The Challenge
Reliance Retail's performance campaigns were running across 6 cities with significant CPL variation — Tier 1 cities running at ₹290 CPL while Tier 2 campaigns ran as high as ₹680. There was no systematic approach to city-level budget allocation, bidding strategy, or creative variation. As expansion to 12 more cities was planned, a manual approach to management would have made the account unmanageable and allowed the CPL variation to compound.
The Solution
Each city structured as its own campaign set with city-specific keyword themes — covering local brand terms, category searches, and location-intent modifiers. Bid strategies set independently per city cluster based on local CPL benchmarks.
Adtrafix OS tracked CPL by city in real time. Weekly budget shifts moved spend toward lowest-CPL cities automatically — ensuring total budget always weighted toward highest-efficiency markets.
AI content calendar generated city-specific content variations — local festivals, regional language posts, and city-specific product availability. Posting cadence maintained consistently across all 18 markets without proportionally increasing production time.
PMax campaigns used for city launch — fed with city-specific audience signals and creative assets. Brand Search campaigns run alongside to protect branded traffic in new markets.
Results
The combination of structural changes, AI-driven optimisation via Adtrafix OS, and weekly cadence produced measurable improvement starting in week 3, compounding through month 3:
Adtrafix OS automated city-level budget reallocation weekly — ensuring spend always concentrated in highest-efficiency markets without manual intervention.
Scaling to 18 cities would have required doubling our marketing team. With Adtrafix OS doing the heavy lifting, we expanded faster with the same headcount.
— Head of Digital Growth, Reliance RetailScaling Performance Marketing Across 18 Cities Without Scaling the Team
The conventional wisdom in performance marketing is that scaling a campaign geographically requires proportionally scaling the team managing it. More cities means more campaigns, more reporting, more bid adjustments, more content variations. A 3× expansion from 6 to 18 cities would conventionally require 3× the management bandwidth. Adtrafix OS changed this equation fundamentally.
The City-Level Campaign Architecture
Each of the 18 cities was structured as a self-contained campaign cluster with three key elements: brand and category keywords specific to that city's search volume profile, bid strategies calibrated to that city's observed CPL benchmark, and location extensions pulling from the city's GMB profile. Adtrafix OS treated each city cluster as an independent performance unit — tracking CPL, conversion rate, and lead quality separately.
This granular structure revealed something that the previous 6-city account had masked: the CPL variation between cities was not primarily driven by competition levels (as assumed) but by audience quality. Cities with higher smartphone penetration and better 4G coverage showed 40–60% better trigger rates than lower-connectivity Tier 2 markets, despite similar or lower Generation CPL. This insight drove a deliberate shift toward mobile-first ad formats in high-trigger cities and a more volume-oriented approach in lower-trigger markets.
The 4-Day City Launch Process
Before Adtrafix OS, launching a new city campaign required 2–3 weeks of manual setup — keyword research, ad copy, extensions, bid strategy configuration, GMB profile setup. With Adtrafix OS's campaign replication framework, a new city could be launched in 4 working days: Day 1 template replication and city-specific keyword adjustment; Day 2 GMB profile optimisation for the new location; Day 3 localised social media content calendar for the city; Day 4 go-live with monitoring activated. This compressed timeline meant the 12-city expansion happened in parallel over a 3-week period rather than sequentially over 9 months.
The Budget Intelligence Layer
Every Monday morning, Adtrafix OS generated a budget reallocation recommendation based on the previous week's CPL by city. Cities performing below their CPL target received a budget increase; cities above target received a reduction. The total monthly budget remained constant — only the distribution changed. Over 12 weeks, this automated reallocation concentrated spend in the 6 highest-efficiency cities, which accounted for 71% of total leads while representing only 42% of total spend. The remaining 12 cities were maintained for brand presence and data accumulation rather than lead volume optimisation.
Key Takeaway
This case demonstrates what changes when AI is embedded into the operating model rather than used as an occasional tool. The results compound weekly — each optimisation feeds the next — rather than spiking once and stabilising. Adtrafix OS running behind every decision is what separates a one-month win from a structural improvement.
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