Company Overview
Apna is a job discovery and hiring platform connecting employers with job seekers across India. NYX ran Google Performance Campaigns, Search, Display, and Performance Max, for Apna's employer-side acquisition, aiming to optimize campaign efficiency and ROAS over a Dec'24-Jan'25 engagement.
Challenges
NYX diagnosed a connected set of problems spanning cost, campaign structure, engagement, and conversion:
High CPC & Budget Inefficiencies
CPC on job-related search terms ranged between ₹45 and ₹60 per click, 30-50% higher than the industry benchmark of ₹25 to ₹35, driven by intense competition for keywords like “hire cashier,” “hire digital marketing expert,” and “website developer required.”
Underperformance of Performance Max Campaigns
Budget misallocation across placements and audience segments was reducing overall campaign efficiency, with P-Max spend not consistently reaching the highest-performing inventory.
Wasteful Ad Spend
15-20% of employer campaign spend was wasted on underperforming keywords and irrelevant display placements, with budget also being consumed by low-intent keywords like “how to post a job in Apna app” and “job post on Apna,” searches from people looking for the platform itself rather than employers looking to hire.
Low CTR & Engagement Issues
CTR dropped across Search, Display, and P-Max campaigns since Dec'24. Employer-focused queries (“hire office coordinator,” “hire HR manager,” “hire site supervisor”) had 25-35% lower CTR than industry benchmarks, driven by ineffective ad copy and generic messaging that reduced ad relevance. Display ads faced even steeper challenges, high impression volumes but low interaction rates, primarily due to broad targeting and a lack of audience segmentation.
Conversion Drop-Offs & Roadblocks in the Funnel
ROAS on P-Max campaigns dropped to 1.8x in Dec'24-Jan'25, against an industry benchmark of 3.5x to 4.2x. Search campaign ROAS declined from 4.5x in early 2024 to 3.2x by Jan 2025, with the budget allocated to low-intent audiences reducing overall returns.
NYX Solution
NYX's AI-driven approach worked across four connected areas: bidding and budget, keyword and placement quality, creative, and audience targeting.
Bid Adjustments & Budget Allocation
Analyzed auction data, audience behaviour, and conversion probabilities to shift budgets from underperforming placements to high-performing ones, dynamically adjusting CPC bids and reallocating budget to maximize conversions while minimizing cost.
Keyword & Placement Optimization
Automatically identified irrelevant search terms, negative keywords, and placements draining budget without driving results during Dec'24-Jan'25, comparing that data against the account's stronger prior-period performance. Ads were kept focused on high-intent search queries and quality placements while wasteful traffic was eliminated.
Creative Enhancements & Optimization
Ran A/B testing on ad copy, adjusting messaging dynamically based on audience segment, and flagged long-running creatives that had started showing declining conversion rates for removal. Ad creatives and landing pages were optimized based on actual user engagement and conversion patterns.
Advanced Audience Segmentation & Targeting
Identified high-converting user segments from past data, applied predictive lookalike audiences, and adjusted targeting based on real-time job application trends, building refined audience segments from job seekers' intent, past engagement, and behavioural data.
The AI model running these four areas, bid and budget shifts, keyword and placement cleanup, creative testing, and audience refinement, is the same category of workflow NYX Campulse, our AI-powered campaign co-pilot, is built to run continuously rather than through periodic manual review. For an account juggling Search, Display, and P-Max at once, that's what kept all three optimized against the same conversion signal instead of managed as three separate campaigns.
Business Impact
The deck frames the result as 75% better performance achieved with roughly 95% less manual effort. Across every core metric tracked, CTR, ROAS, and wasted spend, the optimization moved Apna's Google campaigns from below industry benchmark to at or above it.
Key Takeaway
NYX’s AI-driven optimization helped Apna improve Google campaign performance by continuously optimizing bids, budgets, keywords, placements, creatives, and audiences across Search, Display, and Performance Max—delivering 75% better performance with ~95% less manual effort while reducing wasted ad spend and improving ROAS.