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Dynamic Pricing in e-Commerce

We provide clients with a new way to get the best price every time all the time.
Dynamic Pricing

Dynamic Pricing in e-Commerce combines decades of McKinsey pricing expertise and deep industry insights. Our approach provides online retailers with a proven pricing engine to drive revenue and profit growth. Our engine is a Price Advisor module and part of our Periscope Pricing Solutions.

An overview of how we work with you

Step 1: Assess your price potential. E-commerce pricing has become complex and time-consuming, especially when done manually. Our approach to dynamic pricing helps e-commerce companies not only overcome these pain points but also drive revenue and margin growth (in the short and long term). We start with a quick assessment and benchmarking project. The assessment combines advanced analytics with simplicity to deliver a practical answer.

Step 2: Prototype and build your dynamic-pricing capability. To validate the opportunity, we build a proof of concept using established capabilities and tools. With the help of internal and external data, we work with your team to implement the minimum viable version that’s suited to your organization.

Once the opportunity is proven, we work with you to prototype, pilot, and build out a dynamic-pricing engine. We also work closely with you to build the necessary capabilities so you can achieve sustainable, consistent, and ongoing pricing improvements rather than one-time price adjustments.

Following are five modules that can be selected and adapted as needed to customize solutions to client needs:

  • The key-value-item (KVI) module estimates how much each product affects consumer price perception by dynamically evaluating user behaviors (e.g. click rates, product reviews, and search data).
  • The competitive-response module recommends price adjustments based on competitor prices updated in real time.
  • The elasticity module uses time-series methods and big data analytics to calculate how a product’s price affects demand, accounting for a wide variety of factors, including seasonality, cannibalization, and competitive moves.
  • The long-tail module helps a retailer set the introductory price for new or long-tail items through intelligent product matching.
  • The omnichannel module coordinates prices among the retailer’s offline and online channels.

Examples of our work

We have helped clients improve sales growth 2 to 5 percent and margin growth 5 to 10 percent. We serve a diverse set of global retail and consumer clients across industries.

Optimizing the long tail for a large US retailer with more than two million SKUs

  • Implemented a dynamic pricing solution that balanced maximizing absolute revenues and increasing productivity.
  • Our advanced-insights specialists adjusted the algorithms in each of the solution modules.
  • In eight weeks, the team joined the client’s pricing managers to deploy the long-tail module and a competitive-response module as part of the dynamic-pricing engine.
  • The impact: Up to 3 percent increases in both revenue and margins in the pilot categories.

Helping a European retailer find out which items to price competitively

  • Built a tailored module that could statistically score each store item’s importance to consumer price perception.
  • The module scored each item from 0 to 100 by analyzing granular internal and external data, including shipping costs, return rates, search volume, number of competitors carrying the product, and competitor pricing.
  • With this information, the system was able to understand which part of the assortment to price for margin and which one to price competitively.
  • The impact: After a three-month pilot, the retailer saw a 4.7 percent improvement in earnings before income and taxes in pilot categories and identified a 3 percent improvement potential in overall return on sales.

Designing an Asian online retailer’s elasticity module

  • Built an item-level pricing solution that could optimize for both profit and gross merchandise value (GMV) for a leading Asian e-commerce player.
  • The created multifactor algorithm drew on data from approximately ten terabytes of the retailer’s transaction records, including product price, price of a viable substitute product, promotions, inventory levels, seasonality, and estimates of competitors’ sales volumes.
  • Recommendations were displayed on an easy-to-read dashboard that category managers helped design and test.
  • The impact: After only a few months of using the module, the retailer saw a 10 percent rise in gross margin and a 3 percent improvement in GMV in the pilot categories.

Featured experts

Sebastian Klapdor

Partner, Munich

Gadi BenMark

President and General Manager, McKinsey Social, New York

Mathias Kullmann

Senior Partner, Düsseldorf

Stephan Zimmermann

Senior Partner, San Francisco

Featured Insight

Article

How retailers can drive profitable growth through dynamic pricing

– The secret is in customization: dynamic-pricing solutions must be tailored to a retailer’s business context, objectives, and ways of working.

Want to learn more about how Dynamic Pricing can help your organization?