Article

Data Decay: What, Why and How?

People and companies change every day. Companies make acquisitions, people change jobs, and intentions are dynamic. This means your data changes every day – but is your database up to date? Is the data you use to drive your business as accurate as the day you procured it? Data Decay is an issue that every company must face at some point. Email marketing databases, for example, naturally degrade by approximately 23% every year according to Hubspot.

Data decay has especially accelerated during and after the pandemic. This is agreed on by 79% of Customer Relationship Management (CRM) users according to The State of CRM Data Health in 2022 published by Validity. As the business environment restructures itself in the post-pandemic era, a new symptom is quickly spreading among companies: millions of workers are still quitting their jobs in 2022. The “Great Resignation” is affecting even the most solid data-driven strategies for B2B marketers.

High-quality data is the fuel that makes the sales funnel engines spin. According to the Global Data Management Report, which considered responses from 700 data-centric business leaders around the globe, 84% of B2B companies saw increasing demand for data-driven insights within their organizations during the COVID-19 pandemic. The effects derived from the “Great Reshuffle” or “Big Quit” have accelerated this decay to levels not fully understood.

While the degree of decay is not yet fully understood, our response to it can greatly mitigate the effects the decay will have on our organizations’ successful use of data to drive decisions. Here are 6 ways you can address data decay to ensure your data is accurate, up-to-date and, most of all, insightful:

What Is The Hidden Cost of Lead-Based GTM?

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How Do You Calculate Match Rate on B2B Data?

Your CRM has thousands of records. Your marketing automation platform is loaded with contacts. Your outbound sequences are running. But when you try to enrich those records, route leads to sales, or fire a signal-based workflow, a large portion of your data simply does not match.


Match rate is the metric that tells you how much of your data is actually usable. For revenue teams running enrichment programs, account-based campaigns, or automated scoring, this number carries real operational weight. A low match rate means your workflows are running on incomplete information, your segments are thin, and your automation is making decisions with missing context.


Understanding how to calculate b2b data match rate, and what actually drives it, is one of the most practical things a RevOps or marketing ops leader can do to improve GTM performance.

Learn what signal-based selling is and what GTM data infrastructure your revenue team needs to execute it at scale.

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What Is Signal-Based Selling?

Your CRM is full of contacts. Your marketing automation platform fires campaigns on schedule. Your sales team works the list. And yet, deals stall, outreach lands flat, and pipeline forecasts drift further from reality every quarter.


The problem is not effort. The problem is timing.


B2B sales teams have spent years optimizing how they reach buyers. Very few have focused on when buyers are actually ready to engage. Signal-based selling changes that equation entirely. It shifts your go-to-market execution from a calendar-driven model to a behavior-driven one, so your team shows up when intent is live, not when the cadence says it is time.


This post breaks down what signal-based selling is, why it matters now, and what your revenue infrastructure needs to support it at scale.

Data Management System strategies that align marketing and sales on inbound SLAs, routing, and faster follow-up.

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Aligning marketing and sales on inbound SLAs that work

Your inbound engine breaks when marketing and sales work from different clocks, different definitions, and different routing rules. That gap shows up fast in missed follow-up, weak conversion, and low trust across teams.


If you want inbound SLAs that hold up under volume, you need more than a handoff document. You need aData Management System that keeps records clean, routes leads with context, and gives both teams the same operating view.


That is the GTM alignment moment most teams miss. Marketing says the lead hit the threshold. Sales says the lead lacked context, landed late, or reached the wrong rep. Both teams look at the same funnel and see different stories.


A working SLA removes that ambiguity. It ties response time, routing logic, ownership, and enrichment to a shared data foundation.