- 192% Increase in Sales in 6 months
- Reduced CPC's from 26.96 to 20
- ACoS reduced from 29.39% to 26.90%
- Increased CVR by +8.1%
- Monthly Click Volume increased by 294%
TL;DR:
Challenges:
BonKaso was stuck in a high-cost growth loop – most ad spend was locked into saturated exact-match keywords, expensive Top-of-Search placements, and poorly aligned competitor targeting. This inflated CPC, suppressed conversion rates, and limited profitable scale.
What We Did:
We rebuilt the entire ad architecture around intent, efficiency, and scale – shifting spend from saturated exact terms to long-tail discovery, redistributing budgets to higher-efficiency placements, cleaning competitor targeting using price + intent alignment, and fixing front-end conversion leaks.
Outcome:
This systematic restructuring unlocked profitable scale: sales jumped from ₹82L to ₹2.40Cr (+192%), while CPC dropped 26%, ACoS reduced to 26.9%, conversion rate improved by 8.1%, and monthly traffic nearly quadrupled – without increasing risk or waste.
Category: Home & Kitchen
Sub-Category: Ladders, Bucket Mops, Safe Lockers
Timeline: April → December 2025
Marketplace: Amazon India
Brand: BonKaso
About BonKaso:
BonKaso sells Ladders and cleaning tools products, including shower hoses and bucket mops. The brand was scaling spend aggressively, but CPC inflation and inefficient campaign structure were limiting profitability and sustainable growth.
Adsify rebuilt the ad account structure to scale efficiently.
Challenges & Fix:
Approach & Execution
- 72% of Spend Concentrated in High-CPC Exact Keywords:
- Out of total monthly ad spend (~₹24L):
- ₹17.3L (72%) was on exact match keywords
• Average CPC on exact match keywords: ₹38.42
• Conversion rate on these keywords: 4.2%
• ACoS: 31.8%
These keywords were saturated, including:
- Ladders
- Ladder for home
- Mop
- ₹17.3L (72%) was on exact match keywords
(Metrics) – Before
(Metrics) – After
These keywords had heavy competition from established brands with larger budgets.
This structure limited scale and inflated CPC.
We introduced modifier-style and structured broad match campaigns targeting intent-rich long-tail queries like:
- ladders for home 7 steps
- steel ladders for home
- cleaning mop with bucket
Within 60 days:
Outcome:
- Long-tail keywords scaled to 41% of total spend
- Reduced blended CPC from ₹26.96 → ₹20
- Contributed 34% of total ad sales
This reduced dependency on inflated exact keywords while increasing scale.
2. 63% of Spend Concentrated in Top of the Search Placements
- Placement-level analysis showed:
Placement
Spend Share
CPC
CVR
ACoS
Top of Search
63%
₹31.20
5.8%
30.9%
Rest of Search
22%
₹18.40
4.9%
25.1%
Product Pages
15%
₹16.10
4.4%
24.3%
Top of Search was consuming most of the budget despite being least efficient.
Fix: Redistributed Budget Accross Placementsin Top of the Search Placements
- We reduced bid multipliers and shifted spend toward lower CPC placements.
After restructuring:
Outcome:
- ₹3.4L/month saved in excess CPC spend
- Product Pages became the highest efficiency placement
- Overall blended CPC reduced by ₹6.96
3. Competitor Targeting Included Irrelevant and Premium ASINs
Competitor targeting analysis showed:
Out of 412 targeted competitor ASINs:
- 38% were priced 25%+ higher than Bonkaso products
- 21% had unrelated utility intent
- Conversion rate on mismatched ASIN targeting: 2.9%
- ACoS: 41.3%
Example:
Bonkaso 6 step ladder: ₹3099
Competitor targeted: ₹4200 premium ladderLow price alignment reduced conversion probability.
Fix: Rebuilt Competitor Targeting Based on Price and Intent Alignment
We filtered and rebuilt competitor targeting based on:
- Price within ±15% range
- Same product type and use case
- Rating between 3.8–4.5 stars
- Direct category relevance
Removed 173 inefficient ASIN targets.After restructuring:
Metric
Before
After
Conversion Rate
2.9%
4.6%
CPC
₹24.80
₹18.90
ACoS
41.3%
27.2%
Competitor targeting contribution to sales increased from: ₹6.2L/month → ₹21.4L/month
4. No Intent Segmentation – Same Campaign Targeted All Buyer Types
Before restructuring, campaign structure mixed all intents:
Example campaign contained:
High intent keywords:
- steel ladders for home
- ladders 7 steps foldable
Low intent keywords:
- ladder
- ladder for home
- foldable ladder
These had very different conversion behaviours:
Intent Type
CPC
CVR
ACoS
High Intent
₹29.10
7.4%
22.3%
Low Intent
₹21.60
2.8%
46.2%
Because bids were same, low-intent queries consumed excess spend.
Low intent queries consumed 31% of total spend but generated only 12% of sales.
Fix: Segmented Campaigns by Purchase Intent
High intent keywords → aggressive bids
- Low intent keywords → reduced bids or discovery-only budgets
- High Intent Campaigns.
Keywords containing:
– Aluminium
– Stainless Steel
– Step Count
– Rust Proof - Low Intent Campaigns
Keywords containing: - best for home
- how to install
- comparison
Bid strategy:
- High intent keywords → aggressive bids
• Low intent keywords → reduced bids or discovery-only budgets
After restructuring:
Metric | Before | After |
High intent spend share | 46% | 68% |
High intent CVR | 7.4% | 8.2% |
ACoS | 29.39% | 26.90% |
Result: Shifted budget toward higher converting traffic without increasing CPC.
5. Front-End Conversion Leaks Reduced Ad Efficiency
Conversion analysis showed performance drops correlated with:
- Listing quality (images, bullets, A+ content)
- Delivery delay
- Buy Box availability
Example SKU:
Conversion rate dropped from 5.6% → 4.3% during delivery delay period.
Ads continued spending, increasing ACoS.
Fix: Listing and Conversion Optimisation:
We improved listing clarity by adding:
- Usage-focused product visuals
- Rust-proof benefit communication
- Installation clarity visuals
After front-end improvements:
Metric | Before | After |
Conversion Rate | 4.90% | 5.30% |
Revenue per click | ₹55.4 | ₹63.7 |
This improved ad efficiency without increasing CPC.
Impact:
