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Manufacturing & Distribution

Leveraging Machine Learning to Uncover End-User Opportunities

Author
Chris Meggs - Alexander Group, Inc.
Chris Meggs Manager

Using Machine Learning to Model the SAM and Empower Manufacturers

Manufacturing leaders are under growing pressure to identify new paths to growth, especially when it comes to capturing more share of wallet (SoW) from hard-to-reach end users. One powerful tool that forward-thinking organizations are turning to is machine learning. By combining data science with practical sales and marketing strategy, manufacturers can begin to unlock visibility into demand signals that were previously buried behind layers of distribution. This article explores how machine learning can be used to model the serviceable addressable market (SAM) and uncover actionable insights that empower manufacturers to take smarter, more proactive steps toward revenue growth.

For manufacturers, understanding customer demand and SAM, including realistic wallet size, is critical for expanding SoW. Broadly speaking, the manufacturing sector focuses on producing goods while the distribution sector ensures those goods reach end users. This interdependence creates a complex ecosystem in which the performance of one sector directly affects the other.

Manufacturers rely on distributors to bridge the gap between production and consumption. Distributors, in turn, count on manufacturers to deliver high-quality products that meet market demands. This symbiotic relationship has historically ensured a steady flow of goods and high customer satisfaction. However, changing market conditions, including increased consolidation among distributors and the growing commoditization of manufactured products, have destabilized that balance.

As a result, manufacturers are increasingly looking to better understand end users and their specific demands. But projecting end-user demand remains difficult due to the opaque nature of the distribution layer. The presence of intermediaries often clouds visibility into customer preferences, limiting a manufacturer’s ability to assess the full market opportunity.

This is where machine learning becomes a game-changer. Advanced modeling techniques allow companies to dig below the surface and analyze customer behavior at the end-user level. These insights help manufacturers directly engage high-priority accounts with targeted marketing and sales efforts, strengthening brand awareness, arming sellers with sharper intelligence and ensuring distributors are prioritizing their products where appropriate. Importantly, this isn’t about disintermediating distribution—it’s about enabling better collaboration through better data.

Distributors, too, can use SAM modeling to better segment customers and refine their account strategies. In the sections below, we outline how machine learning can be used to model the SAM in the manufacturing sector and the tangible benefits it can deliver.

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Machine Learning Techniques for Overcoming Distribution Layer Challenges

 Machine learning offers a range of techniques that help manufacturers and distributors overcome the limitations of the distribution layer. These methods make it possible to uncover customer insights and identify high-value opportunities that might otherwise remain hidden. Here’s a six-step process for building and implementing a machine learning–powered SAM model:

1. Align on Goal and KPIs

Begin with a clear understanding of the business goal, in this case, uncovering end-user demand to drive SoW growth. This objective informs what data is needed, which stakeholders must be involved (typically sales and marketing leaders) and what success metrics to track. Relevant KPIs might include SoW growth percentage, seller productivity and percentage growth in net-new end users.

2. Establish a Data Foundation

A robust data foundation is essential. Manufacturers should aggregate data from all available sources, including indirect and direct sales records, distributor performance data and any available point-of-sale (PoS) or distribution data shared by partners. This internal data should be enriched with external firmographic information (e.g., annual revenue, subindustry, employee counts) and relevant market reports.

Data quality and consistency are critical. All records should be unified via shared account IDs and thoughtful fuzzy matching to create a single source of truth. This foundational step ensures that the SAM model is built on accurate, comprehensive data.

3. Potential Modeling

Spend Potential models estimate a realistic dollar-value wallet size for both current customers and prospects. This should include models for both known current customers and prospects.

Current Customer Modeling

Using the unified dataset, the model applies a combination of clustering and regression techniques to identify look-alike customer groups. At a high level, the SAM model is a look-alike model that looks across all the data to determine what customers are most similar, and within those look-alike groups project out potential based on the purchasing behavior of the top performers within each group. Distributors and end users must be modeled separately due to their distinct characteristics. Automation enables this process to scale across many product and industry combinations.

Prospect Modeling

The current customer model is then leveraged to identify and prioritize new prospects. This includes identifying high-frequency industry codes (e.g., NAICS or SIC), setting firmographic thresholds (e.g., revenue, headcount) and building a look-alike forecast.

For example, if Company A is a food and beverage manufacturer with $500M in revenue and 1,000 employees and has a projected wallet size of $10M, then a similar Company B with $400M in revenue and 800 employees might be projected at $8M. While the actual modeling occurs at a more granular, product-category level, this illustrates the approach.

4. Stakeholder Engagement

SAM modeling success depends on involving the right stakeholders early and often. Sales, marketing, product, revenue operations and executive leadership all contribute essential insights. Engaging them not only improves model accuracy through institutional knowledge but also builds buy-in, which is critical for driving adoption.

5. Implementing the Model

Once validated, the model must be integrated into core systems and workflows. This includes embedding insights into CRM platforms, setting up data refresh cycles and activating sales plays. End-user prospect lists can be shared with sales to coordinate with distributors on joint targeting efforts. Aligning implementation with broader business planning ensures the model drives impact across teams.

Ongoing measurement and optimization are vital. KPIs should be continuously tracked and the model retrained with new data as it becomes available. This ensures the approach remains relevant and high-performing over time, even as market conditions change.

Machine learning provides manufacturers with a scalable, data-driven method to break through the opacity of the distribution layer and get closer to end-user demand. Compared to traditional methods, these models are faster, more precise and continuously adaptable. Research from Alexander Group shows that manufacturers investing in commercial data science capabilities saw an additional 4.5% year-over-year growth compared to peers.

As manufacturers look for new ways to fuel growth in a shifting market, SAM modeling offers a powerful and practical way forward. By unlocking visibility into true market potential and helping manufacturers and distributors align more strategically, machine learning paves the way for smarter decisions and stronger performance.

Need more information?

For more insights on how to leverage machine learning for your organization, contact Alexander Group today.

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