Signalytics faced a common challenge, high Total Advertising Cost of Sales (TACoS) eating into profit margins. Despite strong sales, ad spending continued to climb without delivering proportional returns. The team needed a clear, data-driven strategy to bring costs under control while maintaining growth.
By refining ad placement, optimizing bids, and aligning campaigns with organic performance, Signalytics cut TACoS from 38% to 18% in just 30 days. This rapid improvement came from a focused approach that balanced automation with hands-on analysis, proving that small, consistent adjustments can create measurable impact.
This case study breaks down how Signalytics identified key inefficiencies, executed targeted changes, and tracked results to ensure lasting improvement. It offers practical insights into what worked, what didn’t, and how similar strategies can help other brands achieve more efficient advertising performance.
Background and Objectives
Signalytics aimed to improve advertising efficiency by identifying cost drivers and optimizing campaign performance. The company focused on reducing wasted ad spend, improving conversion tracking, and aligning marketing actions with measurable business outcomes.
About Signalytics
Signalytics is a data-driven marketing agency that supports e-commerce brands in improving their Amazon advertising performance. It uses analytics and automation tools to help sellers understand their ad metrics and make informed decisions.
The team specializes in optimizing TACoS (Total Advertising Cost of Sales), a key metric that connects ad spend with total revenue. By analyzing campaign data, Signalytics identifies patterns that affect product visibility, conversion rates, and long-term profitability.
Its approach combines machine learning models, keyword analysis, and real-time reporting dashboards. This allows clients to adjust bids, pause low-performing ads, and reallocate budgets quickly. The company’s focus on measurable results and transparent reporting has made it a trusted partner for sellers seeking sustainable growth.
Initial Challenges with TACoS
Before the optimization effort, Signalytics faced a TACoS rate of 38%, which indicated a high dependency on paid ads to drive sales. This level suggested that organic visibility and repeat purchases were not yet strong enough to balance ad costs.
The issue stemmed from several factors:
- Broad targeting that wasted spend on irrelevant keywords.
- Limited tracking of organic sales impact.
- Inconsistent bid adjustments across campaigns.
These challenges caused inefficiencies in both budget allocation and product ranking. The marketing team realized that short-term ad gains were not translating into long-term growth. They needed a structured plan to improve ad efficiency while maintaining sales volume.
Goals for the 30-Day Reduction
Signalytics set clear objectives to lower TACoS from 38% to 18% within 30 days. The main goal was to cut unnecessary ad spend without reducing total revenue.
Key goals included:
- Increase organic sales through improved listing optimization.
- Refine ad targeting using performance-based keyword segmentation.
- Enhance reporting accuracy to measure true ad impact.
The team also aimed to strengthen decision-making through daily performance reviews and automated alerts. By combining data insights with disciplined campaign management, Signalytics sought to create a repeatable model for efficient ad performance.
Strategic Approach to Lowering TACoS
Signalytics used a structured optimization process focused on precise data analysis, ad refinement, and budget control. Their approach improved ad efficiency, reduced wasted spend, and increased the share of organic sales relative to total revenue.
Data-Driven Campaign Analysis
Signalytics began by collecting detailed performance data from Amazon’s advertising dashboard. They tracked ACoS, TACoS, ROAS, and conversion rates across campaigns to identify underperforming products and keywords.
They used this data to separate campaigns into three groups:
- High-performing – strong sales and low ACoS.
- Moderate – stable but with room for optimization.
- Low-performing – high spend with limited return.
By comparing these groups, the team pinpointed wasted ad spend and opportunities for reallocation. They also reviewed keyword match types, search term reports, and placement data to understand which ads drove profitable growth.
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This analysis provided a clear baseline that guided every adjustment made during the 30-day optimization period.
Targeted Advertising Adjustments
After analyzing campaign data, Signalytics refined targeting to focus on high-converting keywords and audiences. They paused low-performing keywords and shifted bids toward search terms that consistently generated sales.
They also tested exact match and phrase match keywords to improve precision and reduce irrelevant clicks. Ad copy and product listings were updated to align with top-performing search terms, ensuring better ad relevance and click-through rates.
To maintain momentum, Signalytics monitored daily performance metrics and made small bid adjustments based on conversion trends. This steady optimization helped sustain lower costs while maintaining visibility for profitable listings.
The result was a measurable drop in TACoS as ads reached more qualified shoppers at a reduced cost per conversion.
Budget Reallocation Strategy
The team restructured the ad budget to prioritize campaigns with the highest return potential. Instead of spreading spend evenly, they allocated more funds to proven performers and reduced investment in low-yield campaigns.
A simple breakdown guided decisions:
| Campaign Type | Budget Allocation | Purpose |
|---|---|---|
| Branded Search | 40% | Maintain visibility and capture loyal buyers |
| Category/Competitor | 35% | Expand reach and attract new customers |
| Product Targeting | 25% | Focus on high-margin ASINs |
This reallocation ensured each dollar worked toward measurable growth. Signalytics reviewed results weekly and shifted funds as performance data evolved, maintaining flexibility while driving consistent TACoS improvement.
Implementation Process
Signalytics used a structured optimization plan that combined data-driven decisions, automation tools, and coordinated team effort. Each phase focused on measurable improvements in ad efficiency, targeting precision, and cost control.
Step-by-Step Optimization Timeline
The process began with a baseline audit of all active campaigns. The team identified high-spend keywords with low conversion rates and paused underperforming ads. This reduced wasted spend within the first few days.
Next, they applied placement bid adjustments, focusing on top-performing product pages and search placements. Daily monitoring allowed quick responses to changes in click-through and conversion rates.
By week two, keyword segmentation separated branded, competitor, and generic terms. Each group received custom bids based on ROI. Negative keywords were added to prevent irrelevant clicks.
In the final phase, Signalytics tested automated bidding rules to maintain lower TACoS while sustaining sales volume. Continuous reporting confirmed a steady drop from 38% to 18% over 30 days.
| Week | Focus Area | Key Actions | Outcome |
|---|---|---|---|
| 1 | Audit & Cleanup | Pause poor performers | Reduced wasted spend |
| 2 | Bid Optimization | Adjust placements | Improved efficiency |
| 3 | Keyword Strategy | Segment & refine | Higher conversion rates |
| 4 | Automation | Implement rules | Stable low TACoS |
Key Tools and Technologies Used
Signalytics relied on a mix of Amazon Advertising Console, Helium 10, and internal analytics dashboards. These platforms helped track metrics like ACoS, TACoS, and conversion rates in real time.
Helium 10 provided keyword insights and search volume trends. The team used this data to refine targeting and remove low-value terms.
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Custom dashboards built in Google Data Studio connected ad data with sales performance. This integration allowed faster decisions and reduced manual reporting time.
Automation tools managed bid adjustments and budget pacing. Scheduled scripts ensured that campaigns stayed within target TACoS thresholds without constant manual oversight.
Team Collaboration and Roles
The optimization involved a small, specialized team. A campaign strategist led data review and performance planning. A PPC analyst executed bid changes and tracked daily metrics.
The creative specialist adjusted product listings and ad copy to improve click-through rates. A data engineer maintained reporting accuracy and automated data pulls.
Weekly meetings aligned all members on current metrics and next steps. Each role contributed to consistent communication and rapid iteration.
This structure allowed Signalytics to identify issues quickly, implement fixes efficiently, and maintain accountability across all campaign stages.
Results and Key Metrics
Signalytics achieved measurable improvements in advertising efficiency, cost control, and sales performance through structured campaign optimization. Data-driven adjustments to bids, keyword targeting, and placement strategies directly influenced both advertising spend and revenue outcomes.
TACoS Reduction Outcomes
Signalytics lowered its Total Advertising Cost of Sales (TACoS) from 38% to 18% within 30 days. This change reflected a stronger balance between paid and organic sales.
They achieved this by refining campaign structures, reallocating budget toward high-performing keywords, and reducing bids on low-return placements. These actions improved efficiency without cutting visibility.
| Metric | Before | After | Change |
|---|---|---|---|
| TACoS | 38% | 18% | ↓ 20 pts |
| Ad Spend | $42,000 | $31,000 | ↓ 26% |
| Organic Sales Share | 45% | 63% | ↑ 18 pts |
The lower TACoS indicated that organic growth began to offset ad spend, showing a sustainable improvement in profitability.
Impact on Sales and ROI
Sales volume rose as ad efficiency improved. Paid sales increased slightly, but the larger gain came from organic conversions that followed higher product visibility.
Return on investment (ROI) rose from 2.4x to 4.1x, driven by lower costs per sale and higher average order values. Signalytics measured this through consistent reporting across campaigns, ensuring that performance gains were not inflated by short-term promotions.
Key factors included:
- Improved click-through rates (CTR): +12%
- Lower cost-per-click (CPC): –18%
- Higher conversion rate: +9%
These combined metrics confirmed that the campaign changes created lasting efficiency rather than temporary spikes.
Performance Benchmarks
Compared to industry averages, Signalytics’ results exceeded typical Amazon PPC performance for similar categories. Many brands maintain TACoS between 20–30%, while optimized accounts often target 15–20%.
By reaching 18%, Signalytics positioned itself at the high-efficiency end of that range. The team’s focus on long-term TACoS stability, rather than short-term ACoS improvement, helped maintain balanced growth.
| Benchmark Area | Industry Avg | Signalytics Result |
|---|---|---|
| TACoS | 25% | 18% |
| ACoS | 22% | 15% |
| ROI | 3.0x | 4.1x |
These benchmarks showed that Signalytics’ approach aligned with best practices for sustainable Amazon advertising performance.
Lessons Learned and Best Practices
Signalytics achieved a sharp drop in TACoS by improving campaign structure, refining keyword focus, and aligning ad budgets with high-performing products. The results came from disciplined testing, continuous optimization, and data-driven decision-making rather than one-time changes.
Critical Success Factors
The team’s success relied on precise campaign segmentation. They separated branded, competitor, and category keywords to control bids and measure performance clearly. This structure allowed them to identify which segments drove the best return.
Inventory forecasting also played a key role. By ensuring products stayed in stock during high-traffic periods, they protected organic ranking gains and prevented wasted ad spend.
They used data monitoring tools to track daily TACoS and conversion rates. These insights guided real-time adjustments, such as shifting budgets toward keywords with strong click-through and conversion performance.
Finally, collaboration between marketing and operations ensured that advertising, pricing, and inventory strategies aligned. This cross-functional approach reduced delays and improved response times to market changes.
Challenges Overcome
Signalytics faced early issues with budget waste on underperforming keywords. Many campaigns had overlapping targets, which diluted performance data and inflated costs. They resolved this by consolidating duplicate campaigns and tightening match types.
Another challenge involved balancing automation and manual control. Automated bidding helped speed up testing but sometimes overbid on low-value clicks. The team introduced hybrid management—using automation for data collection but manual adjustments for strategic keywords.
They also managed inventory shortages that threatened ranking stability. By improving forecast accuracy and adjusting ad spend during low-stock periods, they avoided running ads on unavailable items.
Finally, data overload made it difficult to identify actionable insights. They simplified reporting dashboards to focus on a few core metrics: TACoS, conversion rate, and organic sales growth.
Actionable Takeaways
- Segment campaigns by intent and product type to gain clear visibility into performance.
- Monitor TACoS daily and adjust bids based on conversion efficiency.
- Forecast inventory to prevent ad waste and protect rankings.
- Use automation selectively, combining it with manual oversight for precision.
- Align teams across advertising, operations, and inventory to ensure consistent goals.
| Focus Area | Key Practice | Result |
|---|---|---|
| Campaign Structure | Segmentation by keyword type | Clearer performance tracking |
| Budget Allocation | Shift to top-performing ads | Lower TACoS |
| Data Use | Daily metric monitoring | Faster optimization decisions |
Frequently Asked Questions
Reducing Total Advertising Cost of Sale (TACoS) depends on balancing ad spend with organic growth, improving conversion rates, and refining keyword strategies. Each factor contributes to better long-term performance and more efficient use of marketing budgets.
What strategies can effectively reduce Total Advertising Cost of Sale (TACoS)?
Brands can lower TACoS by improving product listings, optimizing ad targeting, and increasing organic visibility. They can also focus on campaigns that drive repeat purchases and customer loyalty. Monitoring performance data and adjusting bids or budgets regularly helps maintain efficiency.
Can you explain the difference between ACoS and TACoS in advertising metrics?
ACoS (Advertising Cost of Sale) measures ad spend compared to revenue from ads only.
TACoS (Total Advertising Cost of Sale) includes both ad-driven and organic revenue.
A lower TACoS shows that advertising supports overall business growth, not just paid sales.
What are the key factors influencing TACoS in online advertising?
TACoS depends on product pricing, conversion rates, organic ranking, and ad efficiency. Seasonal demand, competition, and listing quality also affect results. Tracking these factors helps identify where to adjust for better performance.
How do changes in advertising spend impact TACoS over time?
When ad spend increases without improving conversions, TACoS rises. As ads help boost organic sales, TACoS usually falls. Over time, stable or lower TACoS suggests stronger brand presence and more cost-effective marketing.
What role does keyword optimization play in lowering TACoS?
Keyword optimization helps reach the right audience and reduce wasted spend. Using high-performing, relevant keywords improves click-through and conversion rates. Regularly reviewing search term reports ensures ads target profitable terms and avoid low-value traffic.
What are some common mistakes to avoid when trying to improve TACoS?
Many advertisers rely too heavily on paid ads and neglect organic growth. Ignoring listing quality, poor keyword selection, and failing to test new strategies can raise TACoS. Consistent analysis and small, data-driven changes prevent unnecessary costs and improve results.