Precision Over Spend: How a Brand Scaled with Adsify & SellerMate.AI
- Aastha Jain
- Jun 27
- 8 min read
About the Agency
Adsify Digital is one of India’s leading e-commerce advertising agencies, recognized by Amazon three times for excellence in performance marketing. Known for their hands-on approach and advanced PPC execution, Adsify helps brands scale on Amazon through strategy-first campaign management, full-funnel optimization, and performance-focused automation.
About the Client
A fast-growing personal wellness and lifestyle brand founded in 2022, focused on Ayurvedic solutions for varicose veins, competing with brands like Dr. Ortho, Himalaya, and Boldfit, it holds a 12% share in its niche. Its focused product line and performance-led growth make it a rising category specialist.
Initial Objectives
The advertiser sought to scale advertising-driven revenue while maintaining strict efficiency benchmarks. Key goals at the beginning of the engagement included:
At the time of onboarding, the brand held a 12% market share on top-performing keywords in the varicose vein category (based on Brand Analytics SQP reports). A core objective was to increase this share to 20% within 12 months, signaling stronger category presence and new customer acquisition.
To ensure profitability during scale, the advertiser aimed to keep ACoS consistently below 30%, making efficiency a non-negotiable priority throughout the campaign duration.
The target was to grow market share to 20% within 12 months.
A key performance constraint was to keep ACoS consistently below 30% to ensure profitability
Discovery
During the initial audit phase, our team conducted a detailed performance diagnosis using internal tools, ad manager logs, and heatmap dashboards provided by tech partner. This surfaced multiple inefficiencies that had been limiting campaign effectiveness.
The first and most prominent discovery was that over 70% of ad spend was concentrated on branded and bottom-funnel campaigns. Using the targeting report, we identified that these campaigns delivered strong ROAS but lacked impression diversity. Non-branded keywords contributed to less than 20% of traffic, which indicated a heavy dependence on existing demand and a missed opportunity in acquiring new customers / audiences.
A second major finding came from the hourly performance heatmaps. These showed that 20% of daily spend was occurring during off-peak hours (12 AM to 8 AM), where ACoS averaged 54% significantly higher than daytime hours (28%–32%). This budget was being consumed without any bidding or scheduling controls in place.
This inefficiency remained hidden because the spend was distributed across multiple campaigns, making it easy to overlook.
We also discovered that campaign optimization cycles were slow and reactive. Ad manager logs revealed that bid changes and keyword actions were typically executed every 10–14 days, which meant underperforming targets were often live for weeks before action was taken.
While campaign adjustments were being made regularly, SellerMate’s Ad Manager Logs revealed something unexpected: bid changes and keyword actions were executed every 10–14 days, meaning underperforming targets remained live for weeks before corrective action was taken. The problem wasn’t visible because individual performance metrics seemed stable.
Finally, an audit of creative assets across Sponsored Brands and Sponsored Display revealed that top-of-funnel creatives had not been refreshed in months. CTRs had dropped to 0.25%, far below the category average. This low engagement rate suggested poor performance in awareness campaigns and inflated CPCs due to weaker ad relevance.
These insights gathered across targeting patterns, time-of-day performance, optimization cadence, and creative analysis laid the foundation for understanding what was holding the account back.
The Challenge
The issues identified in the discovery phase were more than operational inefficiencies; they were active blockers to growth and profitability.
Over-concentration on branded campaigns meant the brand was capturing only existing demand. With over 70% of the budget tied up here, new customer acquisition was minimal, and monthly revenue remained capped around ₹4L despite consistent ad spend.
Low non-branded keyword share (<20%) limited visibility to shoppers not already aware of the brand, directly impacting market expansion and category share growth.
Wasted spend during off-peak hours (12AM–8AM) drained ₹20K–₹30K/month from the budget. With no performance control during these time slots, the high ACoS (54%) from these hours pulled down overall efficiency.
Delayed optimizations caused by manual workflows led to prolonged exposure to underperforming targets. Keywords with high ACoS and low conversion rates often remained active for weeks, wasting up to ₹50K monthly.
Poor upper-funnel engagement due to outdated creatives caused CTRs to drop below 0.25%. This weakened the performance of Sponsored Brands and Display campaigns, driving up CPCs and failing to generate awareness at scale.
Collectively, these challenges restricted the brand’s ability to scale profitably, stalled new customer growth, and made campaign performance highly reactive instead of proactive.
The Strategy
To scale ad revenue while maintaining efficiency, we restructured the entire campaign strategy using a full-funnel approach. Prior to this, the brand was heavily reliant on bottom-funnel, branded campaigns, which mostly captured repeat buyers. As a result, new-to-brand (NTB) customer acquisition had stalled, and organic brand searches remained low.
Competitor brands with broader budgets and category presence were dominating the top-of-funnel with aggressive Sponsored Brands and Display investments, often bidding on non-branded keywords at higher CPCs. With a relatively limited budget, this brand could not afford to compete at those rates using brute force. Without a presence in awareness and discovery layers, the brand risked losing visibility to more aggressive players.
The full-funnel strategy was adopted to:
Increase impression share on generic and category-level terms.
Drive non-branded traffic at a manageable CPC.
Create consistent brand visibility across all stages of the customer journey.
Improve NTB customer growth through structured exposure and retargeting.
1. Full-Funnel Segmentation
We reorganized all campaigns into three distinct funnel stages, each serving a different buyer intent:
Top Funnel (Awareness):
Launched Sponsored Brands Video and keyword campaigns to build top-of-mind awareness.
We created fresh creatives for Sponsored Brands Video ads, which replaced outdated content and immediately improved CTRs by 2X.
Top campaigns -
Mid Funnel (Discovery):
Introduced broad match, phrase match, and ASIN targeting via Sponsored Products to capture potential buyers in the consideration phase.
Added Sponsored Display campaigns focused on competitor conquesting and retargeting PDP visitors using a 30-day lookback window.
Top campaigns -
Bottom Funnel (Conversion):
Consolidated branded and exact match Sponsored Products campaigns to capture high-intent traffic and repeat customers more efficiently.
Optimized bid strategies and allocated budgets based on conversion performance.
Top campaigns -
2. Keyword Targeting Overhaul
Our keyword targeting structure was redefined to offer granular control and better performance insights:
Split campaigns by match type: Broad, Phrase, and Exact keywords were separated to allow precise bidding and budget control.
Separated branded vs non-branded keywords to better measure new customer acquisition vs existing demand capture.
Launched two new Auto campaigns dedicated to keyword harvesting and ASIN ranking opportunities.
Enabled automated keyword harvesting and migration: Converting search terms from Auto/Broad campaigns were automatically added to exact match campaigns with optimized bids.
3. Sponsored Display Tactics
To strengthen mid- and top-funnel visibility, we deployed multiple Sponsored Display strategies:
Retargeting: Targeted users who had visited product detail pages but had not converted, using a 30-day window.
Conquesting: Targeted competitor ASINs and high-performing category placements.
PDP Defense: Defended our own listings by advertising on our top product pages to reduce competitor hijacking.
4. Budget Realignment
The most important structural change was budget redistribution to reflect a full-funnel strategy:
Branded/Exact Match budget reduced from 70% to 30%
Broad/Phrase/ASIN campaigns received 40% of the total budget
Auto campaigns and SB/SD creative campaigns were each allocated 15%
This redistribution helped unlock new impressions, increase brand visibility beyond loyal buyers, and improve campaign reach across the funnel.
5. Automation Strategy
Manual campaign changes had previously created long response times and wasted spend. We addressed this with a rule-based automation system that ran on our internal platform. Key rules included:
Bid adjustment rules: Automated bid increases or decreases based on ACoS thresholds, conversion rates, and impression volume.
Dayparting rules: Paused or reduced bids automatically during 12 AM to 8 AM, a window that had previously caused 54% ACoS and ~₹30K/month in wastage.
Automated keyword harvesting: High-performing search terms were promoted to exact match campaigns automatically.
Automated negation: Low- or non-converting terms were added to negative targeting lists to prevent recurring waste.
The Result:
The Brand experienced a significant turnaround in both performance metrics and operational efficiency within just 7 months of implementing the full-funnel strategy and automation-led optimizations.
ACoS Trend from (Oct 2024 → Apr 2025)
Ad Sales Trend from (Oct 2024 → Apr 2025)
Quantitative Performance Improvements (Sep 2024 → Apr 2025)
Metric | Before (Sep 2024) | After (Apr 2025) | % Change |
ACoS | 36% | 28% | ↓ 22% |
TACoS | 17.84% | 15.80% | ↓ 11.5% |
The brand’s 80% growth in ad sales was driven by stronger funnel coverage, improved creative performance, and more efficient targeting. ACoS dropped 22% due to bid control, dayparting, and negative targeting allowing scale without sacrificing profitability.
Operational Efficiency Gains
Optimization cycles reduced from 10 days to 24 hours: With automation handling bid changes, keyword harvesting, and negation, the team cut decision lag and improved reaction speed across the board.
Manual effort significantly reduced: Rule-based automations ensured consistent actions across campaigns, allowing the brand to scale without increasing team bandwidth.
₹30K/month saved through dayparting: Automated pause/reduce rules during low-conversion hours (12 AM–8 AM) eliminated 20% of daily spend waste, directly improving ACoS.
Customer & ASIN-Level Growth
New customer acquisition grew by ~45%: Expansion into non-branded keywords and ASIN targeting exposed the brand to new audiences.
Repeat orders increased by ~18%: Sponsored Display retargeting and improved PDP defense kept the brand top-of-mind and retained high-value shoppers.
ASIN visibility surged: The brand’s top 3 SKUs improved their average search rank from 4.6 to 2.1, resulting in a 60% increase in impressions.
Category leadership goal achieved early: Market share for top varicose vein keywords increased from 12% to 20% in just 7 months—achieving the 12-month objective ahead of schedule (Source: SQP Report).
Scalability & Replication
The strategy deployed was intentionally designed to be scalable, automation-driven, and adaptable across brands and verticals. What made it effective was not just tactical success, but the structured, repeatable logic behind each action.
During the optimization phase, several inefficiencies surfaced that were not exclusive. Issues like off-peak spend wastage, delayed bid adjustments, and manual keyword pruning were common across other accounts too. Recognizing this, we extended our internal capabilities to build a more permanent, scalable solution.
Using performance patterns observed in the account, we built automation logic on top of Amazon Ads APIs. This allowed us to implement bid adjustments, dayparting, keyword harvesting, and negation at scale. What started as a brand-specific intervention has now evolved into a rules-based automation engine used across more than 40 accounts.
While building these capabilities, we faced challenges like ensuring data accuracy across hourly windows, managing API rate limits, and maintaining logic flexibility across accounts of different sizes. Balancing customization with ease of deployment required close coordination between strategy and tech teams.
We also evaluated DSP but decided not to use it in this case. The brand had a limited monthly media budget and DSP would have required a higher upfront commitment. Moreover, the audience was already actively searching on Amazon, so full-funnel reach could be effectively achieved using Sponsored Brands Video and Sponsored Display without the need for programmatic spend.
Repeatable Framework The campaign structure, targeting logic, and optimization approach were built around modular full-funnel segmentation, which can be reused across any advertiser:
Top Funnel: Sponsored Brands (video and keyword) for awareness
Mid Funnel: Sponsored Products (broad, phrase, ASIN) and Sponsored Display for discovery
Bottom Funnel: Sponsored Products (exact and branded) for conversion
Automation-First Execution Our automation system enables:
Daily bid adjustments based on ACoS tiers and time-of-day performance
Auto-pausing of underperforming keywords
Automated keyword harvesting and migration
Scheduled dayparting based on performance dips
Since these rules are data-driven and threshold-based, they can be ported to any advertiser account with only minor customization. This ensures efficiency regardless of vertical or account size.
Sustainable Scaling This approach allows:
Faster onboarding of new accounts with pre-built rule templates
Consistent performance monitoring through a unified dashboard view
Reduced dependency on manual workflows, freeing up strategy teams for higher-level planning
The case served as the proving ground. It demonstrated how combining structured funnel segmentation with intelligent automation creates a scalable growth engine. Today, this framework is a core part of how we approach all performance-focused advertisers.
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