Data Enrichment Services

Your GTM team isn't losing deals because your offer sucks. You're losing deals because you can't reach half the people you want to sell to. I tested 12 enrichment tools last quarter. Most find emails for 40-50% of your target list. The rest of your TAM?  Ghost town. The brutal truth: While SDRs waste 6 hours daily on manual LinkedIn stalking, your competitors are already talking to those prospects. Yesterday's deployment proved it: Client's SDR team: 200 dials daily, 18% connect rate. After fixing their data problem: Same 200 dials, 67% connect rate. Not more activity. Better data. Here's what actually ships: FullEnrich runs waterfall enrichment across 20+ premium providers. Not another single-source vendor. A system that hits 80%+ find rates. Triple-verified emails that don't bounce. Real mobile numbers. Not corporate switchboards. Fresh data. Not 18-month-old LinkedIn exports. Pair it with n8n Automation: New lead hits your CRM → N8n calls FullEnrich API → Complete profile in 3 seconds → Scored, routed, and triggered for outreach. No more: ✔️ SDRs googling email patterns ✔️ Inbound leads sitting dark for 72 hours ✔️ RevOps cleaning spreadsheets until 9pm I watched a Series B startup burn $400K on enterprise enrichment tools. Still couldn't reach 60% of their ICP. Meanwhile, a 5-person team using this stack: - Enriched 10,000 contacts - 83% find rate - $8K monthly (not $40K) - Deployed in 2 days The setup that ships: 1. Connect FullEnrich to your CRM 2. Deploy N8n's automation workflows 3. Watch your pipeline fill with actual humans 4. Stop paying for data that doesn't exist While consultants debate "data quality frameworks," builders are closing deals with numbers that actually work. Today. Grab 50 free enrichment credits and test your worst performing list. What percentage of your TAM is currently unreachable?

Is HubSpot Enrichment a magic wand or a freight train? I 🧡 HubSpot, but we need to talk about where its native enrichment shines, and where it might actually be destroying your data integrity. In a recent data audit for a customer, we uncovered a critical distinction that every RevOps and Data leader needs to understand. ✅ Where it’s GOOD: If you are selling into SMBs or single-entity companies, HubSpot enrichment is amazing. It flawlessly pulls in the correct domain, LinkedIn page, and location. It just works. ❌  Where it creates CHAOS: The moment you step into the Enterprise world with complex corporate structures (think Nestle, Pfizer, Coca-Cola), native enrichment can turn into a “freight train.” Here is what we saw: The client had distinct records for regional subsidiaries (e.g., Nestle Spain, Nestle Canada). HubSpot’s enrichment saw “Nestle” and indiscriminately overwrote the specific local data with the Global HQ data. The result? - 10 different company records all overwritten with the exact same HQ phone number and LinkedIn URL. - Leads in EMEA are routed to US reps. - Zero visibility into the actual local entity you are trying to sell to. The Takeaway: If your strategy relies on navigating complex parent-child relationships and selling to specific subsidiaries, turn off automatic company enrichment. Don’t let convenience overwrite context. Sometimes, “smart” tools are the reason your data is dumb. Has anyone else experienced the “Enrichment Freight Train”?

I wasted $47k testing 200+ AI sales tools so you don't have to. Here's the exact stack that took us to $6M ARR: 1,300+ AI sales tools exist in 2025. Most are unnecessary. Here's what you actually need: 1/ Accurate B2B data Data quality determines campaign performance. Everything downstream depends on this foundation. Your sourcing options: - Standard databases: LinkedIn Sales Navigator, Ocean.io, Apollo - Niche targeting: Openmart for local business focus - Custom scraping: Apify, Instant Data Scraper for specific requirements - Intent signals: Clay, Common Room - prospects showing buying behavior - AI agents: Claygent, Relevance AI, Exa, Linkup - automated prospect discovery 2/ Reliable data enrichment Valid contact information is non-negotiable. You need verified emails and phone numbers. Two approaches: - Point solutions: Prospeo.io, Wiza, LeadMagic - specialized tools - Waterfall platforms: FullEnrich, Clay - multiple data sources in sequence 3/ Engagement platforms - Email solutions: Instantly.ai - LinkedIn outreach: Expandi.io, Valley - Multi-channel: lemlist - email + LinkedIn 4/ Deal execution When prospecting generates consistent pipeline, you need a system to close those deals: - CRM: Attio, Breakcold for deal tracking - Intelligence: Attention, Momentum.io - call recording, CRM enrichment, next-step recommendations The strategic advantage comes from integration, not tool quantity. What's your latest stack addition? Want weekly breakdowns of the tools that actually work? Join 10,000+ reading getting our AI sales newsletter.

A lot of people screw up data enrichment. And not in small ways...in big ways. I've walked into instances where: 😱 Recent sales-entered data was being overwritten by stale enrichment data 😱 Instead of setting up an integration, a massive file was imported all at once, into standard fields, without a data back-up....leaving no audit trail and losing historical data 😱 Enrichment was set up to trigger every time a record was created or updated in Salesforce, creating a situation where only 1,000 records or less could be updated at one time without hitting the Salesforce API limits 😱 Enrichment data wasn't standardized, so each vendor was entering in different formats for fields like employee size So, how do you set it up correctly? Here's what it should look like... Typical steps included: 1. Input Stage: Define the entry points for raw data (e.g., web forms, imports, email captures). 2. Cleaning Stage: Build workflows to: 🔺 Standardize formats (e.g., phone numbers, dates, addresses). 🔺 Correct invalid or missing data (e.g., normalize country names to ISO codes). 🔺 Remove duplicates based on unique identifiers (e.g., email or account ID). 3. Enrichment Stage: 🔺 Match records with external datasets to fill gaps. 🔺 Append metadata (e.g., confidence scores, enrichment source). 4. Output Stage: Push cleaned and enriched data back into your CRM or database. Example washing machine flow: 1. Input: New leads enter from web forms or imports. 2. Cleaning: 🔺 Deduplicate by email or company domain. 🔺 Standardize phone numbers to E.164 format. 🔺 Normalize country names to ISO codes. 3. Enrichment: 🔺 Call Clearbit API to append industry, company size, and LinkedIn URL. 🔺 Validate emails with an email verification tool. 4. Output: Push cleaned and enriched data back to CRM, tagging it with the enrichment source and date. Things to consider: 🔻 Typically, you want to enter enrichment data into separate custom fields. This is duplicative, but if you don't have really strong audit trails and strong enrichment rules, you shouldn't write into a default field because you could cause confusion and frustration with sales, if you overwrite their recently entered data. 🔻 You need to understand all of the fields you're enriching very intimately...what is their purpose, at which stage do they need to be enriched? Don't be lazy and enrich every field at every record edit, it'll harm your systems and speed-to-lead. 🔻 Make sure any enrichment automation takes race conditions into consideration -- what other automations could be triggered, and how would that impact the API limits/system performance? 🔻 How will you monitor results? Set up reports and audit trails, whether through Snowflake or field history in Salesforce. 🔻 Don't forget about consent management fields! Running out of room....what else? Did you find this helpful? #marketing #sales #marketingoperations #revenueoperations

Here’s the dirty secret about lead enrichment: The vendors you’re paying for are incentivized on volume, not outcomes. And when that clicks for our customers, it’s like an explosion. Multiple members of a buying committee recently stopped one of our AEs 10 minutes into his demo to make sure they’d heard him right when he explained our approach to enrichment. Common Room doesn’t have a credit-based enrichment system. We don’t charge you to enrich individual records, like email addresses, phone numbers, and social handles. And we don’t charge you to refresh those records over time. We price per person. As in, the actual human being behind all that data. And we perpetually refresh their info at no extra charge. Why? Because we believe complete, accurate identity resolution and enrichment is table stakes for go-to-market teams. It’s why both come right out of the box for all Common Room customers. Messy SFDC instances with a dozen separate records for the same person isn’t an aberration—it’s the norm. I was talking to the VP of Marketing at a $2B software company recently. He told me he pays literally millions of dollars for Salesforce storage (their instance has 220 fields when the sales team really only needs 6). But the problem goes far beyond the cost of contact sprawl. Connecting disparate identity markers to real people—quickly, accurately, and scalably—is hard. And that’s just the data inside your own four walls! It gets exponentially harder when you capture and combine data from 2nd- and 3rd-party sources. We think it should be easy. So we automatically unify those records to one profile. We enrich that profile with our proprietary, AI-powered technology: Person360. And we refresh that profile as new records pop up over time, syncing it with Common Room and any other systems you have connected. No manual data engineering. No guesswork. And no extra costs. We do it this way because, while identity and enrichment is a core pillar of our platform, it’s not the end goal. It’s the start. Today we’re making it even better. Here’s how: - AI quality control that auto-blocks inaccurate enrichment Nip bad data in the bud in real time. Our AI proactively blocks inaccurate enrichment data that sequential tools snowball forward. - Enhanced identity merging that reduces duplicate records Make sure GTM teams work from a single source of truth. Our AI-assisted identity merging cuts duplicate profiles by as much as 79%. - Premium package option for expanded visibility Soon you’ll be able to build on our out-of-the-box offering with expanded coverage from real-time search providers for verified phone numbers, current tech stacks, and regional data outside of North America. Our customers sell to people, not contact records. That’s why identity and enrichment come baked into our platform—and why we continually work to deliver more value from both.

Our enrichment vendor's industry data is wrong. And we're just... okay with it? We just wrapped a major account enrichment initiative at Canva. Here's what I learned: Traditional data vendors optimize for standardization, not accuracy. SIC codes. NAICS classifications. Neat little boxes that make reporting easy. But leave your GTM teams guessing. 🫠 So we tried something different. We let AI models classify accounts based on how we actually go to market. → Our verticals → Our emerging ICPs → Our language Not over-relying on some government taxonomy designed for census data. The results? Dramatically better than any vendor-provided industry field (especially for how we classify our verticals), and it costs less than a cent per row. LLMs are genuinely exceptional at reading a company's web presence and mapping it to YOUR segmentation framework. Not just how your enrichment vendor views the world. <2025 was accepting 60-70% accuracy because "that's just how enrichment works." 2026 is targeting 100%. Anything less means: → Pipeline in the wrong hands → Revenue left on the table → Reps wondering why their "perfect fit" account sells office furniture 🪑 Stop letting someone else define how you segment your business. Our lead funnel is next now that our wonderful team has moved it into Clay too 💪

Legacy enrichment tools treat firmographic data as a checkbox exercise. Sales reps spot the obvious mistakes, and RevOps teams take the blame. At Kernel, we treat each data point as its own product. Our product team uses proprietary tooling, search agents, and reasoning agents to constantly improve core firmographic accuracy over time. For every core firmographic data point, we also assign a dedicated product owner. Headcount is one of the most critical data points to anchor the CRM, territory planning, and market segmentation around. Katie Peachey owns it end-to-end: structured and unstructured data sources, heuristics, exceptions, edge cases, and explainability. Our process replicates how a RevOps professional would establish firmographic data accuracy with unlimited time, based on foundational AI-native master entity data, account identity resolution, agent searches, and our reasoning agents. What does this approach prevent? ➡️ Entity confusion. Mosaic, the VC, vs. Mosaic, the biotech is an example of where generic AI tools merge or mix entities. ➡️ Single-source heuristics. Relying on LinkedIn alone can be wildly misleading. Upwork is a perfect example. ➡️ Stale databases. Fast-growing companies like Anthropic can double headcount long before a static vendor refreshes. ➡️ Missing primary data. Most systems do not have the workflows or expertise to find and validate it at scale. 🚨 Sales reps messaging RevOps with obvious mistakes and undermining credibility in CRM data. Here's how this accuracy translates into value at enterprise scale: ✅ Accuracy you can trust across hundreds of thousands of accounts, not just a handful of hand-checked logos enables confident planning at scale. ✅ Fewer escalations from reps who spot obvious errors in territories or TAM. ✅ Explainability. Reps can see why a headcount number is correct and review the underlying evidence and data sources. ✅ Better planning. Cleaner segments, cleaner territories, and fewer missed high-value accounts caused by incorrect data. When core firmographics are developed with ownership, data source evaluation, and feedback loops, RevOps teams finally get the data quality they need to plan with confidence. Our reasoning agent avoids the black box experience that's at the root of broken trust between sales and RevOps. If you want to see the difference for yourself, DM me and we can benchmark Kernel headcount data against whatever you are using today, whether it's your own CRM data, your enrichment vendor, or both. 👉 https://lnkd.in/dRkyzHNw Save your seat for my conversation with Jordan Crawford on the topic next Tuesday: 👉https://lnkd.in/dw7UqBWY

One of the most misunderstood problems in outbound sales is data quality. Teams spend enormous amounts of time debating dialers, scripts, talk tracks, and AI tooling. But if the list is bad, none of that matters. Low pickup rates are rarely a dialing problem (unless your dialer has a spam caller ID problem). They’re a data problem. If your numbers are wrong, outdated, or low-quality, your reps spend most of their day not talking to anyone. Even the best dialer can’t fix that. Last year, we invested heavily in building what became Smart Enrich at Salesfinity. Originally, we treated it as a supplemental tool: - Replace bad numbers - Fill in missing mobiles - Patch gaps in existing lists It worked well — but over time, something became clear. Data enrichment tools, as an industry, are fundamentally broken. Historically, teams had two choices: Single data providers They offer simplicity, but accuracy degrades quickly. There’s no real feedback loop telling the system which numbers work and which don’t. Waterfall enrichment tools They offer broader coverage, but most are optimized for cost, not accuracy. Because they resell third-party data, they prioritize cheaper providers at the top of the waterfall. Again, no meaningful feedback loop. In both cases, enrichment happens in isolation from reality. So we approached the problem differently. What if enrichment learned from actual calls? With Smart Enrich, every number is not just enriched — it’s evaluated over time: - Numbers are validated at each step of the waterfall - Call outcomes create feedback - Accurate providers are automatically ranked higher - Inaccurate providers are deprioritized The system optimizes continuously for pickup rate, not theoretical coverage. The result has been consistent: Customers using Smart Enrich see materially higher connect rates — often in the 10–12% range — without changing anything else. The takeaway isn’t that one tool is better than another. It’s that outbound works best when data is treated as a living system, not a static input. If you don’t measure accuracy, you can’t improve it. And if you don’t improve it, everything downstream suffers. That’s the lesson we learned building Smart Enrich. P.S. if you'd like to test it out, we're offering 100 free enrichment credits to benchmark against your current data provider.

Whether its Autofill columns with SharePoint Syntex, or the new Prompt columns available in Dataverse, the utility of moving AI-based enrichment directly into the data layer of your app or process makes a huge amount of sense. 1️⃣ Understandability for human users: While aggregated summaries can make information more easily accessible, being able to drill down in a way that is clear and signposted can really boost efficiency. Using automated AI-based processing to add those record-by-record signposts makes navigating the data easier, makes filtering more convenient, and ultimately, allows users to get to the data groups or individual records they need more efficiently. 2️⃣ Greater ROI from data capture: Often the raw data that gets captured doesn't entirely convey the whole story in the way that's most readily useable. Additions like sentiment analysis, summarization, or entity extraction, can reorganize the inputs to work more seamlessly with our desired outputs. Adding this enrichment at the point of capture makes that data more readily useable across your organization from that point forward. 3️⃣ Less complex processing: Anyone with Power Automate and an afternoon to spare can achieve exactly the same output without AI-processing at the data source, but it adds unnecessary complexity. Let your data store fully bake the data with the extra information you need and then move over to the automation steps (or autonomous steps with agents) you need from there. The AI enrichment logic is right there in the set-up of your table (or document library) so you don't need to go hunting to understand what's going on. Prompt columns in Dataverse are now in public preview. Autofill columns in Microsoft Syntex are available - see my video linked below if you need more information on this. 🖼️ from Copilot Studio blog post (linked below)

Most people think enrichment starts with a name. It doesn’t. Sometimes it starts with just… an email. A form fill. A scraped list. An old CRM record. A random inbox reply. You have: john@company.com And that’s it. No title. No seniority. No LinkedIn. No context. That’s where most workflows stop. CompanyEnrich’s Lookup Person endpoint is actually a reverse email lookup. You input an email. You get back a structured person profile: → First name → Last name → Job title → Department → Seniority → LinkedIn URL → Experience history Email in → enriched identity out. That’s powerful. Because now your flow becomes: 1️⃣ Capture email 2️⃣ Reverse lookup 3️⃣ Enrich role + seniority 4️⃣ Route dynamically (SDR? VP? Founder?) 5️⃣ Personalize properly You can even run it directly inside Clay as part of a live workflow. No manual LinkedIn check. No guessing the job title. No “Hi {{first_name}}” with zero context. Outbound isn’t just about finding new leads. It’s also about cleaning and upgrading the data you already have. If you’re sitting on thousands of emails in your CRM without context, you’re probably underusing your own database. Are you enriching emails before you launch sequences? If you want more Sales Tech insights, subscribe to my newsletter here: https://lnkd.in/eBPC-4VC CompanyEnrich link in the comments 👇