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An Inside Look at B2B Data Supply Chain
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An Inside Look at B2B Data Supply Chain

It's time to lift the curtain on the B2B data supply chain and take a real look behind the scenes.
Author
Primer team
Updated on
February 6, 2024
Published on
December 20, 2023

Table of Contents

If data is the rocket fuel that powers marketing, sales, and revenue growth, then most B2B organizations are running on empty tanks. The world of business data is complex, messy, and downright confusing for many. There are so many misconceptions floating around; it's like a game of telephone gone wild. Vendors only show you the shiny bits that make their data look good, while the unbiased truth gets buried under all the noise.

But no more! It's time to lift the curtain on the B2B data supply chain and take a real look behind the scenes. In this no-nonsense guide, we'll uncover some hard truths that other providers won't tell you. We'll bust those widespread data myths and give you practical insights that can actually help you make smarter decisions.

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Peeling Back the Data Supply Chain

Data providers don't just magically create their own proprietary information out of thin air. The truth is that a big chunk of their data actually comes from third-party suppliers further up the supply chain. It's like there's a whole hidden network supporting the data economy. These providers source their information from specialized partners, either directly or through intermediaries, and then sell it under their own brand.

For example, let's take a tool like Clearbit. They likely get a lot of their firmographic data from sources like MixRank, Xverum, LiveData, and Oxylabs, which specialize in aggregating professional social media data. Maybe they also have their own data supply chain connected to LinkedIn scrapers. You might think 100% of Clearbit's data is all produced in-house, but if you dig a little deeper, you'll realize that they integrate data from other sources into their own offering. There's this intricate web of interconnectedness beneath the surface.

What this means is that switching from Data Provider A to Provider B doesn't necessarily mean you're getting completely different data in terms of breadth and depth. Much of it might originate from the same sources, just packaged and presented differently.

But here's the kicker: many brands have this strong gut feeling that Data Provider B is somehow "better" than Provider A without really understanding the underlying data supply chain that feeds both. They form opinions on data quality without actually knowing how these sources obtain their information.

When you lift the curtain, you realize that behind those fancy provider interfaces, there's a complex and shared data economy. Most providers do have some proprietary data, but they also heavily rely on third-party suppliers higher up the food chain. Understanding this whole ecosystem is crucial for accurately assessing vendors. But if you’re looking for a trusted data provider for sales outreach, try one of those from our TOP-10.

So, don't be fooled by the shiny surface. Get to know the real backstory of where your data is coming from and how it's being sourced. It's the key to making informed decisions and finding the best data solutions for your business.

Facing the Hard Truth About Imperfect Data

Once you take a peek behind the curtain at the complex data supply chain, an undeniable truth comes to light: no single provider can offer 100% accurate and comprehensive B2B data. Yet, many B2B organizations still cling to the notion that their data should be nearly perfect, leading to frustration when reality falls short of their expectations.

The hard truth is that business data is inherently messy. Even sources considered authoritative, such as LinkedIn, ZoomInfo, and Dun & Bradstreet, have their fair share of inconsistencies, gaps, and inaccuracies. Data is far from the flawless asset that many anticipate.

This means accepting imperfection and uncertainty as an integral part of utilizing data. Providers struggle with balancing information quality and completeness throughout their data supply chain. Some take a cautious approach, leaving fields blank when data is unconfirmed. Others populate fields with their best estimates, prioritizing completeness even if the information is uncertain.

Technical challenges also contribute to the complexity. Resolving conflicts between profiles involves intricate processes like entity resolution, which determines if records from different sources refer to the same company or contact. These matching methods often rely on error-prone techniques like similarity scoring algorithms or joins on fields prone to variation, resulting in gaps and inaccuracies.

As a result of these human and technical challenges, no data set can ever be perfect. However, embracing a certain level of imperfection allows teams to extract significant value if their expectations remain realistic. Instead of dismissing data supply chain outputs as useless, teams can carefully analyze them to filter out noise and focus on valuable signals. This nuanced perspective enables data-driven success.

The Myth of More Data Always Being Better

Many organizations mistakenly believe that the value of data lies solely in its total volume and the number of contacts or companies covered. They assume that massive databases with billions of records automatically provide superior insight. However, this thinking is a common misconception.

In reality, larger datasets filled with irrelevant contacts offer little incremental value while incurring higher licensing, storage, and management costs. Simply having more profiles does not guarantee better insights from your data supply chain.

The key metric that truly matters is the coverage and depth within your organization's unique ideal customer profile (ICP). Wide databases may boast huge volumes overall, but their size becomes inconsequential if they lack richness for your target accounts.

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For instance, consider Primer's raw database, which encompasses over 50 million companies. However, based on actual customer usage patterns, the active subset is just a fraction—around 3.4 million companies on average. Despite having over 50 million total profiles, the actionable coverage is significantly narrower.

This example clearly demonstrates the distinction between breadth and depth within an ICP. Additionally, the value derived from additional data follows the principle of diminishing returns. As overlaps increase, adding more data sources to the data supply chain escalates costs without substantially expanding relevant coverage.

Instead of fixating on total volume, B2B brands should prioritize acquiring the deepest, most accurate data specifically for accounts that match their ICP. Laser focus on quality surpasses the pursuit of big numbers when it comes to selecting data sources. Sometimes, less can be more if it delivers precise information that drives tangible business outcomes.

The Data Transition Trap: Hard Truths About Adding or Switching Providers

When deciding whether to add new data providers or switch between data supply chain sources, B2B brands need to consider two important factors:

  1. Incremental Value: Will the new provider actually bring significant improvements to customer coverage and data quality in the areas that matter most to our business, or is it just more of the same? It's essential to carefully assess whether the increase in actionable data is worth it.
  2. Transition Cost: What challenges will arise during the integration or migration process? Let's face it, no transition is seamless. There will always be some leakage, loss, or disruption to existing workflows. Switching data supply chain providers may even result in losing previously categorized data points if they use different classification systems. The migration costs should not be underestimated, even when adding sources to improve data incrementally.

It's easy for brands to get caught up in the excitement of potential added value without fully considering the integration challenges. That's why understanding the trade-offs involved in transitioning between data providers is crucial. Each addition or switch requires careful analysis, as compromises are inevitable.

Even when adding new providers, there needs to be a plan in place to link identifiers, accommodate classification differences, and ensure minimal disruption to existing workflows. It's important to note that no provider transition will perfectly preserve all firmographic history and linkages—there will always be some decay.

By shedding light on the hard truths surrounding the costs of data supply chain rearrangement, we can make pragmatic decisions that balance incremental improvement against potential challenges. Every addition or switch requires this analysis because compromises will always be present.

Cutting Through the Noise: Principles for Pragmatic B2B Data Success

Ultimately, successfully enabling a B2B data supply chain requires filtering out noise and tailoring your strategy to align with your organization's unique objectives. To help guide your pragmatic success, consider these key principles:

  1. Prioritize Relevant Data: Instead of fixating on theoretical volume, focus on the depth of coverage for your ideal customer profile. Remember, less can be more if it delivers specific, high-value information.
  2. Continuously Evaluate and Scrutinize: Proactively identify gaps and inaccuracies through rigorous analysis, customer feedback, and profiling. Take proactive measures to enhance your data before it undermines your initiatives.
  3. Approach Additions or Switches with Caution: When considering new data providers or transitioning between sources, carefully weigh the incremental value against the integration costs and potential data loss. Understand that data supply chain rearrangements require compromises—there is no one-size-fits-all solution.
  4. Blend Quantitative Rigor with Qualitative Feedback: Scrutinize your data statistically, but also gather insights from frontline teams and customers. These qualitative perspectives often highlight gaps that quantitative analysis alone might miss.
  5. Embrace Some Imperfection: While it's important to set high standards for data quality, it's equally crucial to recognize that data is never flawless. Allow room for imperfections and focus your energy where it will have the greatest business impact.

By adopting the right mindset and implementing a systematic approach, B2B brands can cut through the clutter of assumptions, half-truths, and myths. They can build a data supply chain and data asset that fuel real-world business outcomes, all while avoiding the distractions of theoretical perfection.

The B2B data landscape is now unveiled. It's time to set aside misleading assumptions and make intelligent, practical decisions based on how data tangibly drives revenue. The future of data-driven success starts with open eyes and a commitment to informed choices rather than blind faith.

Use Primer to Align Your B2B Data Sets with ICP Criteria

Primer fosters precise and hassle-free alignment of your list-based audiences with ICP criteria. It enhances targeting precision by providing unique data enrichment capabilities: build your laser-focused audiences by pulling together hundreds of data points from multiple data providers at once.

Eventually, you can win higher match rates for your paid ad campaigns across popular ad platforms: up to 85% on LinkedIn, 65% on Facebook, and 45% on Google Ads. Explore how it works – request a live Primer demo today!

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