How platform algorithms impact business owner trust

Can you really trust the ad performance data coming from the platforms who serve the ad themselves? A recent AMA (American Marketing Association) study highlights a critical flaw in how A/B testing is run on digital platforms like Google & Meta. The study found that these platforms don’t truly randomize who sees what. Instead, they serve ads to users they believe are most likely to respond - based on their own algorithms. This is a good idea for maximizing engagement with ads but it’s BAD research. Research 101 states that everyone within the target group should have an equal chance of being selected for research but that’s not happening here. Demographically representation ≠ Psychographic representation The result? Ads that “win” in such studies may simply be those that reached more receptive users - not necessarily the better idea. That’s fine if you’re trying to optimize in-platform performance. But if you’re using those results to guide broader brand or media decisions, or applying learnings across markets and formats, the picture gets murky fast. That’s where independent market research firms come in - especially those using agile, experiment-led approaches. Unlike the publishing platforms, they have no incentive to nudge outcomes in any direction. The credible ones apply rigorous, unbiased sampling and testing so you can isolate what’s truly working from what’s just algorithmic match-making. In today’s complex and noisy media landscape, speed matters - but so does clarity. Fast research doesn’t have to mean compromised research. Will strongly recommend to choose partners who help you see past the platform and get closer to the truth - they will work for you and have your interest in mind. The impact on your creative, comms and budget decisions and thus the business impact can be transformative. #AdTesting #MarketingScience #AgileResearch #MarketResearch #CreativeEffectiveness #ABTesting

Mark Zuckerberg just outlined a future where Meta's AI handles everything from creative generation to campaign optimization to purchase decisions. His vision: businesses connect their bank accounts, state their objectives, and "read the results we spit out." The technical architecture he's describing would fundamentally reshape how advertising technology works. But there's a critical flaw in this approach that creates an opportunity for the next generation of advertising infrastructure. The trust problem isn't just about measurement transparency—though agency executives are rightfully skeptical of platforms "checking their own homework." The deeper issue is institutional knowledge transfer and real-time brand governance. Enterprise brands have decades of learned context about what works, what doesn't, and what could damage their reputation. This isn't just about brand safety filters. It's about nuanced understanding of seasonal messaging, competitive positioning, cultural sensitivities, and customer journey orchestration that can't be reverse-engineered from campaign performance data alone. If AI truly automates the entire advertising stack, brands will need their own AI agents—not just dashboards or approval workflows, but intelligent systems that can negotiate with vendor AI in real-time. Think of it as API-level conversation between two AI systems where the brand's AI has veto power over creative decisions, placement choices, and budget allocation. This creates fascinating technical challenges: How do you architect AI-to-AI communication protocols that maintain brand governance while enabling real-time optimization? How do you build systems that can incorporate institutional knowledge without exposing competitive advantages to vendor platforms? We're talking about building advertising technology that functions more like autonomous diplomatic negotiation than traditional campaign management. For platform companies pushing toward full automation, the question becomes whether they're building systems that enterprise clients can actually trust with their brands and budgets. For independent technology builders, there's an opportunity to create the middleware that makes AI-powered advertising actually viable for sophisticated marketers. The future of advertising isn't just about better algorithms—it's about building trust architectures that let those algorithms work together.

⚠️Instagram’s Reels Meltdown: When Algorithms Turned Chaos Into “Engagement” This week, Instagram’s Reels feed exposed a massive failure in AI-driven content curation. Users reported an influx of graphic violence, explicit material, and disturbing posts—even if they never engaged with such content. Here’s what this means for brands, marketers, and digital leaders: The Algorithm Glitch That Broke Trust What happened?  Meta’s recommendation system pushed inappropriate content into mainstream feeds. Why it’s alarming?  Sensitive Content Controls failed—meaning even cautious users weren’t protected. The irony?  Posts from years ago resurfaced with viral momentum—proving algorithms lack context and nuance. Meta’s response?  “We’re working on it.” But users aren’t waiting around. 3 Harsh Realities for Brands 1️⃣ Algorithms Don’t Have Ethics – They chase engagement, not responsibility. 2️⃣ Your Brand’s Reputation Is at Risk – What happens when your content is placed next to disturbing material? 3️⃣ Users Have Lost Patience – Trust in platforms is fragile. A few days of chaos can permanently shift brand perception. The Bigger Conversation ❓AI’s Blind Spots: How can a $800B company fail to filter outdated, harmful content? ❓Free Speech vs. Platform Responsibility: Should social platforms limit algorithmic amplification of “edgy” content? ❓Who Pays the Price? Hint: Not Meta. It’s the users and brands caught in the mess. What Smart Leaders Should Do Next This isn’t just an Instagram issue—it’s a wake-up call for anyone relying on AI-driven platforms: ✅ Audit how algorithm failures could impact your brand ✅ Demand transparency from tech giants ✅ Build strategies that don’t depend solely on platforms you can’t control Hard truth: If your business relies only on rented platforms, you’re gambling with your reputation. 👉 YOUR TURN: Would you trust Instagram with your brand’s content right now? How should Meta rebuild trust? Drop your take below—let’s talk about fixing social media. 👇 ♻️ Repost to help other 📍 Follow Sivanandan N. for more PS: If your feed felt like a horror movie this week, share this post. Let’s hold platforms accountable. ----------------------------------------- #AIEthics #SocialMediaCrisis #Instagram #NSFW #DigitalTrust #BrandSafety #TechResponsibility #ContentModeration #UserPrivacy #AIFailure

Building on platforms can give you instant distribution and cut your CAC in half. But… It can also cut your exit multiple in half. This is a serious downside most founders overlook. Platform dependency kills valuations in M&A. In the 5+ years I’ve been doing this, I’ve seen what happens when companies become too reliant: • Salesforce and Atlassian apps becoming obsolete overnight when platforms launch native features • Shopify apps getting kicked out of the marketplace or restricted from accepting more customers because they became "too successful" • Companies getting sued for building tools on platforms that ban them in their T&Cs - think LinkedIn automation Why? Because in all of these cases, the platform owns the game board. Policies change overnight. Algorithms shift and kill your distribution. New features launch without warning - and you’re suddenly irrelevant. A few examples: Noosa Labs ran into serious trouble after acquiring a WhatsApp business Guillaume Moubeche losing his LinkedIn account over and over Basecamp’s Hey email app launch delayed and forced to change to comply with Apple's policies. Tweethunter got shut down after Elon took over (now back up again). Our portco Juicer's margins suffered when Twitter raised API prices significantly. And there are plenty more. I’ve seen companies lose 50-70% - sometimes even 100% - of their revenue when platforms pivot or take action. There aren’t many buyers who will even consider acquiring a platform-dependent business. And those who do will price in the risk - heavily. Use platforms as a launchpad, not a foundation. Try to diversify your risk early. It will make it much easier to find a new home for the business when you’re ready to exit - and far more likely you'll get the valuation you’re aiming for. #saas #startups #exitplanning #salesforce #atlassian

In an e-commerce marketplace, a seller’s business depends heavily on their visibility on the platform. This visibility is either organic or driven through promotions. In general, platforms focus on improving overall outcomes—be it number of orders, revenue (including ads), or reducing costs (tech, operations, etc.). Ultimately, the goal is to increase the top-line and improve the bottom-line. In my previous org, as an engineer, I started looking at these problems from a business-first lens. You have millions of items in inventory, a customer visits the platform and either ends up buying something or leaving. To increase the top-line, you work on improving customer buying frequency (through nudges, introducing them to new categories, etc.), upselling items (buy more or buy higher-ticket items), and showing relevant ads (more clicks, better ROI). So it’s a mix of exploration and exploitation. The bottom-line improves through operational efficiencies—right matching and batching of orders, optimal supply planning, etc. Time efficiency becomes even more critical in quick commerce (deliveries within 1 hour). Ultimately, a lot of this is controlled by what you show to the customer—so Search & Discovery plays a key role. Having worked on this firsthand, I’ve contributed to a variety of tech initiatives that improved revenue and increased profitability. We always measure these numbers at the platform level. But what about the sellers? Improvements in platform-level metrics don’t always translate to better business for every seller. I recall instances where a change in the recommendation algorithm impacted a few sellers’ businesses overnight. Sellers have their own growth journeys that can be severely affected by just a few lines of code. In my opinion, changes to recommendation algorithms and new product offerings should be gradual—sellers need time to adapt to shifting consumer behaviour. While ranking methodologies are not usually public, sellers should have access to tools that improve their visibility. They should be able to track growth not just through order volumes, but also through customer engagement—time spent on the product page, drop-off reasons, etc.—and receive actionable recommendations to improve on those. I’ve seen consultants build businesses around helping sellers grow on these platforms. I believe those capabilities should be part of the platform itself. Some of these tools can even be monetised (beyond just ads). All in all, a platform will be successful and preferred if it truly offers equal and unbiased opportunities to all its sellers. #ecommerce #recommendation #sellerplatform #ranking #ads