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:
Why Is the Lead Breaking Down?
The lead was created for a much simpler version of B2B. One buyer. One form fill. One company. One mostly linear journey from awareness to purchase. In that world, the lead made sense.
That world doesn’t exist anymore.
Today’s enterprise buying motion is messy by design. Deals involve multiple stakeholders with different roles and levels of influence. People change jobs mid-cycle. Subsidiaries, regions, and parent companies blur account boundaries. Buyers research anonymously, often with AI assistance, long before they ever identify themselves. Signals appear across dozens of systems, none of which see the full picture.
The problem is that the lead object tries to represent a person, a moment, and a channel-specific interaction all at once. It’s overloaded. And instead of creating clarity, it fragments identity across systems.
What Is The Hidden Cost of Lead-Based GTM?
One of the most costly side effects of lead-centric architecture is identity fragmentation. A single person can easily become multiple leads, multiple contacts, multiple scores, and multiple engagement histories depending on where and how they show up. RevOps teams spend enormous effort trying to clean this up after the fact – deduplicating, merging, rerouting, and reconciling records after downstream systems have already acted on bad assumptions.
From an automation or AI perspective, this fragmentation is fatal. Systems can’t reason about buyers if they don’t know which records represent the same human.
The lead model also struggles badly with buying groups. Buying groups are now table stakes in enterprise B2B, but leads were never designed to represent roles, influence, or relationships within a group. They don’t understand how someone in a regional subsidiary relates to a global parent account. They can’t easily model influence versus authority. When teams try to layer buying-group logic on top of lead objects, the result is usually brittle rules, manual workarounds, and incomplete visibility.
There’s also a more subtle issue: leads tie identity to channels instead of reality. Someone becomes a lead because they filled out a form, clicked an ad, or downloaded content. But buyers don’t think of themselves that way. And neither do modern AI systems. What actually matters is who someone is, where they work, what role they play, and how their behavior evolves over time and across touchpoints. The entry point shouldn’t define the identity.
As GTM stacks become more automated, this gap becomes even more dangerous. Routing, prioritization, scoring, segmentation, and personalization decisions increasingly happen without human review. When those decisions are based on fragmented lead data, errors don’t just happen, they scale. And AI isn’t fixing those error. It’s amplifying them.
What Replaces the Lead? Buyer Profiles.
The future of GTM isn’t lead-centric. It’s buyer-centric.
Instead of treating the lead as the core data object, forward-looking teams are shifting toward buyer profiles: persistent, unified representations of real people and real companies. A buyer profile isn’t tied to a single interaction or channel. It’s person-centric rather than channel-centric. It’s dynamic instead of static. And it’s synchronized across systems rather than trapped in silos.
At its core, a buyer profile represents a real human and their relationship to a real organization, independent of how or where engagement happens. It captures identity, company association, hierarchy, behavioral and intent signals, engagement history, fit and persona indicators, and buying-group role or influence. This model aligns far more naturally with how AI systems, answer engines, and modern GTM workflows reason about the world.
Why Is This Shift Urgent as Teams Plan for 2026?
For RevOps teams, the pressure is mounting. They’re expected to reduce friction, improve handoffs, support buying groups, make automation safer, and enable AI adoption, all at the same time. None of that works cleanly on top of lead-centric architecture. Buyer profiles simplify reconciliation, reduce downstream errors, and create a single trusted identity layer that every system can rely on.
GTM engineers feel this shift even more acutely. They’re increasingly responsible for data pipelines, enrichment orchestration, identity resolution, and system interoperability. Leads are brittle data objects that don’t travel well across systems. Buyer profiles behave more like modern data entities: stable, extensible, and reusable across workflows.
For GTM leadership, the stakes are strategic. Predictability, scale, efficiency, and AI readiness all depend on having a clean, consistent understanding of who the buyer actually is. Lead-based models hide inefficiencies that compound over time. Buyer-centric models create compounding returns.
This Doesn’t Mean Deleting Leads Overnight.
None of this requires ripping out your CRM or marketing automation platform. The shift isn’t about eliminating leads entirely. It’s about demoting them.
Forward-looking teams treat leads as ingestion events rather than sources of truth. Identity lives upstream in buyer profiles. Those profiles are synchronized across CRM, MAP, CDP, and data warehouses. Leads become temporary interfaces, not the foundation of the GTM data model.
This approach allows organizations to evolve without breaking existing workflows, while still preparing for what’s coming.
What To Expect in 2026.
The lead served B2B well for a long time. But as buying behavior changes and AI becomes embedded in every layer of GTM, the limitations of lead-centric thinking are becoming impossible to ignore.
By 2026, the most effective enterprise GTM teams won’t ask, “How many leads do we have?”
They’ll ask, “Do we have a complete, accurate, continuously updated understanding of the buyers involved – and can every system act on it?”
The end of the lead isn’t a loss. It’s the beginning of a more accurate, scalable, and intelligent GTM model – and an opportunity to adopt it before your competition does.
Contact us to see how Leadspace does Buyer Profiles.
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Enterprise B2B go-to-market (GTM) teams don’t struggle because they lack tools. They struggle because their customer data lives everywhere, but doesn’t work correctly anywhere.
CRM. Marketing automation. Enrichment vendors. Intent platforms. Sales engagement. Data warehouses. Acquired company databases. Regional instances.
Each system holds a piece of the puzzle but none of them independently point towards the same truth. When customer data is fragmented, siloed, and static, the consequences surface exponentially.
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