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.

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.

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.

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.

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.

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.

Most mid-market companies are still stuck with basic reports and ad-hoc Pricing & Profitability analyses that take several weeks, offer little actionable insights, and are not reproducible. There is also a widespread misconception that it takes heavy upfront investment (both time and money) to build Margin Analytics & Optimization platforms that can truly transform an organization's Pricing discipline and customer & product analytics rigor.

Regardless of your company's growth stage, it is paramount that you have an easy-to-use and actionable Margin Analytics platform that provides rapid descriptive (what happened), diagnostic (what caused it), and predictive (what will be the impact) analytics capabilities.

Commercial Analytics in B2B environments is a team sport: you need to win the trust and credibility of the Sales Team to accelerate the impact of analytics and drive sustainable Pricing and Data ROI for your company.

Read this brief opinion piece on why listening to and collaborating with your Sales teams is not optional - it's essential.

Price leakage is a big problem for most Manufacturers and Distributors and can have a considerable adverse effect on their Operating Profits. One of the big reasons price leakage occurs is not having an adequate Margin Analytics Platform deployed and used across the organization. This means no effective measurement systems track discounts and rebates versus guidelines or strategies.

In this brief guide, we will discuss how to build a robust in-house solution, leveraging standard technologies you are familiar with. Most executives in charge of Pricing/Margin are hesitant to go down the margin analytics platform path, thinking it will take years and $ millions to implement. We will show you how to achieve 80-90% potential value realization using simple techniques and analytical methods.