Executive Summary

Technological advancements are rapidly reshaping industries, and distributors are at a critical juncture. The traditional models that once guaranteed steady growth and profitability are no longer sufficient. Slow market conditions, inflationary pressures, and global disruptions like the COVID-19 pandemic have exposed vulnerabilities in conventional strategies. Despite valiant efforts to navigate these challenges, many distributors are witnessing growth rates falling short of internal expectations and industry benchmarks.

Our latest whitepaper explores the main reasons for this lack of progress, pinpointing key areas where revenue loss happens or revenue growth is stymied: gaps in price realization, customer churn, and missed opportunities in cross-selling and up-selling. We emphasize that conventional tools and methods are not enough to tackle these complex issues, mainly due to problems with data quality, large volumes of data, limited advanced analytical resources and not enough headcount dedicated to Pricing and Revenue Management initiatives.

The allure of Artificial Intelligence (AI) and Machine Learning (ML) as the next "shiny objects" can be tempting, but merely adopting these technologies without a solid foundation is a recipe for disappointment. While AI and ML have become table stakes in modern business, their true value lies in pragmatic applications that drive tangible, profitable growth. These capabilities enable the development of smart Revenue Growth Management (RGM) strategies, dynamic pricing models, predictive analytics for customer retention, and highly targeted marketing capabilities.

With these central topics in mind, we wrote this whitepaper to be a comprehensive guide for distributors leveraging Advanced Analytics and AI/ML in their RGM strategies to overcome growth obstacles.

Partnering with Pricing Lever, a specialist advisory firm in Distributor Pricing and Revenue Growth Management (RGM), we aim to guide you through building capabilities that keep pace with technological advancements and strengthen the foundations of your business for sustainable success.

Introduction

The distribution industry is at a critical turning point. Market saturation, increased competition, and rapidly evolving customer expectations have created a complex landscape that requires agility and innovation. Distributors have shown remarkable resilience over the past decade, effectively managing supply chain disruptions, fluctuating demand, and the need to pass on price increases due to inflation and other economic pressures. The COVID-19 pandemic further tested the industry, introducing unprecedented challenges that required quick and decisive action.

Despite their efforts, distributors are facing a perplexing paradox. Many of them are experiencing slower growth than expected and falling behind industry benchmarks. This stagnation is especially concerning at a time when data is abundant and technological advancements should provide a competitive advantage.

The disconnect suggests that traditional business practices and analytical tools are not addressing the underlying issues.

Distributors must rethink their strategies in a market with slim profit margins and fleeting customer loyalty. It's imperative to identify and tackle the hidden obstacles that are hindering growth. This requires going beyond quick fixes and addressing the systemic issues eating into profitability and hindering expansion.

The main goal of our whitepaper is to highlight the key challenges blocking profitable growth in the distribution sector and offer effective solutions that have been pressure-tested by prior client engagements. By examining key areas where net revenue loss is most significant, we aim to provide a data-driven plan for overcoming these obstacles.

We suggest using advanced analytics powered by AI and ML as the foundation of this transformation. These technologies can process and analyze large amounts of data, uncovering patterns and insights that were previously inaccessible. By enabling dynamic pricing strategies, predictive analytics for customer behavior, and personalized marketing, AI and ML empower distributors to make strategic decisions that are both timely and precise.

Our whitepaper offers a detailed guide on implementing advanced Revenue Growth Management strategies and discovering your hidden revenue potential. It provides practical steps for adopting AI and ML-driven approaches, addressing challenges such as data quality and organizational change management, and measuring the impact on profitability and growth.

Understanding Revenue Growth Challenges

For distributors across most industries, the main focus is often on large-scale strategies such as entering new markets, diversifying product lines, or acquiring competitors. While these initiatives are crucial, they can sometimes overshadow the smaller yet significant challenges that can gradually reduce profitability from within - a phenomenon similar to "death by a thousand cuts." These challenges mainly arise in three critical areas:

Gaps in price realization
Customer churn
…and missed opportunities in cross-selling and up-selling

***With "stretch" growth objectives, most organizations struggle with finding hidden pockets of value.***

Gaps in Price Realization

Price realization refers to a company's ability to capture net revenue growth due to pricing actions, including list price, discount, or promotion optimization. In the distribution sector, the sales process is often decentralized, with individual sales representatives making pricing decisions on the spot. To close deals or satisfy customers, they may offer unplanned discounts and concessions. Although a 1% discount may not seem significant on its own, when applied across numerous transactions, it can significantly reduce profit margins. For example, a 1% net price realization can lead to a 7-10% increase in operating profit dollars, as highlighted by a McKinsey study on the impact of net price realization on operating profits.

It is important to remember that price is the only lever that benefits both the top and bottom lines simultaneously, making it a powerful vector for driving company earnings.

***Pricing is the most impactful business lever to impact your Operating Profits. On average, a 1% price realization drives an 11% improvement in Operating Profit $.***

In addition, during periods of cost inflation and deflation, missed opportunities to pass on cost increases (or slow down the rate of price concessions) can further accelerate the price gaps, leaking a tremendous amount of value.

Furthermore, without reliable price elasticity models, these concessions are often made without a clear understanding of their impact on overall profitability. Distributors may unintentionally train customers to anticipate discounts, weakening future pricing power and further squeezing margins.

You need to regularly measure the Gross Profit $ impact of your Net Price Realization, with dynamic drill-downs into drivers of good/bad performance and straightforward scenario analyses (i.e. what happens to Revenue and GP$ if I increase prices by X%?)

A universal framework that helps us determine where to price, upsell /cross-sell or nurture. “Dead Zone” and “Light Sellers-Ligh Margins” are two easy areas primed for price increases. “Top Sellers-Bad Margins” will carry the largest GP$ upside during price increases, but execution must be done surgically, relying on customer-level price elasticity information.

Customer Churn

Customer churn, the rate at which customers stop doing business with a company, can significantly impact profitability. It's not just about losing a sale; it's about losing out on future revenue streams and the cost of acquiring new customers to replace those lost. Distributors often lack the tools to detect early warning signs of customer defection. Traditional reporting and CRM systems may only flag a customer as lost after they've stopped purchasing altogether, like closing the barn door after the horse has bolted.

Customers may show signs of dissatisfaction long before they churn by reducing order sizes, increasing the intervals between purchases, or making more complaints. Without predictive analytics to identify these patterns, distributors miss the opportunity to intervene and retain valuable customers.

***By integrating various internal and external data points, you can build highly effective Machine Learning models that predict which customers are at risk of churning in the next 30, 60, or 90 days.***

Missed Cross-Selling and Up-Selling Opportunities

Existing customers represent a goldmine of untapped revenue potential. However, the failure to effectively cross-sell (selling complementary products) and up-sell (encouraging the purchase of higher-priced products) is a significant missed opportunity. Sales representatives might focus on what they know best or what they've always sold to a particular customer, neglecting to explore the full range of products that could meet the customer's needs.

For distributors, this is an essential component of business for two reasons:

Distributors typically carry many similar products, yet they may have different customer pricing and internal profitability.
Distributors that effectively manage their assortment and selling strategies have designed GGB (Good-Better-Best) techniques. These techniques help guide customers towards higher-value products, which often yield stronger financial results for the distributor as well.

Without systematic approaches and tools to guide them, these opportunities slip through the cracks. Many distributors rely too heavily on the expertise of their sellers; however, with assortments becoming larger (thousands, if not millions, of products), it’s impossible for sellers to consistently provide this service. This leads to inconsistent customer experiences and missed sales opportunities. Not only does this leave revenue on the table, but it also opens the door for competitors to step in and fulfill those unmet needs.

***Product Affinity Analysis, a popular analytical technique in Retail / Ecommerce, can be highly effective in driving proactive Cross-Sell and Up-Sell efforts in Distribution.***

Data Challenges

Data is often likened to the new oil, with its true value only realized when it can be effectively processed and analyzed. However, several challenges hinder its utilization.

Data Cleanliness

Data quality is the foundation of any analytical capability building. Inaccurate, inconsistent, or incomplete data can lead to flawed insights and misguided strategies. Distributors often face data silos, where information is stored in separate systems that don't communicate with each other. This fragmentation leads to duplications and discrepancies that undermine confidence in analytical outputs.

For example, a customer might be listed under different names or codes in separate databases, leading to confusion and misaligned marketing efforts. Cleaning and standardizing data is a daunting task but essential for accurate analysis.

Product data is another critical challenge for many distributors, as they must manage numerous suppliers, each providing different attributes for their products, along with a constantly evolving product assortment.

Data Volume

The sheer volume of daily data, from sales transactions and customer interactions to scraped competitor prices and supply chain metrics, can be overwhelming. Traditional analytical tools are ill-equipped to handle such massive datasets, resulting in the underutilization of valuable information. Important patterns and trends remain hidden within the data deluge, inaccessible to decision-makers who need them most.

In addition, particularly for distributors that primarily sell over the internet, the speed of the transactional vortex can be a double-edged sword: on one side, it allows for improvements to quickly benefit the distributors’ bottom line. Conversely, ill-conceived and poorly tested price changes can quickly manifest into steep losses for the business.

Limited Analytical Resources

Many distributors lack the specialized expertise required to analyze complex data effectively. Data scientists and advanced analytical platforms are expensive investments; without them, even data-rich organizations struggle to extract meaningful insights. This skills gap results in missed opportunities to optimize pricing, improve customer retention, or tailor marketing strategies effectively.

In addition to data experts, the remainder of the organization needs to enable the implementation of key findings. This requires teams to collaborate closely and bring data insights to life, utilizing thorough testing and planned implementations.

The AI and ML Advantage

Artificial Intelligence and Machine Learning are transforming Commercial Analytics and Revenue Growth Management capabilities. They provide robust solutions to distributors' data challenges, turning obstacles into opportunities for growth and competitive advantage.

AI algorithms are highly effective at managing large and complex datasets. Machine Learning data pipelines and models can automate data cleaning, normalization, and integration processes, ensuring that analytical efforts are based on accurate and consistent data. For example, AI can reconcile customer records from different systems, identify duplicates, and consolidate information into a unified profile.

One of AI and ML's key benefits is their ability to identify complex patterns and relationships within data that may be beyond human capacity to detect. These models can accurately predict future customer behavior, market trends, and price sensitivities by analyzing historical data. For instance, predictive models can forecast which customers are at risk of churning and why, enabling distributors to address their concerns proactively. Similarly, AI can predict how price changes will affect demand for specific products, leading to more effective pricing strategies.

AI-driven tools transform raw data into valuable insights by highlighting correlations, anomalies, and trends. These insights inform strategic decision-making, enabling organizations to identify areas for improvement, capitalize on emerging opportunities, and mitigate potential risks.

For example, AI algorithms may reveal that customers who purchase a specific product are also likely to buy a complementary item. Sales teams can use this insight to bundle products or recommend add-ons, increasing sales volume and enhancing customer satisfaction.

AI/ML Driven Pricing Strategies

Since pricing is clearly a very powerful and often underutilized lever, let’s examine a few key applications of AI/ML to benefit this lever:

Price Optimization
Dynamic Pricing
Competitive Pricing Analysis
Customer Churn Prevention
Predictive Cross-Selling and Upselling
Customer Segmentation
Customer Life Time Value Optimization
Discount Management and Effectiveness

Learn more about overcoming growth obstacles in Distribution. Download our whitepaper, Overcoming Growth Headwinds - AI/ML-Driven Strategies for Revenue Optimization in Distribution, to gain actionable insights and advanced strategies that drive profitable, sustainable growth

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