AI for revenue growth: using ML to drive more valuable pricing | 7wData

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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%.

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.

FitClasses are a popular offering across FitCo’s brands. They are premium experiences catering to niche fitness demand and sold on a pay-per-class basis on top of standard memberships.

Whilst FitCo had ensured a consistent user experience across its portfolio, local operators were still able to set schedules and prices for FitClasses in their studios with nearly total independence. As a result, prices varied widely between classes and locations.

Whilst FitCo understood that some of this variance reflected local conditions, they also suspected there was considerable room for improvement in the way prices were set across its portfolio.

FitCo had undertaken a pricing exercise two years ago in which prior management had opted to centralize FitClass pricing and institute a blanket price increase of between 10% and 20% across the board.

This blunt approach had not been successful. It had failed to take into account the price elasticity of demand of its customer set across the wide range of classes and locations, and the price increases actually resulted in overall revenue declines of 2% as the subsequent reduction in demand in many classes outstripped the increases in price. They were forced to unwind the price changes a couple months later.

Though painful, that experience had at least been useful in giving FitCo a pretty solid dataset on price elasticity of its FitClass customer set. It could chart how class demand changed in response to price increases for each of three utilization bands — high (>85%), medium (50–85%) and low (