More data, more problems?
In a world obsessed with big data, it’s easy to assume that more information equals better decisions. But for distributors, that’s not always the case.
In reality, most distributors don’t need big data to make strategic moves — they need smart data.
Jonathan Bein of Distribution Strategy Group recently sat down with Greg Hartunian, director of sales at Epicor, and Justin Johnson, founder and CEO of Motivate, to discuss how distributors can maximize the value of their existing data.
Their key takeaway? You don’t need massive datasets to make smarter business decisions. When analyzed effectively, structured data provides all the insights necessary to streamline operations and drive profitability.
If you missed the webinar, you can watch it on demand here.
Here are three tips for unlocking the potential of your existing data and using it to benefit your business.
1. Focus on the data you already have.
Big data sounds impressive, but for most distributors, bigger isn’t necessarily better. The key to smarter decision-making isn’t collecting more data. It’s knowing how to use the data you already have.
A smaller distributor may have 25,000 SKUs and a thousand customers, but not all that data is relevant for analysis. Just a fraction — typically a few thousand active SKUs — accounts for recent sales.
The real challenge isn’t dealing with massive data sets but effectively analyzing the most meaningful data points. Many companies struggle to do this efficiently, missing valuable opportunities for optimization.
Take sales data, for example. Understanding what you’ve sold and who you’ve sold it to is fundamental to driving sales. This core information is likely already housed in your enterprise resource planning (ERP) system, but if it’s not being analyzed effectively, it’s just sitting there instead of working for your business.
By focusing on existing insights — in this case, structured sales data — distributors can identify purchasing patterns, anticipate customer needs, and take a more proactive approach to sales.
As Johnson explains: “If you want to better serve a customer or grow your business, you isolate who bought what when, and then you start to run some patterns and trends around that data so you can start to make sense of anticipating needs, showing up before the customer even calls – making sure you’re not cold calling – and selling them a product.”
The right analysis can transform everyday transactions into actionable insights, helping distributors optimize operations, strengthen customer relationships and ultimately drive more revenue.
2. Cluster customers to drive better decisions.
Keep in mind that not all data points carry the same weight. For example, focusing on a customer who purchased a single product a decade ago provides little actionable insight. The real value lies in identifying repeat customers — understanding who they are, where they are and their purchasing patterns.
By analyzing this data, distributors can not only strengthen relationships with existing customers but also identify new prospects with similar characteristics, creating more targeted and effective sales strategies.
“There are machine learning clustering algorithms that will look for common items that have a common set of characteristics in their demand,” Hartunian said. “And if you can identify those, you can also apply those same types of logic to the types of customers that may make a better fit for your product.”
This is where artificial intelligence (AI) becomes a game-changer. Rather than manually identifying patterns, AI can analyze customer data, detect shared characteristics and scan to find similar prospects.
By automating this, distributors can reduce reliance on sales teams to piece together insights, ultimately enabling smarter, more efficient customer targeting.
3. Apply predictive analytics for smarter inventory planning.
Panelists agreed that, historically, most ERPs have focused more on execution. These systems effectively manage inventory by balancing current orders with available supply, ensuring products are shipped as needed. However, they fall short in forecasting demand and accounting for things like seasonality and growing or waning trends.
While ERPs can alert distributors when stock is running low, many lack the predictive capabilities to anticipate what customers will buy next, leaving a gap in proactive inventory management. That’s a missed opportunity for distributors that want to operate more efficiently and improve the customer experience.
Effective demand planning goes beyond simply executing order suggestions from an ERP system. These recommendations are based on planning parameters like forecasts, safety stock levels and reorder points — all of which directly affect inventory and order schedules.
Because demand is inherently variable and often exceeds forecasts, businesses need optimized inputs to ensure reliable outputs. This ensures that automated order suggestions align with actual business needs, reducing the need for manual intervention and guesswork. That reduces stockouts and avoids excess stock, while keeping customer satisfaction high.
“I think the number one benefit that AI can provide in the context of inventory planning is beyond simply picking up a trend or a seasonal pattern. It’s grappling with the fact that there’s uncertainty,” Hartunian said.
Access additional insights by watching our on-demand webinar here.
