Pricing Analytics: Executing on a New Pricing Strategy for a Global Eyeglass Lens Manufacturer

The Client

The client is a leading global eyeglass lens manufacturer with operations around the globe and a strong reputation for innovation. The US division was launching a new pricing strategy to enhance value proposition to customers.

The Challenge

The client offered 10000+ SKUs to 30,000 major eyecare groups in the US through 1000+ price lists. However, given the complexity of data acquisition across a number of systems and the analytical processing needed, it was difficult to track pricing performance at an individual customer level.

The Solution

Core Compete created a pricing data lake and delivered Self-Service pricing analytics dashboards and tools to help the Pricing Team gain visibility into customer pocket margin leakage and to help them simulate pricing impact on the revenue-mix. Furthermore, the solution enables the pricing team to provide account teams with an up-to-date view of the individual customer level sales mix and profitability along with sales guidance for achieving the target performance capture.

Highlights

  • Complex data acquisition and consolidation to create pricing data lake in the cloud

  • Pricing-elasticity models developed to assess pricing performance at the individual customer level

  • High user adoption from sales end users arising from intuitive visualizations and role-based insights that can drive immediate actions in the field

  • Provides the ability to assess impacts on portfolio performance through cross-elasticity models

Core Compete in Action

A data lake was created for pricing by consolidating existing data from 6 different sources including Sales, Financial Systems, Planning and an existing Data Warehouse. The source data was available in varying formats including some data that was only available in spreadsheets.

Self-Service analytics dashboards with Price-Elasticity Models were developed to analyze customer pocket margin leakage, revenue-mix impacts, and price-list comparisons. Cross price-elasticity models were developed to estimate volume impact of price changes on the overall portfolio.

The technologies used were SAS Office Analytics, SAS Visual Analytics, and SAS add-on for Microsoft Office/Excel. Pre-formatted PowerPoint reports are generated that are automatically sent to sales executives that work as selling tools.

The Result

Over $20 million in pricing benefits through effective discounting and streamlining of the annual rate increase program. The pricing team has been able to utilize the pricing data lake and analytical platform to significantly improve price execution and also support the sales team in key account price negotiations.

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