Discover how Revology Analytics propels mid-market businesses to sustainable, profitable growth by building advanced, in-house Revenue Growth Analytics & Management (RGM) capabilities—fast.
Our senior expert-led, hands-on approach ensures you own the tools, insights, strategy and processes needed to thrive long-term.
Gain exclusive access to the latest insights from over 150 commercial leaders on the state of Revenue Growth Analytics in 2025, based on our expanded Revenue Growth Analytics Maturity Scorecard™.
Discover how Revology Analytics propels mid-market businesses to sustainable, profitable growth by building advanced, in-house Revenue Growth Analytics & Management (RGM) capabilities—fast.
Our senior expert-led, hands-on approach ensures you own the tools, insights, strategy and processes needed to thrive long-term.
Gain exclusive access to the latest insights from over 150 commercial leaders on the state of Revenue Growth Analytics in 2025, based on our expanded Revenue Growth Analytics Maturity Scorecard™.
Overview: Pragmatic AI in Practice This article from Revology Analytics explains pragmatic AI in the context of modern pricing analytics and revenue growth management. It
Unlock the full potential of your sales and marketing teams with AI-driven tools! Discover how artificial intelligence can revolutionize your business by streamlining processes, enhancing customer engagement, and boosting performance. Learn about top AI tools for lead generation, sales forecasting, and campaign optimization. Find out how predictive analytics can forecast demand and automate routine tasks, freeing up time for strategic activities. Get practical tips for selecting and integrating AI tools seamlessly into your workflows. Stay ahead of the competition and drive business growth with AI. Read more now!
Revenue Growth Management (RGM) is often mistakenly equated solely with pricing strategies. However, true RGM encompasses a broader scope, leveraging comprehensive commercial analytics to drive business growth across multiple dimensions.
Beyond pricing optimization, RGM integrates advanced metrics and analytics efforts such as Customer Acquisition Cost, Lifetime Value, Churn Rate, Marketing ROI, and Net Revenue Retention to provide a holistic view of revenue and profit opportunities.
By analyzing these diverse data points, companies can unlock insights that lead to improved customer segmentation, targeted marketing campaigns, optimized product and customer mix, and enhanced operational efficiencies.
Companies that expand their RGM focus beyond pricing are better positioned to identify untapped revenue streams, boost profitability, and achieve sustainable growth in an increasingly competitive and insights-driven marketplace.
Explains how mid-market companies can revolutionize their revenue strategies using advanced analytics, machine learning, and AI. It details how these technologies help identify inefficiencies, enabling companies to make informed, data-driven decisions. This strategic approach is essential for optimizing sales and marketing operations, ultimately leading to sustainable growth and a stronger competitive position.
Modern companies collect substantial amounts of data. The data captured worldwide is expected to exceed 180 zettabytes by 2025, up from 64.2 zettabytes in 2020. This proliferation of data presents a massive opportunity for companies seeking to gain valuable insights into their customers and operations.
Yet, most businesses don’t use data effectively, as up to 73% of all data goes unused. So, how can companies leverage the data they collect to boost profitability? In this article, we’ll explore how businesses, particularly in the mid-market space, or those finding themselves “data rich, but insights poor,” can gather and use data better and increase profit margins through advanced analytics.
In this case study, we delve into the strategic implementation of dynamic pricing by a Fortune 500 specialty retailer, aiming to enhance market share and profitability in their brick-and-mortar (B&M) channel. Despite the success of dynamic pricing in their e-commerce channel, the retailer faced unique challenges in translating this strategy to their physical stores, constituting 90% of their sales. The initiative encountered skepticism and logistical hurdles in frequently changing price tags. In partnership with Revology Analytics, the retailer overcame these challenges through a practical, simple-to-understand dynamic pricing solution, resulting in increased gross margins, fast and cost-effective implementation, and successful stakeholder engagement in their dynamic pricing journey.
It often amuses me that in the era of ChatGPT (and despite the countless books I have in my library on AI/ML), I’ve never used any Deep Learning model for any of my client engagements or prior advanced analytics leadership roles.
For 99.95% of data problems in traditional (non-tech) companies, Deep Learning (and any of its derivations like LSTMs, GANs, CNNs, etc.) are overkill at best and a complete waste of time (or not applicable) at worst.
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