Common Issues in Ad Production Automation

🧩 When Programmatic Deals Won’t Scale 📈 A few months ago, our Global Head of Partnerships, Mike Masiello, was getting frustrated. His team had setup dozens of deals — but some just wouldn’t scale. Sound familiar? His frustration has kicked off one of our most valuable new habits: Deal Debugging Calls. What started as a quick huddle between Ad Ops, Account Executives, and Management is now a structured process we run three times a week — two in North America and one in APAC — to uncover why some deals stall even when everything looks right on paper. 🔍 How We Debug a Deal Each week, Account Executives flag 3 underperforming deals — on the shortlist should be deals getting bid requests, bid responses, and impressions matched, but with low sell-through. Before the call, each AE runs a Deal Debug Report (Internal) covering the past seven days of data. It takes 5–7 minutes to run and replaces a dozen manual reports. We also check buyer seat-level data — open exchange activity, pacing, and delivery mix. On the 30-minute call, we spend 10 minutes per deal reviewing issues live. During the call Ad Ops logs JIRA tickets to track updates (typically reflected within a week). And more often than not — once we talk it through and tweak the deal starts scaling. ⚙️ The Common Culprits 1️⃣ Creative Issues Wrong size, format, or incorrect audit status. Small setup errors often block big budgets. 2️⃣ Overly Tight Allowlists Buyers sometimes restrict inventory too much. We’ve built recommendation lists showing “if you’re buying this, you should also buy that.” 3️⃣ Automated Auditing Isn’t Perfect (Yet) Last week, a face cream ad was flagged as a dating app (buyside DSP: The Trade Desk). Since many publishers block dating, the deal stalled. After Xandr manually reviewed 138 creatives, the deal jumped back to life. 4️⃣ Advertisers Over-Segmenting Some layer multiple audience segments, cutting scale — then ask for contextual deals that narrow it further. It’s not a tech issue; it’s targeting. 5️⃣ CTV Complexity CTV brings its own challenges — from duration mismatches to targeting constraints. We’re now building a dedicated CTV Debugging Report to manage this more effectively. 🤝 The Bigger Lesson Programmatic was meant to automate everything — but the truth is: Automation is powerful, but it’s still human collaboration that solves problems. We take our findings from each session and send a detailed follow-up to the buyside trader. We also update the intranet for future reference. And when all else fails, we jump on a quick call with the trader, Ad Ops, and AE — sometimes even bringing in their DSP contact when we’ve tried everything but run out of ideas. Over the years I’ve learned stacks on tweaking and deal debugging from folks like Mike McNeeley, James Bird, Pat Diggins and Shilpa Kolte + my Ad Ops team. How do you debug your deals? #Programmatic #AdOps #programmaticDeals #DealDebug #ProgrammaticAdvertising #CTV

Most agencies are using AI for the wrong things. They're obsessing over creative generation while their real money drains are happening in the boring operational gaps. We didn't start with AI making ads. We started with AI fixing the delays that were actually costing us revenue. File handoffs taking 30 minutes per ad. Quality control catching errors after client review. Manual hunting for winning creatives. Basic admin work disguised as strategy time. These aren't sexy problems to solve. Nobody's making viral LinkedIn posts about automated file organisation. But when you're pushing 500 ads weekly, those 30-minute delays compound into massive operational costs. Our biggest AI wins come from process improvements that nobody sees. Automatic notifications when work gets stuck. Quality checks before anything reaches clients. Smart file naming that actually works. While other agencies are prompting ChatGPT to write ad copy, we're eliminating the bottlenecks that prevent ads from launching on time. One saves you an hour of writing. The other saves you days of delays and client complaints. The unglamorous AI applications are where the real money is. Because they solve problems that directly impact your bottom line. Your biggest operational headaches are probably perfect candidates for automation. But they're not the shiny AI use cases that get conference talks. What boring, expensive delays in your workflow could be automated away?

16. Lack of Ad Scheduling (Dayparting Issues) ❌ Problem: Ads run during non-optimal hours, wasting budget. ✴️ Reasons: • No scheduling settings. • Ignoring peak user activity hours. ✅ Solution: • Analyze Performance by Hour/Day: Use the “Time” report to find high-converting periods. • Set Ad Schedules: Show ads during peak hours and pause them during off-hours. • Increase Bids for Peak Times: Use bid adjustments to prioritize performance hours. ❇️ Example: A local electrician finds most leads come in from 7 AM to 9 PM. • Schedule ads only during these hours. • Increase bids by 20% between 6 AM and 9 AM for urgent morning calls. 💹 Result: CPL (Cost Per Lead) drops by 25%. 🔰 Strategy: 1. Regularly review the Ad Schedule report. 2. Use automated scripts to optimize schedules dynamically. 17. Overlooking Seasonal Trends ❌ Problem: Campaigns perform inconsistently during seasonal peaks or lows. ✴️ Reasons: • Ignoring demand fluctuations. • Not tailoring ads for seasonal relevance. ✅ Solution: • Analyze Historical Data: Use Google Trends to predict search volume spikes. • Create Seasonal Campaigns: Design ads for upcoming events, holidays, or trends. • Adjust Budgets: Increase budgets for peak seasons. ❇️ Example: A travel agency notices higher search volume for “Christmas vacations” in November. • Create ads like “Book Early for Christmas Deals!” • Increase the budget by 30% for November and December. 💹 Result: Campaign revenue grows by 40% during the holiday season. 🔰 Strategy: 1. Set calendar reminders for key seasonal periods. 2. Test Countdown Ads for limited-time offers. 18. Poor Conversion Tracking Setup ❌ Problem: Campaign performance is unclear due to missing or inaccurate tracking. ✴️ Reasons: • Conversion actions not defined. • Incorrect Google Tag Manager (GTM) setup. ✅ Solution: • Set Up Proper Conversion Tracking: Define key actions like purchases, form submissions, or calls. • Use Enhanced Conversions: Improve tracking accuracy with first-party data. • Test Tracking Implementation: Verify using the Google Tag Assistant. ❇️ Example: An eCommerce store tracks only page views, not purchases. • Set up “Thank You” page tracking as a conversion in Google Ads. • Integrate Enhanced Conversions for better data. 💹 Result: Campaign ROAS becomes measurable and actionable. 🔰 Strategy: 1. Regularly audit conversion tracking. 2. Combine GA4 events with Google Ads for comprehensive reporting. #PPCAdvertising #DigitalMarketing #GoogleAds #SEM #MarketingStrategy 

If your ads remain stuck in the learning phase, Meta’s advertising platform is barely yielding results. Here are the three most common issues that prevent ad accounts from exiting the learning phase. 1. Lack of Consolidation: If each ad set receives fewer than 50 conversions per week, your account is stuck in “Meta Lite.” To resolve this issue, combine your ads into a single campaign and ad set that can exit the learning phase more quickly. 2. Insufficient Creative Versions: Limiting your ads to a single image size or text variation hinders performance. To address this, provide multiple creative formats (square, vertical, horizontal) and 3–5 text variations per ad. 3. Relying on Shopify’s Basic/Free CAPI: Shopify’s free Conversions API is a basic relay that often lacks essential data, such as Facebook click IDs (fbc) and Facebook session IDs (fbp). This lack of data leads to misattribution of conversions. To rectify this issue, upgrade to an advanced data connection (like Popsixle) that provides more accurate data, enabling better ad optimization and reducing the likelihood of misattribution. By addressing these three pitfalls, you significantly increase your chances of exiting the learning phase. Once your ads are out of the learning phase, they become more effective, stable, and profitable. .