Many mid-market retailers and wholesalers' pricing, finance, and merchandising executives have inherited outdated pricing solutions.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.

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.

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.

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.

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

Have you ever offered a deep Price Discount in December in hopes of accelerating 4Q and YE target achievement, only to drive Unit Sales but suffer huge Gross Profit losses?

Commercial Analytics, aimed at improving Gross Profits and Sales Productivity, doesn't have to be complex to drive the proper outcomes with Pricing and Sales teams.

𝐒𝐚𝐥𝐞𝐬 & 𝐃𝐢𝐬𝐜𝐨𝐮𝐧𝐭𝐢𝐧𝐠 𝐒𝐭𝐚𝐜𝐤 𝐑𝐚𝐧𝐤𝐢𝐧𝐠 is another simple yet effective Revenue Analytics technique to steer Sales behavior in the right direction and drive incremental Net Sales (and Gross Profits).

I often see Manufacturers and Wholesalers making the mistake of trying to revolutionize entrenched Customer habits vs. creating easy-to-use, pragmatic analytics solutions that Customers understand and care about.

Fixing our CRM data hygiene should be a top leadership priority to drive sales productivity and revenue growth. However, for many B2B environments particularly in Manufacturing and Wholesale, there’s often a big disconnect between strategy and execution. Companies spend a disproportionate time and investment on market research studies to understand their buyer archetypes and personas, only to stop at great Power Points, executive updates and cross-functional pontifications.

Meanwhile, the CRM systems are plagued by outdated and missing data and no value- or needs-based segmentation information, which lays the foundation for automated lead scoring or prescriptive capabilities like upsell, cross-sell or churn mitigation.

Now is the time to act and start treating our holistic CRM data with the attention and priority it deserves!

In a previous article, I wrote about the three main structural challenges that data scientists and their organizations face when maximizing their career satisfaction and business impact (data ROI).

This edition of Revology Analytics Insider will dive deeper into the first impediment ("mismatch between data scientist aspirations and corporate reality"). We'll decompose why it exists and make concrete recommendations to the data science community and company leaders on how to best address it.

Data science, AI and ML have been overhyped for at least the last decade, resulting in often unrealistic and misaligned expectations between data scientists and employers. Over the next couple of weeks, I shed light on the three major challenges data scientists typically encounter in their companies and provide concrete suggestions on how to tackle them for both personal and organizational success. Would love to hear from you about your experiences!

Last week I wrote about the key tenets for building analytics teams for real, measurable impact in your organization. This week, I’ll focus on one of the four fundamental #datamonetization strategies that companies should employ: capitalizing on their data assets to deploy #operational improvement initiatives that drive cost savings, revenue increases or both. Operational #optimization initiatives are usually a good place for companies to start their #analytics journey, assuming some foundational data capabilities are already in place: reliable internal data, decent #datagovernance and tech stack, a good understanding of customer behavioral profiles and foundational #datascience capabilities.

Read about key analytics use cases across three industries that optimize operational processes to drive real performance. If you have your own analytics use case stories from the trenches (successes or lessons learned), or just want to chat analytics, machine learning or revenue management, drop me a note.

Most analytics transformation efforts do not deliver a positive ROI for the enterprise even after several years. CEOs and their boards know they need to execute an AI-led differentiation strategy to either future-proof themselves, address an existential risk in the marketplace, or simply to make some operational improvements to their business. Yet, according to most estimates, 75-90% of digital transformations fail and less than 1 in 5 companies have fully extracted value out of their analytics journey. Most organizations fail on the last-mile delivery – in other words, front-line employees and decision-makers are not using the analytics tools and processes as intended, or not using at all.
Over the last decade of leading analytics teams, I have succeeded and failed many times. Below are five lessons learned from these experiences. I hope it can serve as a helpful 10K foot roadmap for nascent or aspiring analytics leaders or seasoned business executives who want to build a sustainable practice that adds real, quantifiable value for their company and its customers.