Unlocking Untapped Data in Salesforce

We saved a tech company $128K in 22 days—by eliminating one broken process. Here’s how we did it: 6 months ago, Overjet, the world leader in dental AI, came to us with a problem. Their marketing team was stuck. Their data was scattered across 3 tools, and no one trusted a single source. Decisions were delayed. Growth was stuck. And it was costing them big. Here’s what was happening. Overjet has 185+ employees and had just hired a new marketing director—let’s call him Steve. Steve was spending 5.5 hours a day manually entering lead data: • Names • Emails • Company details Across three tools: HubSpot, Google Sheets, and Salesforce. Every day. That’s 110 hours a month wasted. At his salary, that added up to over $128,000 a year—$128K lost in just 22 working days. And that’s just his time—not counting the ripple effect: • Hours lost fixing reports • Wrong analysis from bad data • Delayed decisions and slow growth They were guessing instead of using real data—because they knew their numbers weren’t reliable. Here’s how we fixed it: First, we ran discovery. We asked: • What’s your process? • What tools are you using? • Where’s Steve spending his time? The problem? Manual data entry. The fix? Automate everything. They were already using Pardot (a HubSpot-like tool for Salesforce) to collect marketing data. So we integrated HubSpot with Salesforce, pushing everything into one system. Google Sheets? Gone. No more third platform. We mapped it out: • Where does the data go? • Which fields get it? Then we tested it in a Salesforce Sandbox to make sure everything worked. Steve signed off, we went live, and it ran perfectly. The results? Steve got 5.5 hours back every day. No more wasted time. No more bad data. That’s $128,000 saved in 22 days—just from fixing one broken process. And with clean data, they could scale faster. For every $1 they paid us, they got $12 back. If your team is juggling tools and bad data, you’re losing money. We’ve helped companies like Overjet fix this. It’s straightforward when you know where to look. Thanks for reading. Enjoyed this? Follow Jordan Nelson for more. P.S. - Running Salesforce but stuck with bad data? DM me "Salesforce"—let’s fix it.

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

One simple Salesforce configuration transformed how my client manages volunteers. The Problem: Google Forms is a quick and easy way to collect data, but for long-term data management, it often creates more work than it saves. A client of mine used it to gather volunteer contact info and consent forms, but the data ended up isolated from their Salesforce system. This meant duplicate effort, manual updates, and missed opportunities to use that information effectively. The Solution: Salesforce Experience Cloud is a great alternative for organizations that want to collect data directly into their CRM. Here’s how I approached this: 1. Start with a Custom Object - I created a custom object in Salesforce to store volunteer information and consent forms. This keeps the data structured and easy to manage. 2. Build a Public-Facing Site - Using Experience Cloud, I built a simple, branded site where volunteers could fill out their details and consent forms. 3. Leverage Salesforce Flows - The site is powered by a Flow behind the scenes. When a volunteer submits their information, Salesforce automatically creates a record in the custom object. No administrative intervention required. The Benefits: --Volunteer data is no longer siloed. It’s stored directly in Salesforce alongside other organizational data. --Manual data entry is eliminated, reducing errors and saving time. --The system is scalable and reusable for future campaigns. Why It Matters: Solutions like this don’t just save time—they lay the foundation for better data management, reporting, and insights. If you’re a Salesforce admin or CRM professional, consider how Experience Cloud and Flows might help you reduce silos and automate processes for your organization. Here's a screenshot of what the Flow looks like inside Salesforce:

I just released a small browser extension for Salesforce admins focused on one problem: understanding whether fields are actually used in a real org. There used to be a free app called FieldTrip from Ringlead in the App Exchange, but it is no longer available. Alternative apps such as FieldSpy, Cuneiform and extensions such as SF Explorer, Inspector Reloaded and now, SFDX Hardis, are filling the gap to a degree. 🙏 This new extension attempts a few different approaches to the challenge of identifying unused fields in an org. It runs targeted SOQL queries and query plans to generate a local HTML report with charts that show: 👉 Percent of NULL values per field to surface unused or underused fields 👉 Distinct value distributions to catch low-quality or misleading data 👉 Monthly usage to spot trends and adoption changes or fields that quietly went inactive 👉 estimates the record counts/nulls via query plan rather than directly running the query (hopefully avoiding timeouts in huge orgs, getting faster results within 95% accuracy). Like other Salesforce-focused extensions, it simply reuses the active session from the Salesforce tab by reading the existing session cookies. There are no backend calls other than consuming a few Salesforce REST/Tooling API requests on your org. Anyone can inspect the full source code here: 🔗 https://lnkd.in/g4J-VhRQ You could build reports and dashboards to get similar answers but this is an automated way to do it, with charts included, without setting up multiple reports. Pick sObjects, pick fields, run, and review the output. The goal is practical cleanup: deciding what fields/values can be deprecated, where validation or training is missing, and where data quality is drifting over time. It outputs a standalone report in a new tab with tables and charts and you can print it to PDF. Feedback from admins dealing with that kind of technical debt is welcome! 🙏 LINK to the extension at the Chrome Web Store: 🔗 https://lnkd.in/gecEN5wn

Too many B2B SaaS CROs think that “better Salesforce hygiene" will magically unlock pipeline visibility. Here are 3 reasons why they're wrong: 𝗖𝗢𝗡𝗧𝗘𝗫𝗧 I learned the hard way. I scaled my previous B2B SaaS business to $76M ARR as the CRO. We invested millions in making our SFDC data better. Sure, we started seeing improvements. But it didn’t convert into the *full* pipeline visibility I craved. And I'm not alone. I see the same disappointment when talking to other CROs every week. Changing the way we think about pipeline visibility eventually gave us: ↳ accurate forecasts ↳ fewer late-stage deal surprises, and ↳ the confidence to scale toward $100M 𝗛𝗲𝗿𝗲 𝗮𝗿𝗲 𝟯 𝗿𝗲𝗮𝘀𝗼𝗻𝘀 𝘄𝗵𝘆 𝗦𝗙𝗗𝗖 𝗵𝘆𝗴𝗶𝗲𝗻𝗲 𝗮𝗹𝗼𝗻𝗲 𝗱𝗶𝗱𝗻'𝘁 𝘀𝗼𝗹𝘃𝗲 𝗼𝘂𝗿 𝘃𝗶𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝘆 𝗽𝗿𝗼𝗯𝗹𝗲𝗺𝘀 (& 𝗵𝗼𝘄 𝘁𝗼 𝗴𝗲𝘁 𝗶𝘁 𝗿𝗶𝗴𝗵𝘁). 1️⃣ 𝗗𝗮𝘁𝗮 𝗶𝘀𝗻’𝘁 𝗲𝗻𝗼𝘂𝗴𝗵. 𝗣𝗿𝗼𝗰𝗲𝘀𝘀 𝗶𝘀 𝗸𝗲𝘆. Clean data without clear processes is meaningless. You’ll still struggle with poor visibility, execution, and forecasting. This is about 2 things: 1. A sales process that accurately reflects buyer progression 2. An effective revenue cadence (pipeline reviews, etc.) that creates visibility Result: your data reflects the true pipeline status. (Side note: I created a free revenue cadence guide. Comment "cadence" + send me a connect request for a copy) 2️⃣ 𝗬𝗼𝘂 (𝘀𝘁𝗶𝗹𝗹) 𝗱𝗼𝗻'𝘁 𝘀𝗲𝗲 𝗿𝗶𝘀𝗸𝘀 & 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀 Even with clean data, hidden risks can derail deals. Ask your RevOps team to convert data into actionable signals: • Days in stage • No next steps • Multi-threading • Last activity date • Next meeting date • Close-date push count • Low buyer engagement flag ... Use them in your pipeline reviews and forecast process. Either: 1. Embed insights in Salesforce yourself, or 2. Use revenue intelligence tools like Weflow to auto-capture customer interactions in Salesforce and surface risks and insights. 𝗥𝗲𝘀𝘂𝗹𝘁: ↳ Visibility into deal health ↳ Proactive risk management ↳ Fewer last-minute surprises 3️⃣ 𝗬𝗼𝘂𝗿 𝗱𝗮𝘁𝗮 𝗱𝗼𝗲𝘀𝗻'𝘁 𝗰𝗿𝗲𝗮𝘁𝗲 𝗮𝗰𝗰𝗼𝘂𝗻𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆 (𝘆𝗲𝘁) Many CROs treat pipeline reviews like status checks. ("When will this close?") That's reactive pipeline management. And it won't get you to $100M. Pipeline reviews are not about status updates. They are about proactive pipeline progression: • Blockers • Next steps • Coaching opportunities Instead, use your: • data • process (step 1) • and insights (step 2) to facilitate honest, data-driven discussions. This way, you'll create visibility (and accountability) for your entire team. What started as status checks have now turned into strategy and accountability sessions. 𝗥𝗲𝘀𝘂𝗹𝘁: ↳ Better sales execution ↳ Predictable pipeline ⸻ 🎯 PS: I created a 𝗙𝗥𝗘𝗘 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲 𝘃𝗶𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝘆 𝗰𝗵𝗲𝗮𝘁 𝘀𝗵𝗲𝗲𝘁 with more tips. Want a copy? Comment "pipeline visibility" + send a connect request.

300,000 customers went silent. Here is how we brought them back. Every automotive brand fears the "service cliff." Once the warranty ends, customers often drift away to local mechanics. For one of our major automotive clients, this wasn't just a drift, it was a landslide. Over 300,000 customers hadn’t visited a service center in more than two years. They were effectively churned. That meant lost revenue and missed chances to sell accessories or upgrades. We knew sending a generic "We miss you" text wouldn't work. We needed to use data to find the specific people who were actually likely to come back. Here is the playbook we used to fix it: 1. The Data Foundation: We pulled nearly 9 million records into Salesforce Data Cloud to build a single, unified view of the customer. 2. The Segmentation: We applied an RFM model (Recency, Frequency, Monetary) to their service data. We didn't treat all 300,000 users the same. We scored them to identify the high-priority 3. The Journey: We launched a 45-day campaign across web and mobile. Instead of spamming discounts, the messaging focused on the value of authentic service, safety, and genuine parts. The Result? In just two months, we recovered 1.5 Cr INR (approx. $180k) in revenue directly from these "lost" customers. We brought over 3,000 owners back into the official service network and saw retention jump by 8-12%. The lesson here is simple: your dormant data is likely your biggest untapped revenue source. You just need the right model to wake it up and act on it. #martech #salesforce

I'll be honest — 𝐰𝐡𝐞𝐧 𝐈 𝐟𝐢𝐫𝐬𝐭 𝐡𝐞𝐚𝐫𝐝 𝐭𝐞𝐫𝐦𝐬 𝐥𝐢𝐤𝐞 "𝐃𝐚𝐭𝐚 𝐋𝐚𝐤𝐞 𝐎𝐛𝐣𝐞𝐜𝐭", "𝐈𝐝𝐞𝐧𝐭𝐢𝐭𝐲 𝐑𝐞𝐬𝐨𝐥𝐮𝐭𝐢𝐨𝐧", 𝐚𝐧𝐝 "𝐀𝐜𝐭𝐢𝐯𝐚𝐭𝐢𝐨𝐧 𝐓𝐚𝐫𝐠𝐞𝐭𝐬", 𝐦𝐲 𝐛𝐫𝐚𝐢𝐧 𝐜𝐨𝐦𝐩𝐥𝐞𝐭𝐞𝐥𝐲 𝐟𝐫𝐨𝐳𝐞.  😅 𝐃𝐚𝐭𝐚 𝐂𝐥𝐨𝐮𝐝 felt like a whole new language. So I did what any 𝐚𝐥𝐞𝐬𝐟𝐨𝐫𝐜𝐞 𝐩𝐞𝐫𝐬𝐨𝐧  would do — I mapped it back to what I already know. Turns out, it's not that 𝐚𝐥𝐢𝐞𝐧: 📥 𝐁𝐑𝐈𝐍𝐆 𝐃𝐀𝐓𝐀 𝐈𝐍 → 𝐃𝐚𝐭𝐚 𝐒𝐭𝐫𝐞𝐚𝐦 = Data Import / MuleSoft connector → 𝐃𝐚𝐭𝐚 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦 = Data Loader column mapping + Flow to clean fields 🗄️ STORE & STRUCTURE IT →  𝐃𝐚𝐭𝐚 𝐋𝐚𝐤𝐞 𝐎𝐛𝐣𝐞𝐜𝐭 (DLO) = Big Object (raw inbox — untouched) → 𝐃𝐚𝐭𝐚 𝐌𝐨𝐝𝐞𝐥 𝐎𝐛𝐣𝐞𝐜𝐭 (DMO) = Contact / Account (clean, structured record) → 𝐃𝐚𝐭𝐚 𝐒𝐩𝐚𝐜𝐞  = Business Unit (same org, separate walls per team/region) → 𝐒𝐞𝐦𝐚𝐧𝐭𝐢𝐜 𝐋𝐚𝐲𝐞𝐫 = Field Labels ("col_4" becomes "Q3 Revenue") 🔍  𝐔𝐍𝐃𝐄𝐑𝐒𝐓𝐀𝐍𝐃 𝐈𝐓 →𝐈𝐝𝐞𝐧𝐭𝐢𝐭𝐲 𝐑𝐞𝐬𝐨𝐥𝐮𝐭𝐢𝐨𝐧= Duplicate Merge — but automated at massive scale → 𝐂𝐚𝐥𝐜𝐮𝐥𝐚𝐭𝐞𝐝 𝐈𝐧𝐬𝐢𝐠𝐡𝐭𝐬  = Roll-Up Summary fields on steroids →𝐃𝐚𝐭𝐚 𝐄𝐱𝐩𝐥𝐨𝐫𝐞𝐫 = List Views + Schema Builder → 𝐐𝐮𝐞𝐫𝐲 𝐃𝐚𝐭𝐚= SOQL in Developer Console →  𝐄𝐢𝐧𝐬𝐭𝐞𝐢𝐧 𝐒𝐭𝐮𝐝𝐢𝐨= Einstein Prediction Builder → 𝐑𝐞𝐩𝐨𝐫𝐭𝐬 & 𝐃𝐚𝐬𝐡𝐛𝐨𝐚𝐫𝐝𝐬  = Reports & Dashboards (same — just bigger data!) 🎯 𝐀𝐂𝐓 𝐎𝐍 𝐈𝐓 → 𝐒𝐞𝐠𝐦𝐞𝐧𝐭𝐬  = Campaigns with smart filters → 𝐀𝐜𝐭𝐢𝐯𝐚𝐭𝐢𝐨𝐧 𝐓𝐚𝐫𝐠𝐞𝐭𝐬 = Pushing a list to Marketing Cloud or Facebook Ads → 𝐃𝐚𝐭𝐚 𝐀𝐜𝐭𝐢𝐨𝐧𝐬  = Flows that fire when a profile changes → 𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐂𝐚𝐩𝐩𝐢𝐧𝐠  = Frequency rules (max 3 emails/week per contact) →𝐒𝐞𝐚𝐫𝐜𝐡 𝐈𝐧𝐝𝐞𝐱  = Global Search field indexing 🔗 𝐂𝐎𝐍𝐍𝐄𝐂𝐓 𝐈𝐓 → 𝐔𝐧𝐢𝐟𝐢𝐞𝐝 𝐏𝐫𝐨𝐟𝐢𝐥𝐞  = The 360° view you always wanted but CRM alone couldn't give →  𝐑𝐞𝐥𝐚𝐭𝐞𝐝 𝐀𝐭𝐭𝐫𝐢𝐛𝐮𝐭𝐞𝐬 = Lookup Relationships between objects 𝐃𝐚𝐭𝐚 𝐂𝐥𝐨𝐮𝐝 𝐢𝐬 𝐣𝐮𝐬𝐭 𝐒𝐚𝐥𝐞𝐬𝐟𝐨𝐫𝐜𝐞 — 𝐛𝐮𝐭 𝐟𝐨𝐫 𝐀𝐋𝐋 𝐲𝐨𝐮𝐫 𝐝𝐚𝐭𝐚, 𝐧𝐨𝐭 𝐨𝐧𝐥𝐲 𝐂𝐑𝐌 𝐫𝐞𝐜𝐨𝐫𝐝𝐬. Same journey: Bring data in → Clean it → Model it → Analyze it → Act on it #Salesforce #DataCloud #SalesforceAdmin #SalesforceConsultant #CareerGrowth #LearningInPublic #Trailblazer

The top sales teams don’t use AI to type faster. They use it to see what humans always miss. The ceiling for sales is not set by AI. It is set by how you use it. I see the same pattern across teams: They plug AI into email. They save a few minutes. They miss the real leverage. Here is how each role can use Claude today as a thinking engine, not an admin helper. SDR Paste a prospect LinkedIn profile. Ask Claude for: → A sharp opener tied to their role and recent activity → 3 reasons they may care about your offer Now you have context, not a generic first line. BDR Drop in account data and public info. Ask Claude to: → Map key stakeholders → List live trigger events → Draft a 5-touch outbound plan Outreach becomes a strategy, not a guess. AE Upload call notes or a deal summary. Ask Claude to: → Simulate objections by persona → Compare what the buyer said vs what you assumed You catch blind spots before the next call, not after a lost deal. Sales Engineer Share feature docs and use cases. Ask Claude to: → Translate features into business outcomes → Draft ROI logic for each persona Demos move from “here is what we do” to “here is what you gain.” Manager Upload call recordings or summaries. Ask Claude to: → Score each call on a standard rubric → Surface coaching gaps by rep Coaching becomes targeted, not random. VP of Sales Feed win/loss notes and pipeline data. Ask Claude to: → Cluster reasons deals close or stall → Flag patterns by segment, stage, and rep Playbook changes come from evidence, not opinion. Sales Ops Paste your comp plan. Ask Claude to find misaligned incentives that push bad behavior. RevOps Compare lead data to closed revenue. Ask Claude: → What marketing qualifies → What sales actually wins Now the funnel aligns around reality, not form fills. The common mistake is using AI to push buttons faster. The real unlock is using it to think sharper at every step of your GTM flow. When you use AI to see what humans miss, it does not replace effort. It turns effort into a system.

One of the most common questions we get from B2B customers is: "How do we get our sales team to effectively use intent data?” The answer is simple: Put it where they already work and provide context. At Demandbase, we've learned that even the best insights get ignored if they require too much platform switching. That's why I’m thrilled to announce that as part of our partnership with Salesforce, we're rolling out a powerful new B2B sales integration to deliver intent directly into the Salesforce Prospecting Center. What we've found is that sales teams must have three things to succeed with intent data: - Real-time access to signals - Clear prioritization - Zero platform switching Here are the 4 core capabilities this integration delivers: 1. Supercharged lead prioritization Ever wasted time on leads that don’t convert? Not anymore. Demandbase’s intent data is designed to help you zero in on prospects who are actively in-market—meaning you’ll spend your time on the ones most likely to close. No more guessing, just informed, targeted action. 2. Streamlined workflow = efficiency + flexibility Tired of toggling between different platforms, tools, and spreadsheets just to manage your leads? With Demandbase’s intent data now baked into Salesforce, everything you need is in one place. This integration streamlines your process, making it both flexible and efficient, so you can spend more time selling and less time wrestling with tech. 3. Personalized outreach that actually lands It’s not just about knowing who to reach out to—it’s about knowing why. With Demandbase’s intent data at your fingertips, you’ll get a clear picture of what your prospects are researching, their pain points, and their priorities. This means you can tailor your outreach to resonate, which increases your chances of getting a response and moving the deal forward. 4. Maximize your existing investment in Salesforce If you’re already using Salesforce, this integration adds even more value to what you’ve built. By tapping into Demandbase’s intent data within Salesforce, you’re squeezing more ROI from your current setup without the hassle of adding new tools or systems. This isn't just another integration announcement. I've spent years watching sales teams struggle with disconnected tools and data. And I've heard countless customers tell me they need intent data directly in their Salesforce workflow. That's why I’m so excited about this. No more switching platforms. No more guesswork. This integration puts insights exactly where you need them: inside Salesforce. At the heart of our partnership is a shared belief: sales and marketing work better when they work together. Now, it’s even easier to align on a common set of goals, data, and insights. Big thanks to Eugene Williams and Bill Cao for all the hard work behind-the-scenes to make this happen.

Salesforce Data Quality: From 3% to 95% with AI-Augmented Operations. This morning I asked Claude to audit my Salesforce data quality. The result: 3.2% complete. Out of 100+ accounts, only 3 had basic information such as industry and website. The rest were empty records - limiting sales effectiveness, marketing segmentation, and strategic visibility. The traditional fix: days or weeks of manual research and data entry. The AI approach: one hour. Claude researched each company, verified official sources, classified industries using Salesforce’s standard categories, updated the records, built analytics dashboards, and documented the business case. All unsupervised. The result: 95% data completeness in under an hour. Imagine applying the same pattern to contact enrichment, opportunity validation, or predictive lead scoring. Imagine applying the same pattern to all of your internal databases? The architecture is simple: Connecting Claude to Salesforce through MuleSoft via MCP. Standard patterns. No custom code. Curious how this works in detail? Let’s chat. #EnterpriseArchitecture #Salesforce #MuleSoft #MCP #AIAugmentation