Enhanced by AI and machine learning, price elasticity modeling enables businesses to optimize pricing strategies by understanding how customer demand reacts to price changes. Unlike traditional methods that often overlook the complexities of consumer behavior, these advanced models provide data-driven insights to set prices that maximize revenue.

Mid-market manufacturing businesses often struggle with accurate demand predictions due to evolving customer preferences and changing market trends. This complicates stock management and necessitates innovative strategies to reduce waste.

Advanced AI/ML-enabled demand forecasting can significantly improve capacity planning, financial planning, and profit margins by enhancing forecast accuracy. Traditional methods, relying on outdated data and gut instinct, fall short, while AI/ML technologies analyze vast data sets to identify patterns and adapt to dynamic market conditions.

Many mid-market retailers and wholesalers' pricing, finance, and merchandising executives have inherited outdated pricing solutions. These legacy pricing processes don't quickly scale and heavily rely on expensive human, mundane tasks. There are no intelligent dynamic pricing capabilities that automatically set prices based on sales patterns, corporate objectives, and changing marketplace behavior. It often results in missed profit opportunities and liquidity problems.

Fortunately, you can build simple yet effective dynamic pricing solutions in 3-to 4 months that achieve up to 80% of its incremental Gross Profit $ potential. You can design and implement using practical methods and accessible technologies that empower your teams to take complete control without expensive 3rd party support.

Read more below about how you can achieve this and unlock an extra 1-3% in GP% in year 1.

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.

Manufacturers need more than great products to stay competitive—they need deep customer understanding and smart decision-making. Traditional methods like Excel spreadsheets fall short. Advanced insights capabilities help manufacturers uncover hidden opportunities, streamline operations, and connect meaningfully with customers by turning data into actionable strategies.

To achieve this, manufacturers should collect, analyze, and apply accurate data across their sales and marketing infrastructure. Clean data, the right tools, and a data-driven culture are essential for leveraging insights to drive growth and improve customer engagement.

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.

Is the lack of robust clearance pricing strategy and manual pricing processes causing unnecessary and costly inventory buildup in your warehouses?

A leading $5B consumer durables wholesaler in North America wanted to unlock significant liquidity tied up in unproductive inventory by deploying in-house markdown optimization capabilities. Please read the below case study to understand how we helped them solve a $150MM unproductive inventory problem with dynamic clearance pricing.

This article is one in a series of five detailed case studies we will publish bi-weekly on key B2B Commercial Analytics topics. It gives insight into our outcome-based analytics process, including both technical and change management pieces.

In the complex world of marketing analytics, understanding the effectiveness of different marketing channels in driving customer conversions remains elusive for many.

Our latest article explores two powerful analytics techniques that can illuminate your marketing efforts: Multi-Touch Attribution (MTA) and Marketing Mix Modeling (MMM). Even if you're not a data science expert, we break down these complex methodologies into easily digestible insights for any commercial professional.

Discover the pros, cons, and best-use cases for MTA and MMM and how advancements in marketing analytics are democratizing access to these potent tools. Join me and Gabriele Franco, co-founder of the breakthrough marketing analytics startup Cassandra, as we unlock the full potential of your marketing investments.

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.

Explore how leveraging advanced commercial analytics can dramatically enhance profit margins for companies growing through acquisitions. Our latest article dives into the pivotal role of unified data sources and system integration in streamlining operations and boosting profitability. Discover how tools like Revenue Growth Analytics reduce manual data processing and provide actionable insights that lead to more efficient decision-making and optimized business strategies. Read the complete insights to understand the transformative impact of these technologies, tools, and methods on acquisition-heavy companies.

It's easy for us to get caught in the hype surrounding the latest and greatest tech advancements. Currently, AI is at the forefront of this phenomenon (what Gartner calls "peak of inflated expectations"). While AI certainly holds transformative potential, companies must approach its adoption with a measured perspective, especially in the mid-market sector (think $100MM-$1B in revenues).

AI and ML have been reshaping industries, and Sales and Marketing Executives stand at a pivotal crossroads as capabilities from leading tech platforms accelerate.

The key to a competitive edge and increased profitability is leveraging popular tech stacks like Microsoft and Google. However, navigating through an oversaturated market of ~ 10,000 "turnkey" software products for Sales & Marketing Enablement is daunting.

Enter In-Sourcing, the strategic approach that elevates your company's analytics maturity, using popular tools like Microsoft's Power Platform and Azure or Google Cloud AI. In-sourcing your Revenue Growth Analytics capabilities highlights the transformation from vendor-reliance to an empowered, insights-driven organization, offering a path for executives to transform their companies to truly data and insights-driven.

Kickstart the New Year with a game-changing gift for your team!

Discover our specialized corporate training programs in Advanced Revenue Growth Analytics, designed to fill the practical skill gaps in Revenue Growth Analytics (RGA) that many companies face.

It's the perfect opportunity to empower your commercial and analytics/data science teams with hands-on, real-world skills that immediately impact your bottom line.

Revology Analytics had the opportunity to join 25 leading Revenue Growth Management executives in CPG and Manufacturing in St. Louis earlier this week. We discussed the state of Revenue Growth Analytics in the CPG industry and the significant opportunity to in-source and own many of the advanced analytics capabilities foundational to a successful RGM and Pricing strategy.

In many ways, the state of Revenue Growth Management and its associated analytics capabilities have not changed meaningfully in the last ten years for Consumer Goods companies. There's still a heavy reliance on data and information providers like Nielsen and IRI to carry out foundational Pricing & Promotional Analytics efforts, including Price Elasticity and Marketing Mix Modeling, or a heavy reliance on 3rd party software for price analytics, scenario analyses, and optimization efforts.

However, the world is changing....fast.

This article was originally published in Authority Magazine’s “The CMO” newsletter.

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With the growing complexity of marketing channels and the need for businesses to constantly adapt their strategies, managing marketing budgets effectively has become more critical than ever. In this interview series, we speak with CMOs and other marketing leaders who have significant experience in budget management and optimization, to share their “5 Ways To Manage Your Marketing Budget For Improved ROI.” As a part of this series, we had the pleasure of interviewing Armin Kakas.

In our popular webinar on Marketing Mix Modeling (MMM), we disdcuss the pros/cons of MMM, and its growing importance in today's privacy-centric, cookie-less world.

We also deep-dive into Meta's innovative MMM framework, Robyn, highlighting how it has revolutionized marketing analytics with its cost-efficiency, granularity of insights, and flexibility.

Our detailed presentation and recorded webinar offer practical demonstrations of Robyn's capabilities, providing a holistic view of channel effectiveness, adstock decay rates, and marketing budget optimization.

The era of six-figure marketing analytics is a thing of the past - welcome to a future of accessible, affordable, and robust marketing effectiveness measurement.

It's time to rethink traditional sales and price elasticity modeling methods.

Aggregated data at the retail chain level and modern machine learning models are vital in shaping an efficient, practical, and robust approach for predicting and explaining retail sales.

Why this shift? The traditional reliance on granular, disaggregated (i.e., store-product-day level) data and old-school models are costly, complex, and often misaligned with practical management strategies.

Turning to aggregated data and modern ML approaches reduces costs, enhances model accuracy and efficiency, and makes these valuable insights more accessible to smaller firms and faster for larger ones.

Moreover, in-sourcing these capabilities builds critical, sustainable expertise for Revenue Growth Management, freeing companies from reliance on 3rd parties. This shift signifies an effective and sustainable future for Revenue Analytics and redefines competitive positioning, enabling superior service and product offerings.

Discover how Revology Analytics collaborated with a leading med-tech company to develop a dynamic Margin Analytics platform to enhance pricing and discounting capabilities and promote a stronger margin management discipline.

This case study illustrates the effectiveness of our Outcome-Based Analytics framework combined with targeted Revenue Growth Analytics expertise. Our highly collaborative approach involving internal Core Teams and Sales Team power users ensured the co-creation and sustainable adoption of the Margin Analytics and Optimization capability.

Read more about our detailed approach, with examples of a B2B Marging Analytics dashboard built using simulated data.

RFM Analysis is a powerful tool for businesses seeking insights into customer behavior and segmenting them based on purchasing habits. By calculating RFM scores and creating segments, companies can identify valuable customer groups and target them with personalized sales and marketing campaigns. RFM Analysis is not limited to the retail industry or the marketing domain. It can be applied to most industries and functional domains that touch the customer, including pricing, supply chain, A/R, product management, and customer service. Additionally, RFM Analysis can benefit nonprofit organizations by understanding donor behavior to optimize fundraising initiatives.

In part 2 of our RFM Analysis article, we'll dive deeper into how we can calculate RFM scores, visualize customer performance by RFM segment and discuss sales and marketing implications.