Editor’s Note: This article is a summary of the Distribution Strategy Group webinar “Using State of the Art Analytics to Understand Performance,” held on November 5, 2025. The live panel brought together leading technology and analytics executives to explore how advanced analytics is transforming performance management and strategic decision-making across distribution and manufacturing.

The discussion covered which analytic models and tools are driving measurable business outcomes, how to embed analytics into daily workflows and decision loops, and best practices for data governance and adoption. Panelists also shared real-world examples and frameworks for advancing analytics maturity—from foundational use cases to enterprise-wide integration.

For those who couldn’t attend, on-demand access is available to all registered participants.

 The wholesale distribution industry sits on a goldmine of data that most retailers would envy. Unlike consumer-facing businesses that struggle to identify their customers, distributors know precisely who bought what, when, and at what price. Yet many distribution leaders hesitate to fully harness this advantage, often citing concerns about data quality or readiness. That hesitation is becoming increasingly costly as artificial intelligence (AI) and advanced analytics reshape competitive dynamics across the industry.

According to Kristen Thom, senior vice president of customer experience at White Cup, the foundation of effective analytics starts with building robust data sets that pull from multiple sources across the business. This means integrating information from enterprise resource planning (ERP) systems, ecommerce platforms, marketing tools, and customer service interactions into a unified view. The goal isn’t just collecting data but making it accessible and actionable for distinct roles throughout the organization.

Brian Hopkins, chief operations officer at Distribution Strategy Group and a veteran of major distributors including Grainger and HD Supply, emphasized that the analytics landscape has shifted dramatically from retrospective reporting to predictive insights. The question is no longer about what happened last month but what will happen next and what actions leaders should take in response.

Moving Beyond the Perfect Data Myth

One of the most significant barriers to analytics adoption in distribution is the misconception that data must be perfect before any analytics initiative can begin. Thom addresses this concern directly, noting that the pursuit of perfect data is a state that organizations will frankly never reach. Every business has inconsistencies in its data, but modern analytics tools have become sophisticated enough to surface these issues quickly and identify outliers effectively.

The recommendation from industry experts is clear: get started now rather than spending a year trying to clean data before implementation. Analytics tools themselves help identify where data problems exist, making the cleanup process more manageable and targeted. Waiting for perfect data means missing opportunities that could be captured today with good enough information.

Hopkins reinforced this point by highlighting the importance of taking data quality seriously while avoiding paralysis. Organizations should establish a sole source of truth and lock down data governance, but they should start with small automation projects to validate their approach rather than launching massive initiatives that might fail halfway through due to unforeseen data issues.

From Descriptive to Prescriptive Analytics

The evolution of analytics in distribution has progressed through several stages. Descriptive analytics told leaders what happened, diagnostic analytics explained why it happened, and predictive analytics forecast what would happen next. Today, the industry is moving toward prescriptive analytics that recommend specific actions and, increasingly, toward agentic systems that will execute tasks autonomously based on data insights.

This progression demands more than simply better technology. It requires organizational trust in data and in the leaders who interpret and act on it. Hopkins identified two critical types of trust that must exist: trust in the accuracy of the data itself and trust in the leadership presenting and interpreting that data. He recalled running a $230 million district at Grainger without access to margin data, illustrating how some organizations still restrict information flow even to senior leaders.

Building trust requires leaders to let go of data hoarding practices and embrace transparency. It also means suspending long-held beliefs when data reveals unexpected insights. Veterans of the industry often resist findings that contradict their experience, but Hopkins stressed the importance of balancing that experience with openness to new patterns the data might reveal.

Practical Applications Driving Results

The most successful analytics implementations focus on solving specific pain points rather than trying to transform everything at once. Thom highlighted quote follow-ups as an excellent starting point. By identifying quotes in the ERP system that exceed a certain age and automatically triggering follow-up actions through the CRM, distributors can capture revenue that might otherwise slip away. This kind of automation removes time-consuming manual tasks while ensuring consistent customer communication.

Customer dashboards that provide sales representatives and customer service teams with comprehensive views of each account deliver particularly strong returns. These dashboards should include current quotes and order history, but they need to go deeper. Effective dashboards reveal what customers aren’t purchasing, identify products they bought more of last year compared to this year, predict when they’re likely to order again based on historical patterns, flag outstanding accounts receivable issues, and display profitability metrics for the relationship.

Hopkins shared a compelling example from his experience where analytics enabled customer service representatives to move from reactive order taking to proactive selling. Instead of the marketing team telling reps to push whatever inventory was overstocked, the system identified specific products that individual customers were likely to need and flagged when customers who typically ordered on five-week cycles hadn’t placed an order in six weeks. This targeted approach helped the inside sales team outperform expectations by 12%.

The impact was so significant that Hopkins had to conduct a double-blind statistical study to convince skeptical leaders who didn’t believe the results. Even with statistical proof, some executives remained doubtful, illustrating how deeply organizational resistance to data-driven insights can run.

The Mobile-First Analytics Revolution

One distributor Thom worked with established a simple but powerful guiding principle for their analytics rollout: if the information couldn’t be accessed and understood from a mobile phone, it wasn’t helpful. This approach recognized the reality of how outside sales representatives work. Asking field salespeople to pull out laptops and navigate complex spreadsheets simply doesn’t match their workflow. By delivering analytics through mobile-friendly dashboards with clear graphics and charts, this distributor significantly improved adoption rates.

The principle extends beyond device compatibility to information design. Analytics must be delivered in digestible chunks appropriate for each role. Sales representatives need different views than purchasing managers, who need different information than branch managers. Role-based access prevents data overwhelm while ensuring everyone gets the specific insights they need to excel in their position.

Overcoming the Fear Factor

A curious resistance pattern emerges when distributors consider implementing new analytics capabilities: the fear of looking bad. Hopkins identified this as fundamentally an ego issue, where leaders worry that data-driven insights might reveal what they don’t know or expose operational weaknesses. This creates a tension between curiosity and judgment.

The most successful organizations cultivate cultures where facts are treated as friends rather than weapons. Data becomes a learning opportunity rather than a cudgel for punishment. Thom noted that distributors who focus on giving more to their teams than they ask in return achieve much higher adoption rates. When CRM and analytics systems provide genuine value to sales representatives rather than simply demanding more data entry and reporting, enthusiasm follows naturally.

The cultural shift requires acknowledging that human brains simply cannot process the massive data sets that modern distribution generates. Even considering just the thousands of customers, thousands of SKUs, and thousands of price points that typical distributors manage, the permutations become overwhelming. Analytics tools and AI handle pattern recognition across these large data sets, freeing human judgment for relationship building and strategic decision-making.

Preparing for the Agentic Future

The next wave of analytics will feature software agents that don’t just recommend actions but execute them autonomously. These agents will interface with customers, manage marketing campaigns, optimize pricing, and handle numerous other tasks currently performed manually. This agentic AI future, likely to arrive within three to five years, will provide enormous productivity and service advantages for early adopters.

Getting ready for this future requires action today. Organizations need analytics foundations in place, teams comfortable using data in daily decisions, and tight integration between analytics systems and CRM platforms. Much of the agentic activity will flow through customer relationship management tools, making the integration between analytics and CRM even more critical than it is today.

Hopkins and Thom both emphasized starting with small automation projects now. These early wins build organizational capability and confidence while surfacing data quality issues that need attention. As teams develop their analytics muscles through incremental improvements, they position themselves to leverage more sophisticated capabilities as they become available.

The Ownership Question

Who should own analytics in a distribution organization? Historically, data initiatives fall to information technology (IT) departments, but the most successful companies are shifting ownership to business leaders who understand customer needs and business outcomes, not just technical architecture. This might be someone in sales, marketing, or operations who can connect data insights to actual business motions and results.

Thom suggested this owner needs broad understanding across the business, not deep technical expertise. Their role is ensuring data integrity, establishing a single source of truth, defining how data should flow through the organization, and connecting analytical insights to the roles and decisions where they matter most. Without clear ownership, data quality remains nobody’s responsibility and analytics initiatives stall.

Key Takeaways for Distribution Leaders

The message from analytics experts is straightforward: the time to start was yesterday, but today is the next best option. Leaders should abandon the pursuit of perfect data and instead focus on getting started with manageable projects that deliver quick wins. Begin with pain points like quote follow-ups or customer retention analysis where the business case is clear and the risk is low.

Establish someone as the owner of data quality and analytics strategy, preferably a business leader who understands customers and operational realities. Create role-based views that deliver relevant insights without overwhelming users. Build organizational trust by treating data as a learning tool rather than a weapon and by demonstrating leadership curiosity rather than defensiveness when data reveals uncomfortable truths.

Start implementing small automations that help teams act on insights rather than just viewing reports. Use analytics to drive meeting agendas and decision-making processes, reinforcing their value through consistent application. Most importantly, recognize that competitors who build strong analytics capabilities now will have decisive advantages when agentic AI tools arrive soon.

The wholesale distribution industry’s data advantage is real, but it only matters if that data gets transformed into insight and action. The distributors who win in the next decade will be those who started building that capability today.