Discover the power of Revenue Growth Analytics with our newly launched Maturity Scorecard, designed to assess your organization's capabilities in Margin Analytics & Optimization, Sales & Customer Growth Analytics, and Promotion Effectiveness & Optimization.

Take the 3-minute assessment and gain access to tailored recommendations for improvement, as well as an extensive library of free Revenue Growth Analytics resources, including Tableau dashboards and price elasticity models in R and Python. Don't miss out on this opportunity to elevate your Revenue Analytics capabilities and drive significant business impact.

Understanding the impact of price changes and promotional investments on business outcomes is crucial for all commercial teams (revenue management, finance, analytics, sales, and marketing). In today's data-driven world, it is essential to have a deep understanding of price and promotional elasticities at the customer-product level to optimize sales, profitability, and market share.

The below guide gives an overview of the typical modeling approaches for price elasticities and contrasts regression vs. machine learning methods for industry best practices.

In this video, the Revenue Growth Drivers Analysis technique is explored in detail for a fictitious company, Company ABC, to understand the drivers of year-over-year net revenue growth.

The analysis breaks down the impact of Pricing actions, Volume, and Customer and Product mix on the company's Net Revenue growth. The detailed analysis is conducted at the customer-product level rather than an aggregate level, allowing for more actionable insights.

We illustrate the detailed calculations for Pricing, Volume, and Mix impacts using an Excel Growth Drivers Analysis template, available for download at https://www.revologyanalytics.com/revenue-analytics-tools.

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.

AI/ML is not the ultimate solution for every data-related problem. We must first set up foundational descriptive and diagnostic analytics capabilities and more straightforward ML approaches before applying more advanced techniques. It's essential to understand the business problems and work closely with functional partners to solve them in a way that aligns well with the company's analytical readiness and operating rhythm.

The examples of Revenue Growth Analytics use cases mentioned, such as Promotional Analytics, Everyday Price Optimization, Dynamic, Automated Clearance Pricing, Bulk Purchase Optimization, Customer Segmentation & Predictive Insights, and Customer Churn & Cross-Sell Modeling, are practical and impactful capabilities that can drive measurable sales and gross profit improvements. They can be implemented using simple math and essential ML and with popular tech stacks with which pricing, supply chain, and sales partners are familiar.

Overall, the focus should be on pragmatic and co-created approaches with business stakeholders that are most likely to get adoption and impact rather than on celebrating complexity for its own sake.

Companies are in the business of making money, and most often, they care about maximizing their Revenues, Gross Profit, and Operating Income. One of the biggest challenges Finance and Revenue Management teams face is the ability to systematically diagnose the drivers of fundamental business performance changes near real-time.

The price-cost-volume-mix analysis, sometimes known as Revenue or Gross Profit Growth Deep Dive, helps you understand the relevant components that drove your revenue, gross Profit, or other critical financial changes from one period to the next (or for actuals vs. budget).

This short video will show you a simple Gross Profit Growth Deep Dive built on customer transactional data.

This week, I wanted to show you another simple yet effective revenue analytics technique to steer sales behavior in the right direction and drive Gross Profits. Part 2: Discount Curve Analysis

Revenue Growth Analytics (RGA) is a foundational enabler for organizations looking to transform their Revenue Growth Management strategies. RGA goes beyond traditional pricing techniques and provides insights into areas such as customer mix management, customer retention and cross-sell opportunities, and customer lifetime value. One of the key techniques used in RGA is RFM (Recency-Frequency-Monetary) Analysis.

RFM Analysis is a simple yet effective method of analyzing customer transactional data to drive better customer insights and improve customer retention, profits, and customer satisfaction.

The article discusses the common issue faced by Analytics/Data Science COEs where they invest heavily in new initiatives but struggle to see adoption and measurable outcomes. I advise new leaders in the field, data practitioners, and CXOs to focus on fewer initiatives that directly impact the revenue and gross profit drivers of the business, prioritize a subset with the right balance of impact, effort, and support, and involve an internal team of experts from relevant functions in the development process. Additionally, I advocate focusing on specific areas, such as price optimization, customer churn reduction, cross-sell optimization, promotion and discount optimization, and procurement optimization, to generate substantial value and internal adoption in the first couple of years before tackling larger-scale digital transformation type efforts.

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.

Data science is an ever-evolving field, and its roles are also changing. As businesses increasingly rely on data to inform their decisions, there is a growing need for people with both the technical skills and domain/industry expertise to drive measurable value.

Analytical fire drills and knee-jerk pricing actions are becoming increasingly common in the consumer goods industry. With intense competition and ever-changing customer needs, companies must be agile enough to rapidly adjust their strategies to remain competitive and deliver relative value to their consumers.

By creating a dynamic integrated performance deep dive, CPGs can identify specific areas of improvement to maximize efficiency while delivering high-quality products at competitive prices — all of which will help them stay ahead of the competition in today's market.

In the below quick guide, we'll explore the steps needed to build a reproducible and highly actionable (and impactful) performance deep dive that can help CPGs address Revenue performance shortfalls.

It's no secret that many retailers and distributors are struggling with suboptimal prices, leading to substantial profit losses or missed revenues.

But by developing a solid price-testing playbook, retailers and distributors can maximize profits without sacrificing sales.

You don't need to spend 12-18 months deploying an enterprise price optimization solution or waste a ton of CAPEX. You can start building your own semi-automated price-testing solution today at a fraction of the development times and costs.

If you're a Pricing or Finance executive looking for a way to improve your company's bottom line, read this 5-minute guide on how to build a sustainable Price Testing playbook.

A brief guide for Manufacturers and Distributors to boost profits by 5-10% using simple, effective pricing and analytics techniques.

If you're a manufacturer or distributor, you know that discounting is vital to the success of your sales force. But are you discounting enough? Are you discounting too much?

This article will discuss the importance of discounting in B2B settings and how to determine the right discounts for your customers and products. We'll also discuss when to use each type of discount and offer tips for increasing Net Revenue and Gross Profit with analytics and surgical discounting.

Below are my top Revenue Growth Analytics quick wins that Distributors should implement (or right-size) to boost gross profits $ by 5-20% and increase liquidity.

At its very basic level, industry margin pools help you understand what share of the total profits goes to each supply chain participant, from Manufacturer, Distributor, to Retailer.

If you are a ~$1B Manufacturer or Distributor that spends ~ 15% of its Gross Revenues on Promotions & Rebates, every 50bps improvement in your Promo ROIs will drive +$0.75MM to your EBITDA. Let me describe the steps to take to enable these capabilities for your organization.

Did you know that most promotional investments by Manufacturers are a negative ROI investment, and each 1% improvement in Promo ROI can be a massive benefit to your Operating Profit?

Many mid-market manufacturing CFOs, CMOs, Pricing, and Sales executives are struggling with Promotional spending outpacing Profit growth, leading to unnecessary profit erosion.

Fortunately, most companies have the proper data assets to build sophisticated and actionable Promotion Effectiveness & Optimization solutions in-house, using methods and technologies they are already familiar with.

If you've always wanted to know how to build robust Pricing & Promotion Analytics capabilities organically and quickly (in ~ 4 months), please read this detailed Case Study that walks you through the critical steps with concrete examples.

Part 1: Using Gross Margin % vs. Net Sales Customer Matrix to segment customers into actionable Sales & Pricing performance clusters.