Dive into the comprehensive "2023 Revenue Growth Analytics Maturity Report" with our detailed webinar. This session covers key industry capabilities, strategic insights, and best practices in Margin Analytics & Optimization, Promotion Effectiveness, and Sales & Customer Growth Analytics.

One of the critical mistakes earlier in my Revenue Growth Analytics career was relying on internal experts to inform the building of "Price-Value Maps" (aka. "PVM") for our Products and those of key Competitors. It proved to be a costly mistake, and we ended up eroding Gross Profits and EBITDA as all of our Price List increases were heavily offset by increased Rebates, Promotional and Shopper Marketing spend.

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.

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.

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.

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.

Below is a quick guide summarizing a previous Revology Analytics article that demonstrates why healthy Customer Data is crucial to enabling powerful Sales Growth Analytics for companies. It also highlights steps you can take today to translate your CRM data into Revenue & Margin Growth.

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!

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.