The Pricing Evolution: The Perfect Storm Compelling Retailers to Shift from Rules-based to Optimized Pricing
The siren’s song of rules-based pricing solutions is tempting, however business rules are only one component of a comprehensive optimized pricing strategy that enables retailers to compete in today’s complex retail environment.
Historically, technology, data and analytical limitations forced retailers to use gut-feel, spreadsheets and rules to make pricing decisions. However, a trio of conditions has created a perfect storm that has compelled retailers to evolve and mature in pricing sophistication:
- Competitive Environment: Fierce & continuously shifting
- Data Deluge: New sources, increased frequency & granularity, lacking cohesion
- Empowered Shopper: Savvy, digital, social & price sensitive
The evolution of pricing has taken place over a number of years as this perfect storm has developed. In concert, optimization has also evolved by embracing each of the evolutionary stages and incorporating them into a pricing process that enables retailers to transform as the landscape continues to morph:
- Cost Plus. Tacking on a minimum margin requirement was a quick and easy way to automatically determine the price of a product when pencil and paper were the typical tools used for calculating prices. This concept worked for years, particularly when competitors were not on a retailer’s doorstep (physically or virtually) and the cost/volume advantages of the retail giants were not yet unsurmountable.
- Competitive Indexing/ Price Matching. As category killers and big box stores began capturing significant market share, the need for price matching and indexing became apparent- because shoppers were less loyal, more price sensitive and prone to deal seek.Again, indexing was an easy way to apply a pricing rule to automatically determine the price of a product. However, it still left the decision of determining which items drove price image, what competitor to target, and how much to index, to gut feel.Price indexing rules worked for many years, until format encroachment blurred the lines between competitors and geographic growth created environments where different competitors for different products were competing in different channels and markets.
- Zone Pricing. So retailers entered the third phase of the evolution which enabled them to differentiate their pricing for groups of stores in different environments. Initially, zones were based on proximity to a DC and/or some form of regionalization. This first generation of zoning drove up the number of zones actually required to differentiate pricing and neither pencil nor spreadsheets could keep up with the data volumes at speed and scale. Price zones continue to evolve and now tap into both shopper demand and all forms of competitive data (shopped, online and market data) in order to enable differential pricing that is shopper-centric and cognizant of competitive positioning goals. Science enables the derivation of store clusters that are appropriate for differential pricing and determines the optimal number of store clusters.
- Category Roles & Strategies. In conjunction with this movement from pencil and paper to spreadsheets to software solutions was the introduction and evolution of the discipline of category management. This introduced the concept of category roles, strategies and objectives and, for the first time, retailers recognized the fact that products played distinct roles in the way shoppers perceived their brand and shopped their stores. The real challenge came when category managers were unable to translate the high level strategies developed in the executive suite into action at the store level. Pricing became more complicated as specific tactics associated with the different category roles and strategies came into pricing decisions– they could no longer simply apply rules for margin and indexing, they needed tools to help them drive to specific strategic objectives.
- Demand-Driven Pricing. Although computing power at the desktop had increased dramatically over this period, the amount of data to be analyzed was increasing even faster and spreadsheet capacities were exceeded.Retailers turned to demand-based science to analyze and predict shopper behavior at the store/item level. This supported a process-driven, fact-based approach to pricing that systematically operationalized internal and external data, enforced business rules and competitive indices, prioritized decisions within operational constraints and enabled them to take strategic, targeted action at speed and scale.
- Shopper-Centric Pricing. And the future will continue to require more granular, adaptive and predictive capabilities as retailers move to more segmented and personalized pricing to meet the demands and gain the loyalty of the empowered consumer. Changing the focus of everything they do from product-centricity to shopper-centricity, and doing it profitably, depends on understanding and exploiting variation in shopper and competitor behavior in different channels, markets and stores for different categories, sub-categories, item groups and products for different seasons, holidays and events.
Optimization meets all of these evolutionary requirements, permitting retailers to propel themselves along the pricing maturity curve by:
- Tapping into vast amounts of internal & external data
- Enforcing business rules & operational constraints
- Enabling competitive positioning at the most appropriate levels
- Analyzing shopper behavior & price sensitivity at the most granular levels
- Ensuring that category strategies are translated into precisely executed pricing tactics
- Simulating alternatives & prioritizing decisions
- Measuring effectiveness & adapting on-the-fly
- Achieving strategic & financial objectives
Although rules-based pricing is tempting in its simplicity, it is unable to recognize and adapt to rapidly changing shopper and competitor behavior or strategically maximize profitability at the speed and scale required to survive and thrive during this perfect storm.