Background and challenge 

A growth-equity-backed construction software business engaged L.E.K. Consulting to develop a strategy to optimize its pricing/packaging approach. The goal was to better match the right software price/package to the right customer, thus improving customer service.

The client asked L.E.K. to conduct a study to evaluate customer willingness to pay for various features and propose optimal levels of pricing across key customer segments, thereby maximizing adoption and revenue uplift. In addition, the client needed support in developing a framework to guide how to best bundle its standalone support and services offerings with the existing software features. 


L.E.K. performed a range of analytical tasks to support the client’s goals: 

  • Led an in-depth assessment of customer needs for construction software across various segments via extensive interviews, evaluating customer preferences for product features and functionality 

  • Performed a rigorous analysis of comparable software solutions and bundles across the competitive landscape to determine the industry’s best practices for pricing and packaging strategy 

  • Conducted extensive analyses using multiple techniques, including TURF (total unduplicated reach and frequency), conjoint analysis, Van Westendorp, and financial impact analysis, to identify the optimal go-to-market bundle, tier and estimated incremental revenue  


  • The work led to a clear understanding of the trade-offs and upside from changing pricing and an architecture for packages to both on-board new customers and migrate current customers to the suite of solutions that best matched their needs 

  • The new pricing model was executed for new sales and used as the basis for implementing price changes to legacy customers.  This has led to an increase in both conversion and average sales, increasing new revenue run-rate by over 30% 

Sophisticated Pricing Engine Points the Way to a 9% Price Increase Across Company’s Multi-Category Product Portfolio
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We created a successful pricing engine which enabled our client to achieve an average price increase of about 9% across product-channels while re-assessing optimal pricing and promotion on a weekly basis.

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