How Clean Product Data Can Become a Differentiator for Distributors

Imagine if a baseball team only kept track of their players’ height, weight, and jersey numbers while ignoring their actual output. That’s basically what distributors do when they view product data as merely SKUs and specs. 

Just as baseball teams dig into advanced statistics to determine who they should sign, distributors should dig into product data to determine which products are the most profitable and strategically important. These insights can improve their operational efficiency and enhance customer experience. 

Of course, that’s easier said than done. Even if a distributor understands the value of data, they may take one look and get spooked by how messy it is. But you can’t afford to look away. 

The Product Data Management Problem

Product data can be messy. Here’s why:

• Inconsistent naming conventions. For example, the same product may be named “¾-inch ball valve” in one spot and “Valve – ¾ inch” in another.
• Duplicate SKUs or entries. A product duplicated in the system, each with a different SKU, makes inventory and sales reporting inaccurate.
• Missing or incomplete descriptions. This includes product entries with no specs, dimensions, materials, or usage information.
• Units of measure conflicts. One system might list a product in cases and another in eaches, for example.
• Legacy data from acquisitions. Data inherited from acquired businesses may have different formats, structures, or standards. That compounds an already messy problem.
• Incorrect or outdated supplier information. This may include misspelled or outdated vendor names.
• Incorrect category tagging. A product may be miscategorized, such as putting a ball valve under electrical supplies, or lack the key attributes needed for better search.
• Poor image and document management. When photos, videos, spec sheets, or other documents are missing or outdated, that reduces the quality of the customer experience.
• Inconsistent part numbers. Differences in part numbers across vendors and distributors can prevent proper matching.

Instead of focusing on tracking down and fixing problems, your team should be focused on tasks that produce real business value.

Using Clean Data

Product data holds value – a lot of it, in fact. If you go through the not-so-simple process of cleaning it and centralizing it, you set yourself up for a significant return on investment. Here are just a few use cases:

Vendor inventory visibility

With the supply chain remaining unpredictable and real estate at a premium, distributors need to reduce their carrying costs and use their warehouse space as efficiently as possible. One way to do that is by gaining visibility into your suppliers’ inventory levels. When you know a supplier has enough of a particular product, you can wait until your customers need it to order it, thus expanding your ready-to-go inventory without having to expand your available physical space. 

Holding costs go down, and your customers benefit from a more efficient fulfillment process.

One major distributor working with ProfitOptics is doing this to fulfill orders faster, reduce on-hand stock, and better partner with suppliers who want a front-row seat at the point of customer demand.

Post-M&A product matching

M&A helps distributors reach new markets and expand their customer base. But there’s a downside. The more companies brought into the fold, the more product data variation you have to deal with in the aftermath. 

The same item could go by five different names, and that can create kinks in sales and fulfillment. It also makes it more difficult to track product-specific sales figures; a product that’s moving well could seem like it’s underperforming because the data is split. 

Distributors with clean, centralized data can use AI-powered tools to find redundant listings and match the product names, descriptions, and codes. We’ve worked with distributors where the same product had three descriptions in three systems. Using AI across 18+ variables, we hit 99% match confidence and then looped in a human for validation.

Laying the groundwork for AI and Analytics

The true value of clean data isn’t just efficiency; it’s readiness. With centralized, standardized data, distributors can start using AI for demand forecasting, customer segmentation, rebate optimization, and more. If you don’t structure your data now, you’ll miss out on tomorrow’s opportunities.

Managing manufacturer updates

Just as thorough spring cleaning makes it easier to keep your house tidy the rest of the year, a proper data cleanse makes it easier to clean and maintain that data over time. When your data is messy and a manufacturer updates their pricing, it’s a headache for the team tasked with managing those changes.  

But when your data environment is set up to automatically incorporate changes, everything tilts in your favor. Obviously, this constitutes table stakes, but clean data is the difference between struggling to keep up and turning that process into a real edge over the competition. 

Building a Foundation for Data-Driven Strategy

Baseball seasons are long, and teams have to make decisions in March that have implications in October. When it comes to data, distributors need to play the long game, too. You won’t be able to clean everything up overnight, and data maturity is a journey that requires a unified vision and a concerted effort from all stakeholders.

The first step is to begin building a foundation upon which you’ll build a long-term data strategy. That might mean working with a data partner like ProfitOptics to de-silo and normalize your data. It could mean tackling smaller projects to demonstrate the usefulness of clean, organized data, and then using those results to drive toward a bigger overhaul of how your company collects and uses its data. 

We’ve helped dozens of distributors chart a course for data maturity. Take your first step and reach out to our expert team today. Wherever you are in your journey, we can help you keep moving forward in meaningful ways. 

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