Two years ago, the standard Meta Ads advice for India was: "Stack 5–8 interests, narrow by behaviours, exclude irrelevant audiences." Today, that advice is mostly wrong. Here's what our data from 100+ Indian Meta Ads accounts shows about broad targeting in 2025.
What Changed with Meta's Algorithm
Meta's algorithm has fundamentally changed since 2020. The shift started with iOS 14+ (which reduced third-party data available for targeting) and accelerated with Meta's investment in AI-powered Advantage+ products. The algorithm now relies heavily on first-party signals — what people actually do on Facebook and Instagram — rather than declared interest graphs.
What this means in practice: Meta's algorithm in 2025 is vastly better at finding the right people within a broad audience than an advertiser is at hand-picking interests. When you restrict the audience with 8 interest layers, you're actually constraining the algorithm's ability to optimise — forcing it to show ads to a limited pool rather than finding buyers anywhere in a larger population.
The Data: What We've Seen Across 100+ Indian Accounts
| Audience Type | % of Accounts Where This Won | Average CPL Difference |
|---|---|---|
| Broad (age/gender/geo only) | 68% | 22% lower CPL vs interest |
| Lookalike 1–3% | 72% | 18% lower CPL vs interest |
| Interest-based (3–5 interests) | 28% | Baseline |
| Narrow interest (5+ stacked) | 8% | 35% higher CPL |
| Advantage+ Audience | 61% | 19% lower CPL vs manual |
Data from 100+ Indian Meta Ads accounts managed by GrowthVolt team, January 2024 – June 2025. All accounts spending minimum ₹30,000/month.
Why Broad Targeting Works: The Algorithm Explanation
Here's the core reason broad targeting outperforms detailed targeting in most cases:
More data = better optimisation. When you run broad targeting on a ₹2,000/day budget in Delhi NCR (reaching a potential audience of 8M people), Meta's algorithm has millions of people to learn from. It can identify subtle patterns in who converts — job titles, browsing behaviour, past purchase patterns — that no advertiser could manually identify through interest stacking.
When you stack 7 interests and narrow to a 250,000-person audience, you've taken that learning opportunity away. The algorithm is now constrained to optimising within a small, potentially wrong pool of people.
When Broad Targeting Consistently Wins (5 Indian Verticals)
| Vertical | Why Broad Works | Recommended Geo |
|---|---|---|
| E-Commerce / D2C | Large purchase intent audience; algorithm finds buyers better than interest stacking | Metro cities + Tier 1 |
| Real Estate (under ₹1Cr) | Wide buyer pool; price accessibility means any working adult is a potential buyer | NCR + feeder cities |
| Education (Online Courses) | Course buyers span all demographics; interests like "education" are too vague anyway | Pan-India |
| Healthcare (Diagnostics) | Everyone needs diagnostics; broad finds the high-intent health-conscious segment | City-level |
| FMCG / Food | Universal product; no need to narrow what is already a universal audience | Pan-India |
When Detailed Targeting Still Wins
Broad targeting is not always the answer. Here are the cases where interest-based targeting still outperforms:
- Luxury real estate (₹2Cr+): You genuinely need to filter by income signals. Layer: "High-income households" + "Frequent international travellers" + city
- NRI investor targeting: Target India-origin users in UAE, USA, UK, Singapore — this geographic+demographic filter is precise and necessary
- Very niche B2B products: HR software, medical equipment, legal services — where job title/industry targeting narrows to genuine buyers
- Small budgets (under ₹500/day): With very limited budget, some interest narrowing prevents the algorithm from spreading spend too thin
How to Test Broad vs Detailed Targeting
Don't just switch — test first. Use this CBO test structure:
Create a CBO campaign with ₹2,000/day minimum
CBO (Campaign Budget Optimisation) is essential for this test — it lets Meta allocate budget to the better-performing ad set automatically.
Ad Set 1: Broad (age + gender + city only)
No interests. No behaviours. Just your target demographic in your target geography. 3 creatives.
Ad Set 2: Your Current Interest Targeting
Whatever you're currently running. Same 3 creatives as Ad Set 1.
Run for 14 days minimum, then compare CPL and lead quality
Don't judge after 3 days — the algorithm needs time (50+ events) to optimise each ad set. Check CPL AND lead quality (call back rate, site visit rate).
Advantage+ Audience Explained
Meta's Advantage+ Audience (formerly "Audience Expansion") is the most extreme form of broad targeting — you give Meta suggested targeting as a starting point, and it can expand beyond that if it finds better results elsewhere. In our tests across 40+ Indian accounts, Advantage+ Audience delivers results in the top quartile for 61% of accounts. Worth testing, especially for e-commerce and D2C.
The Correct Broad Targeting Campaign Structure
For Indian businesses switching to broad targeting, use this structure:
- Campaign: Lead Generation, Advantage Campaign Budget (CBO), ₹2,000+ per day minimum
- Ad Set 1: Broad — target demographic only (age, gender, city). No interests.
- Ad Set 2: Lookalike 1–3% from your best customer list (if you have 500+ customers)
- Ads: 3–5 creatives per ad set. Let the algorithm find which creative works with which audience.
- Run for 14 days before making any major changes. Trust the process.
Not sure how to structure your targeting?
Our team will audit your current targeting and recommend the right approach for your business — free.