Email Campaign Optimization

Welp, Gmail’s done it again. More new updates to make the inbox experience better *for recipients*, much to the chagrin of some senders. The Promotions tab is getting ranked by relevance (hell yeah), post-purchase mail is getting its own swim lane with a dedicated Purchases tab (just in time for the holiday email season!), and Manage Subscriptions makes pruning senders you no longer want to hear from effortless. Why does *this* change matter, given all the new features they’ve been rolling out recently? Because it means delivery ≠ visibility… even if you send at the “perfect” time for your subscribers. In this new reality, two people can open at the same time and see different stacks. If you’re not in their regular rotation, you’re headed to the Promotions basement (or shuffled to page two)… send-time optimization be damned. Tabs are optional, but the consequences of not sending it right are universal. Ranked visibility rewards usefulness and consistency, not hacks or send-time optimization. Here are some things you can do right meowww… 1️⃣ Separate (and de-glam) your transactional. This one’s all about business up front and (promotions) party in the back. Dedicated subdomain/IPs, and kill the promo pixie dust that might make your business-critical mail seem promotional: upsell CTAs, cross-sell banners, and “while you’re here” copy. 2️⃣ Lean into easier unsubscribes Allowing users to easily “Manage Subscriptions” will accelerate list churn, whether you like it or not. But they don’t hurt deliverability (unless there are a TON), and there are ways you can encourage would-be unsubscribers to stay with you by getting in front of it, like offering a preference center where subscribers can opt-down instead of out. 3️⃣ Adjust your Promotions presentation for “Most Relevant” You can’t force… really anything in your recipients these days. But you can encourage them to engage by making it super clear what your message is about and why they need it in their lives with very little effort. For example, instead of: “Our Fall Drop is Here”, get specific! Say the outcome: “24-hr restock on [X] you favorited” 4️⃣ Update your scorecard to match how Gmail ranks mail Unlike the “Most Recent” view, where your visibility’s been mostly about timing, getting mail delivered under a “Most Relevant” reality doesn’t equate to a fair shot at being seen (even if you land in the inbox). 💌 Optimize for speed of engagement, reach (how many engage), and stickiness (how often they engage). Metrics like Time-to-First-Open, First-6-Hour Click Share, Active Gmail Reach (30d), Click Reach (30/60/90), Replies/“helpful” signals, and Persistence (opened 2+ of last 4) will help you figure out how your mail’s really landing. 5️⃣ Stop blasting to everyone. No, seriously. Stop it. I wrote all about this in my recent Send It Right blog post (and newsletter). Get the full scoop: https://lnkd.in/gSP5GtgE

I interview a lot of performance marketers, and there’s one question no one till now has got it right! I ask them, "How many days should we retarget BOF audiences?" They take a deep breath, straighten up, and confidently say: 👉 "7, 14, and 30 days." Like they just cracked some secret code. So, I push further. "Why 7, 14, and 30?" Silence. A nervous smile. Maybe a generic answer like "That’s what everyone does." Most marketers run ads like they’re following a playbook, not understanding the psychology of a buyer. They apply the same fixed formula whether the product is a ₹500 lipstick or a ₹2,00,000 luxury watch. And guess what? That’s why their BOF campaigns underperform. They all know the technical side of running ads on Meta, but I often find one big gap in their thinking. Here’s the problem: BOF retargeting windows should not be fixed. They should be based on how long a customer takes to make a buying decision. If you sell a ₹499 pair of sunglasses, your customer doesn't need 30 days to decide. But if you sell a ₹50,000 camera, they’re not buying within 3 days either. The Right Way to Set BOF Retargeting Days 👇 💰 Impulse Buys (₹500 - ₹4,000) → BOF: 1-7 days 🛍️ Examples: Skincare, fashion accessories, snacks 📅 Why? People make quick decisions. If they haven’t bought in 7 days, they probably never will. 💰 Considered Purchases (₹4,000 - ₹25,000) → BOF: 3-14 days 🛍️ Examples: Sneakers, home decor, small appliances 📅 Why? Customers compare brands, check reviews, and take about a week or two to decide. 💰 High-Involvement Purchases (₹25,000 - ₹1,50,000) → BOF: 7-30 days 🛍️ Examples: Laptops, furniture, luxury watches 📅 Why? These require more research. Customers look for warranties, financing options, and comparison videos before buying. 💰 Big-Ticket Investments (₹1,50,000+) → BOF: 14-90 days 🛍️ Examples: Cars, premium gadgets, real estate 📅 Why? These are major financial decisions. Customers take months to decide, so BOF retargeting needs to run longer with trust-building content. Lesson for Performance Marketers 🚀 There is no one-size-fits-all approach in BOF retargeting. Every product category has a different buying cycle. If you’re just applying the same 7-14-30 day formula to every business without understanding the logic, you’re leaving money on the table. Next time you set up a BOF campaign, ask yourself: 1️⃣ How expensive is the product? 2️⃣ How long does the customer take to decide? 3️⃣ What objections do they need help with? If you get this right, your retargeting ads will convert better and your clients will notice the difference. #PerformanceMarketing #MetaAds #D2C #Retargeting #MarketingStrategy #HonestMarketing #bottomfunnel #digitalmarketing

My latest from Forbes: Empowering Employers to Enhance Health Care Quality Employers hold immense potential to drive quality improvements in health care, which is vital for the well-being of their employees. With nearly half of Americans depending on employer-sponsored coverage, the responsibility to provide accessible, high-quality health benefits has never been more important. Yes, employers face challenges in pushing for quality and scaling innovations that can help. Currently, only 21% of commercial insurance payments incentivize improvements in health outcomes, a stark contrast to 43% in Medicare Advantage. This gap not only affects costs but also directly impacts the care and support our workers receive. Most employers are focused on their core business, not driving innovation in their benefits. To support increased employer focus on quality, Morgan Health, in partnership with JPMorgan Chase benefits, has established a roadmap that empowers employers to effectively measure and enhance health care quality through five key steps. A link to the Forbes piece is in the comments! 1. Identify Today’s Improvement Opportunities: Understanding the current health status of your employee population helps identify gaps in care quality. For instance, high levels of A1c among certain groups may lead to targeted goals to reduce diabetes prevalence. 2. Select Measures Based on Your Quality Goals: Determine what matters most for your workforce’s health. This could include reducing hospitalizations, enhancing access to preventive care, or improving provider satisfaction scores to ensure that employees are engaged in their health. 3. Determine Measure Baselines and Set Targets: Utilize national benchmarks, like those from NCQA Quality Compass, to establish baselines for key health indicators. This can guide you in measuring improvements against evidence-based expectations. 4. Establish Performance Payments that Incentivize Improvement: Align payment structures with quality improvement goals. Discuss and agree on fees-at-risk for performance targets to ensure accountability from health plans, providers, and vendors. 5. Document the Timeline and Process for Measuring Quality: Clearly outline how baselines are set and how results will be calculated. This not only promotes transparency but also helps in aligning all parties involved in the contract, especially when mitigating risks. Together, we can ensure that employers are equipped to foster a healthier workforce. Improving health care quality is not just beneficial—it’s essential for the health and happiness of our workers. Let's make quality care a priority! #HealthCare #QualityImprovement #EmployeeWellbeing #MorganHealth

It’s no revelation that incentives and KPIs drive good behavior. Sales compensation plans are scrutinized so closely that they often rise to the topic of board meetings. What if we gave the same attention to data quality scorecards? In the wake of Citigroup’s landmark data quality fine, it’s easy to imagine how a concern for data health benchmarks could have prevented the sting of regulatory intervention. But that’s then and this is now. The only question now is how do you avoid the same fate? Even in their heyday, traditional data quality scorecards from the Hadoop era were rarely wildly successful. I know this because prior to starting Monte Carlo, I spent years as an operations VP trying to create data quality standards that drove trust and adoption. Whether it’s a lack of funding or lack of stakeholder buy-in or cultural adoption, most data quality initiatives fail before they even get off the ground. As I said last week, a successful data quality program is a mix of three things: cross-functional buy-in, process, and action.And if any one of those elements is missing, you might find yourself next in line for regulatory review. Here are 4 key lessons for building data quality scorecards that I’ve seen to be the difference between critical data quality success—and your latest initiative pronounced dead on arrival: 1. Know what data matters—the best only way to determine what matters is to talk to the business. So get close to the business early and often to understand what matters to your stakeholders first. 2. Measure the machine—this means measuring components in the production and delivery of data that generally result in high quality. This often includes the 6 dimensions of data quality (validity, completeness, consistency, timeliness, uniqueness, accuracy), as well as things like usability, documentation, lineage, usage, system reliability, schema, and average time to fix. 3. Gather your carrots and sticks—the best approach I’ve seen here is to have a minimum set of requirements for data to be on-boarded onto the platform (stick) and a much more stringent set of requirements to be certified at each level (carrot). 4. Automate evaluation and discovery—Almost nothing in data management is successful without some degree of automation and the ability to self-service. The most common ways I’ve seen this done are with data observability and quality solutions, and data catalogs. Check out my full breakdown via link in the comments for more detail and real world examples.

Should you retarget by intent? We ran the test... Most B2B retargeting looks something like this: Someone visits your site, any page at all…and immediately: they’re getting hit with “Book a demo” or “Start your free trial” ads. No nuance. No context. Just one-size-fits-all messaging chasing every visitor around the internet. It’s simple. It’s easy. But also pretty broken. Here’s why: > Not everyone on your site is in the same headspace. > Blog readers aren’t ready to talk to sales. > Product page visitors are curious but not convinced. And people on the demo page? They’re this close but something’s holding them back. Treating all three the same? That’s how you burn ad dollars without actually building pipeline. So we ran a test. One of our clients had a basic retargeting setup. One campaign. One CTA. One generic message. We broke it apart and rebuilt it based on intent. ___________________________ Here’s how we segmented it: Blog readers Top-of-funnel folks in research mode. → We showed them value-first content: guides, checklists, downloads. Product & feature page visitors Mid-funnel visitors sniffing around the solution. → We served ROI calculators, interactive tools, and “how do you stack up” style CTAs. Pricing/demo page visitors Bottom-of-funnel leads with real buying signals. → They saw direct “Book a demo” and “Start your trial” ads with tons of social proof. ___________________________ Here’s what happened over 60 days: Old campaign (one-size-fits-all): > Low click-through rates (~0.4%) > Modest form fill volume > Demo-to-close rates hovering around 17% New segmented retargeting: > 3.1x higher CTR > 2.4x more total form fills > 29% increase in demo-to-close conversion from high-intent segments ___________________________ Better message-match. Cleaner funnel transitions. Better results.

SaaS company spending $30,000+ on LInkedIn Ads getting $400+ per lead This was 3-4x higher than before. They could not understand why spending more money brought worse results. I looked into their ad account and found three clear problems. 1. Very small target group, very high repetition (frequency) They aimed their “cold” ads at a tiny list of companies. Because the budget was large, the same people saw the same ads over and over...and over...and over.. After a while, those people stopped noticing the ads, so results dropped off a cliff after seeing initial pop of results that gave everyone hope. What we changed: Added nearby groups of similar companies and looked for ways to meaningfully increase audience size without compromising quality. Created new ad versions so viewers did not see the identical message every time. 2. Retargeting settings mixed good and bad visitors Retargeting is meant to show ads only to people who have already visited your site and match your ideal customer. An “AND vs. OR” filter error let thousands of unrelated visitors into the retargeting group, wasting 80 percent of that budget and essentially turning this into a cold campaign will little chance or ROI. What we changed: Fixed the filters so only the right visitors stayed. After the fix, the same budget brought three to 3x more sign-ups. 3. One-track messaging All ads asked for a sale immediately, whether someone was new to the brand or had visited many times. People who did not yet trust the company were not ready to book a call. What we changed: Added trust-building ads: news mentions, customer stories, short interviews with the founder. As trust grew, more visitors moved to the “ready to talk” group, and cost per sign-up fell. 👇 Advice summary a 5th grader could understand. 👇 1. If your group is tiny, big budgets just annoy people. 2. Retargeting needs clean filters. One wrong setting can drain most of the money. 3. Give people information in steps. First, introduce yourself; then build trust; then ask for a meeting. Managing LinkedIn ads is a special skill. A general-purpose ad agency may miss these details. If you already run LinkedIn ads and want a free check-up, reply “Audit” or send me a note. My team will look at your account and point out quick fixes—no pressure, just clear advice. #linkedinAds

A few months ago, we onboarded a client who was frustrated. Their ads were live. Budgets were being spent. But the leads? Barely trickling in. They kept asking: “Are our creatives not good enough?” “Should we increase the ad spend?” But what they really needed wasn’t better ads. They needed a better system. So we rebuilt their funnel from scratch—not by scrapping everything, but by doing what most skip: 🔁 We added smart retargeting for all top-of-funnel traffic ⚙️ We automated follow-ups with personalized triggers 📥 We created lead magnets that aligned with decision stages—not just interest And slowly, things started to shift. 📉 CPL dropped by 42% 📈 Engagement quality rose 🤝 “Maybe later” became “Let’s book a call” That’s when it hit me—again: Great marketing isn’t about the best ad. It’s about the journey that follows the click. If you're spending on paid ads without a retargeting flow, automation, or value-driven follow-ups—you’re not building a funnel. You're just buying impressions. Let’s fix that.

Today I noticed something interesting on LinkedIn. Several ads in my feed were addressing me by my name. Not in the comments. Not in a mention. Right in the ad creative itself — first name, at scale. At first, I wondered if it was a coincidence. Then I realized LinkedIn has rolled out personalized ad capabilities — where advertisers can use real profile data like first name, job title, company name, and industry to dynamically tailor the ad experience for each viewer. No guesswork or creepy scraping. It’s a built-in feature tied to Dynamic Ads and personalization macros that pull directly from a member’s profile at the time the ad renders. What’s fascinating as a marketer is how this blends relevance with scale. Instead of generic spray-and-pray campaigns, brands can create creative templates like: “%FIRSTNAME%, here’s how teams like yours unlock growth.” and LinkedIn replaces the macro with your actual name when you see the ad. We’ve talked for years about hyper-personalization in email and account-based marketing. Now it’s directly in sponsored content — in the feed itself. That’s a big deal for anyone running digital campaigns: it’s not just about better targeting, but about being personally relevant without losing scale. For marketers and advertisers, this feels like a step toward truly intelligent performance marketing on LinkedIn — where message, context, and audience align in a way that’s both respectful and resonant. And for the rest of us? It’s a reminder that in the right hands, advertising doesn’t interrupt — it converses. #LinkedInAds #DigitalMarketing #Personalization #ABM #PerformanceMarketing #SpeakupwithBhumica

If you think gen AI in ads is just about creative automation—think again. One area I've been really bullish on at Disruptive Digital is using AI to create personalized creative at scale. Imagine being able to use AI to generate the right ad for the right user at the right time... Meta's new retail-specific AI tools should help reach that vision of enhancing both user experience and ad effectiveness, including: → Virtual try-ons using AI models to reduce friction → Background generation for Catalog ads → AI-powered product copy that actually converts I'm thoroughly excited about the virtual-try on being able to showcase AI models of different ages, genders and body sizes wearing your products. Why? When people see themselves represented in ads, they are more likely to buy. In fact, Meta is already seeing this is the case... By combining these with dynamic product sets, some brands saw up to a 25% drop in cost per purchase and 23% lift in ROAS! The takeaway? AI isn't just an efficiency lever—it's becoming a core creative partner.

Retail Brands Spending $10M+ on Google Ads: Here’s Where to Look First Big budgets hide big leaks. Most large retail brands running Google Search and Shopping think they have spend under control. Smart Bidding is on. PMAX is live. Dashboards look healthy. ROAS checks the box. But under the hood, margin slips away. Not in flashy places, but in the quiet details that get ignored while teams chase new campaigns. When I audit a large retail ad account, I don’t start with pretty charts or last month’s CPA slides. I go straight to the five basics that quietly decide if you’re scaling profit or wasting budget you’ll never claw back. 1. Feed Health Your Merchant Center feed is your real storefront. If your product titles are sloppy, GTINs missing, stock data outdated or categories messy, Smart Bidding cannot fix it. A weak feed means your best products stay buried. It is not glamorous work, but it is always the first place I find hidden margin. 2. Branded Overlap Many big retailers pay for the same branded clicks twice. PMAX scoops up branded terms while exact match Search campaigns bid on them too. Broad match expansions grab brand queries on top. Looks fine on a volume chart, but you are paying extra for what you would likely win organically or with a fraction of the spend. Fixing this is simple. Fence brand terms in PMAX, tighten negatives, monitor overlap weekly. I have seen this one change claw back six figures overnight. 3. Geo and Device Leaks Are you paying for clicks in zip codes you do not even deliver to? Is mobile CPA drifting up while the blended CPA hides the bleed? Small geo and device gaps do not matter at small scale, but at $10M+ yearly spend, they add up fast. 4. Bad or Duplicate Signals Smart Bidding works when the data is real. One duplicate conversion tag doubles your CPA overnight. Tracking weak leads as real conversions tells the algorithm to chase junk. The more you automate, the more critical clean signal hygiene becomes. 5. Offline Conversion Gaps Most retailers do not push in-store or phone sales back to Google Ads. That means Smart Bidding only learns half the profit story. Connecting offline conversions is one of the easiest ROAS lifts at scale. Why It Matters More brands trust Smart Bidding and PMAX to just work, but AI is only as smart as what you feed it. Google will not tell you that you are overspending on overlap or tracking duplicate sales. Your margin lives in the boring checks that slides never show. If you are managing a big retail budget, I've put together a short video showing exactly where to look and what to fix. Additionally, you can gain access to Guide/ Report (Latest in Ads Post Google Marketing Live Event 2025) and get complimentary ad account audit. If you want these, or a quick check of your feed, signals and overlaps, comment 'Interested' or DM me. I will share it with you, no sales pitch. Better spend beats bigger spend every time.