitor.
  • Inventory Levels: Prices might be lowered to clear out excess stock or raised for items in limited supply.
    
    
• Personalized Pricing is when a company offers different prices to different customers for the same product at the same time. The price is based on individual data and perceived willingness to pay. This strategy uses: 
  • Purchase History: A retailer might offer a loyal customer a discount on a product they frequently buy.
  • Browsing Behavior: A customer who has repeatedly viewed an item might receive a personalized coupon to encourage a purchase.
  • Location and Demographics: While this practice raises significant ethical concerns, some models may adjust pricing based on data that indicates a customer's location or income level.

II. The Retail Revolution: From Paper to Digital

The shift to these modern pricing strategies is being made possible by technology that bridges the online and offline worlds.

• Electronic Shelf Labels (ESLs): In physical stores, ESLs are starting to replace paper price tags. These digital displays are connected to a centralized system, allowing retailers to change prices instantly across an entire store, or even a whole chain, with a single click. This technology is a game-changer for implementing dynamic pricing, making it easy to run flash sales or adjust prices based on real-time inventory.  Interestingly European countries are leading in the adoption of ESLs citing labor savings as the reason but are also using the technology for strategic purposes.  Large retailers are the ones adopting ESLs in North America including Best Buy, Walmart and Kroger.  
• Omnichannel Integration: Retailers are now collecting and analyzing customer data from every touchpoint, including their website, mobile app, and in-store purchases via loyalty programs. This allows them to create a seamless customer experience and deliver personalized offers whether the customer is shopping online or walking down a physical aisle.

III. How are Retailers implementing Personalized Pricing?

While you're unlikely to see two people at the same checkout register being charged different prices for the same item, retailers are implementing personalized pricing in more subtle ways:

• Targeted Discounts and Coupons: This is the most common form of personalized pricing in physical stores. For example, a grocery store app might send a customer who frequently buys a specific brand of coffee a digital coupon for that brand when they are near the coffee aisle.
• Personalized Promotions: The deals you see in your loyalty app or receive via email are tailored to your interests and buying habits. A shopper who buys a lot of cosmetics might get a special offer on a new skincare line, while another who buys baby products gets a discount on diapers.
• Location-Based Offers: Using a customer's location data from their mobile device, a store can send them a notification about a special offer when they physically enter it using technology like geofencing.

IV. The Legal Tightrope: Price Discrimination and Transparency

The primary legal challenge to personalized pricing in the CPG sector comes from long-standing antitrust and consumer protection laws. The Robinson-Patman Act of 1936, a cornerstone of U.S. price discrimination law, prohibits sellers from offering different prices to competing retailers for the same product if it harms competition. While historically applied to wholesale pricing, its principles are being re-examined in the context of personalized offers to individual consumers. The key question is whether offering different digital coupons or online prices to different consumers for the same product could be construed as a form of prohibited price discrimination.

Further complicating the legal landscape are state-level initiatives. New York, for instance, has recently enacted a law (Bill A3008) requiring clear and conspicuous disclosure when a consumer is being offered a "personalized algorithmic price." Read more about it here.

The Federal Trade Commission (FTC) is also actively scrutinizing these practices. While the agency has not issued specific guidance for the CPG industry on dynamic pricing, its focus on deceptive pricing practices and "surveillance pricing" indicates a keen interest in how companies are using consumer data to set prices. Read more about it here.

Personalized pricing is fueled by data, and this extensive data collection raises significant privacy concerns, particularly under comprehensive data privacy laws like the California Consumer Privacy Act (CCPA) and the General Data Protection Regulation (GDPR) in Europe.

Learn more about CPG relevant legislation in our blog here.

V. The Impact on Trade Promotions

Traditionally, trade promotions—the deals and discounts offered to retailers by manufacturers, and by consumers to retailers, —were planned months in advance. The rise of real-time pricing models could fundamentally change this process and present both challenges and opportunities for brands.

Challenges for Traditional Accrual Management:

1. • Forecasting Inaccuracy: The traditional method of setting financial accruals based on historical sales and data is becoming less reliable. When a retailer can change prices multiple times a day, a manufacturer's forecast for a specific promotion can quickly become obsolete, leading to financial misstatements and a disconnect between planned and actual spend.
   • Reconciliation Nightmares: With a constant flow of discounts and promotions, reconciling trade spend becomes a complex, manual, and time-consuming process. This can lead to a backlog of unresolved deductions and a lack of real-time visibility into the true profitability of a promotion.
   
   

   A New Blueprint for Success:

   • Data will need to become more timely, more granular and more democratized to enable a read on dynamic pricing. Instead of monthly or weekly sales data dumps from retailers, companies will need a continuous live feed of point of sale (POS) information. TPM must become increasingly more omni-channel and analytics must stretch beyond traditional POS approaches to a more comprehensive, multi-factor read. Overall, the data needs to measure and model these strategies, that is the gap!
   • Traditional syndicated data providers face a significant challenge with dynamic and personalized pricing because their models are built on the assumption of consistent, shelf-level pricing. When prices vary by individual customer, time of day, or digital channel, traditional point-of-sale aggregation becomes less representative of the true market.  What's needed to improve this data?
     • • 1. Enhanced Data Collection
       • 2. Data Clean Rooms & Privacy-Safe Integration
       • 3. Segmented Pricing Views
       • 4. Real-Time Data Solutions
     • Many CPG brands are supplementing syndicated data with their own retailer-specific data feeds, digital shelf analytics tools, and first-party consumer data to get a complete picture of pricing dynamics in today's market.

IV. Conclusion: Pricing as a Strategic Asset

In the past, pricing was a fixed and predictable aspect of business. Today, it is a fluid and strategic asset. The convergence of dynamic and personalized pricing models with new in-store technologies is forcing retailers and consumer goods companies to rethink their entire approach to trade promotions. By embracing a data-driven mindset and investing in modern pricing and TPM technology, companies can navigate this new landscape, turning a potential chaos of real-time prices into a powerful driver of profitability and a more relevant customer experience.

Additional reading from the following source material:

McKinsey and Company Guide to Dynamic Pricing

Guardian article on Dynamic and Personalized Pricing