Introduction

Pricing optimization is a powerful lever for revenue growth, yet it’s too often put in the too-hard basket by too many companies.

This is because traditional pricing optimization methods can be both complex to implement and limited in their ability to accurately capture the full range of factors that can impact pricing.

Machine learning (ML) is well-suited to pricing optimization problems — both for its ability to handle complex features, as well as its ability to generalize to new situations. Moreover, recent advances in managed services has put these ML solutions within reach of virtually any organization.

In this anonymized example we explore how a company with no data science expertise was able to use managed ML services to implement an ML-powered pricing strategy that performed 2x above traditional approaches and resulted in estimated revenue growth of 11%.

Situation

FitCo is a premium fitness brand, based in Los Angeles, that operates a portfolio of over 600 gym and fitness center locations across the United States.

Having grown rapidly by acquisition over the past several years, management attention had now turned its attention to boosting organic revenue growth, which had been stubbornly flat on a per-studio basis.

FitCo had identified FitClass — its suite of specialty fitness classes — as a prime source of organic growth. Specifically, it had identified pricing of these classes as a major potential area of improvement.

#management #growth #machine-learning #artificial-intelligence #revenue