Why Digital Platforms Get Ad Targeting Wrong (And What It Reveals About Digital Algorithms)

Last night, I was unwinding with a gripping Malayalam crime series on one of India’s top OTT platforms. It was intense. Suspenseful. And just when the mystery thickened - bam! An ad for ladies' beauty products.

A few scenes later, another ad—this time for dish washing soap and the same sequence of similar advertisements kept playing of throughout the time I was watching..

This experience got me thinking—why was I being fed advertisements of products which I had no interest on even after I had entered my sex and age as requested by the platform in between..

The Promise vs. Reality of Ad Personalization

In theory, ad platforms are built on sophisticated algorithms designed to serve you the “right ad, at the right time, on the right screen.” But in practice, it often feels like we’re stuck in an analogue platform where advertisements are being send for the full audience at pre-defined periodicity..

The key driver of digital advertising is targeting—the ability to place relevant ads in front of the user most likely to engage or convert.

This is supposed to be driven by:

• Demographic Data

• Behavioural Data

• Device & Location Data

• Content Context

• Psychographic Insights

Yet, in my case, clearly, none of these matched the ads I was being shown. So what's going on?

The Algorithms Behind Ad Targeting

Let’s unpack the actual mechanics behind modern-day ad delivery systems, especially on digital platforms:

1. Heuristic Targeting (Basic Rules-Based Logic)

This is the oldest form of targeting. Platforms use generic rules like “Male + Age 35-55 + Kerala + Malayalam Content = Show FMCG Ads.” It’s crude and assumes outdated household decision-making models.

Problem: It ignores nuanced behaviour and personal preferences.

2. Content-Based Targeting (Contextual Advertising)

Some platforms scan the type of content being watched and assume relevance. Watching a family-friendly show? You might see ads for detergent. Watching a thriller? Maybe tech gadgets.

Problem: Content ≠ Intent. I watch crime shows to relax, not to shop.

3. Audience Segmentation (Lookalike Modelling)

This technique uses profiles of people "similar to you" and pushes ads that worked well for them.

Problem: If the training data is outdated or based on poor assumptions (e.g., traditional family roles) or based on the criteria’s of a different country it perpetuates irrelevance.

4. Retargeting (Cookies & Cross-Device Tracking)

This is supposed to be the gold standard. Based on your web searches, app activity, and e-commerce behavior, platforms show you ads tied to your recent actions.

Problem: Privacy regulations (like GDPR and India’s DPDP Act) and Apple's ATT have weakened this dramatically.

5. AI/ML Predictive Targeting (Next-Best Action Models)

The most advanced systems use deep learning to predict what ad you are most likely to watch or act upon—even if you haven't explicitly expressed interest.

Problem: Poor training data + biased feedback loops = mismatched ads.

Where It All Breaks Down: Digital Ecosystems

Digital platforms are notoriously complex:

• User accounts are shared by families.

• Watch patterns vary widely by mood.

• Login credentials are often on multiple devices (TV, phone, tablet).

• First-party data is often shallow or segmented.

Result? Most platforms rely on aggregate household data, not individual identity.

In my case, the system might assume:

• "Malayalam male user = middle-class family = detergent buyer.”

• Or worse: “TV login = housewife + children + Rejo = feed safe FMCG ads.”

What’s the Fix?

✅ Hyper-Granular User Profiling (With Consent)

Shift from household to individual user identity, using multiple signals—not just one profile per device.

✅ Behavior-Driven Targeting Over Demographics

Stop assuming roles based on gender or language. Let behavior be the anchor, not stereotyping.

✅ Real-Time Feedback Loops

Enable viewers to “thumbs up/down” ads or mark irrelevance. AI thrives on feedback. Most platforms  don’t collect this.

✅ Contextual AI Based on Emotional State

Ads during crime thrillers should match the emotional state—serious, intense, intellectual—not frivolous or domestic.

✅ Transparent Opt-Outs & Personalization Controls

Give users agency. Let them set ad preferences, like Spotify or Google Ads allows at times.

The Bigger Question: Who’s Really Watching?

Ad tech must adapt to the evolving digital behavior of users who are digitally savvy, consume niche content, and expect relevance.

It’s not about age or gender. It’s about intent, context, and respect.

If the algorithms can't keep up, brands and platforms will lose money. Worse, they’ll lose attention.

The platforms that fix this will win user trust . The ones that don’t? They'll just be background noise in our binge-watching lives.

If you want to understand how digital ecosystems really work, follow me here and subscribe to Rejo’s Business Bytes to keep getting regular updates.

Because the next digital disruption won’t be about data. It’ll be about relevance.