Product sheet
Building B2B Buyer Profiles
The goal? To deliver effective campaigns targeted at the best opportunities within our Total Addressable Market (TAM) – at the lowest cost.
Doing this successfully starts with creating complete, accurate, dynamic and unified buyer profiles of people, accounts and buying centers so we can properly prioritize and target opportunities with data-driven assurance that we’re delivering the right message to the right people at the right time. Unfortunately, building robust buyer profiles is a complex, cumbersome process – and it usually isn’t cheap.
Meanwhile, your data is decaying. Data decay is an issue that every company faces – especially after the pandemic. As the business environment restructures itself in the post-pandemic era, a new symptom is spreading among companies: millions of workers are quitting their jobs. People and companies change constantly. Companies make acquisitions, people change jobs, and intentions are dynamic. This means your buyer’s data changes every day – but does your database reflect that? Is the data you use to drive your business as accurate as the day you procured it? Historically, achieving that high-quality, up-to-date B2B buyer data has been much easier said than done.
Latest Articles
Sidekick
Article
What Is a Golden Record in a B2B CRM?
Your CRM holds thousands of versions of the truth. One account exists five times. One buyer shows three job titles. One domain maps to four different company names.
That fragmentation breaks everything downstream. Routing misfires. Scoring models train on noise. Territory assignments overlap. Forecasts drift from reality.
A golden record fixes the root cause. It gives every account, contact, and buying group one authoritative profile that your systems trust. When you understand how a golden record CRM strategy works, you stop patching symptoms and start rebuilding the data layer beneath your revenue stack.
Article
Where Do B2B Data Providers Actually Get Their Data?
Every B2B data provider claims their data is accurate, comprehensive, and current. But when your sales team chases down a phone number that goes nowhere, or your scoring model fires on a contact who left the company six months ago, that claim starts to fall apart.
Understanding where b2b data providers get data is not an academic exercise. It shapes how you should evaluate vendors, configure your enrichment logic, and trust the signals flowing through your revenue stack. If you treat all data sources equally, your GTM systems will eventually reflect that mistake.
Article
What Is an AI SDR, and What Data Does It Need to Work?
Sales development has a scaling problem. The volume of accounts to work, signals to monitor, and touchpoints to execute has grown far beyond what a human SDR team handles at consistent quality. AI SDRs have entered the conversation as a way to close that gap. But the question most revenue teams skip past too quickly is this: what actually makes an AI SDR work?
The answer is data. Specifically, the right data, at the right quality, connected to the right systems in real time. Without that foundation, an AI SDR is not a productivity multiplier. It becomes an expensive source of misfires, bad outreach, and wasted pipeline capacity.
This post breaks down what an AI SDR is, where it fits in a modern GTM architecture, and what data requirements determine whether it creates value or creates noise.
