Success Stories

Growing Revenues through Data-Driven Pricing Strategy

Through a combination of data science and business expertise, RevGen devised a strategy that would grow our client’s revenues by more than 20%.

Businessman points to a holographic report of a financial growth line

Project Overview

Our client, a national SaaS company, had an aggressive goal to increase revenues by at least 20% within three months. While their instinct was simply to raise prices across the board, they were not confident that would get them to their goal. Additionally, they were concerned about the impact on their customer experience in initiating such a change.  

RevGen came in with a proposal — why not use data science to model various pricing scenarios to make an informed decision about pricing strategy? 

Client Challenge

While the goal of increasing total revenue was clear cut, getting to a final answer wasn’t so easy.

Need for market data

Our client had collected their own pricing and demand data for years, however this data required significant processing to understand the quantitative relationship between price point and demand.

Significant competition in the industry

Even with a data-supported recommendation, it is never a good idea to make pricing decisions in a vacuum – especially when in a competitive market, like our client.

Fast turnaround

In order to achieve some of their revenue goals for the year, the client needed to make a quick decision. This meant the RevGen team had limited time to complete our analysis.

Approach

We conceptualized the project as two parallel processes. The first would be using the data collected to model various pricing scenarios to test which would be the most likely to achieve the necessary 20% lift. The second would be undertaking a rigorous competitive analysis, so that any recommendation would be grounded by real-world market constraints.

Solution

By marrying our rigorous data science expertise with our many years of customer experience knowledge, we were able to craft a recommendation to meet our client’s goals.

Pricing elasticity model

RevGen’s data scientists created a pricing elasticity model that ran through twelve different hypotheses to see where additional revenue could be found. From that initial test, we were able to narrow it down to three viable options. 

Not just pricing, but demand

Our model also took into account demand curves — calculating whether total product sales would rise or fall based on the hypothesized pricing changes.

Comprehensive competitive analysis

Meanwhile, our customer experience experts undertook a thorough market review of similar products, from both a pricing perspective, and also considering product offerings and features, as well as the holistic customer experience during the sales journey.

Results

Results of our modeling and analysis in hand, we handed over a pricing options that would raise overall revenue while maintaining a quality customer experience.

A path to a 22% revenue lift

Each of the three final pricing strategy options we presented to our client offered an estimated 22% lift in revenue, conservatively. 

"If it were us..."

Of the three, we highlighted our recommended “Go Forward” strategy that would help shift demand for their product from lower tiers to higher price points, enabling greater cross-selling and upselling. While this also required some modifications to their product structure, it had the added bonus of helping them differentiate in a crowded market. 

Quick turnaround to allow for quick implementation

RevGen was able to complete the entire project in just six weeks; our client estimated it would have taken internal resources nearly six months. This allowed them to begin focusing on implementing the new strategy as soon as possible to boost in-year revenues. 

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