Select options and click 'View Performance' to see KPIs.
Overall Campaign Performance Analysis
This report analyzes the performance of a digital advertising campaign for a UK e-commerce brand specializing in clothing, beauty, and homeware products. The primary objective of this campaign is to drive as many sales as possible on their website in the most cost-efficient way, focusing heavily on Cost Per Acquisition (CPA). The data presented covers sample domains, devices, cities, and other dimensions over the last 14 days, providing insights to optimize future media buying—either by increasing spend on high-performing elements or by excluding underperforming ones.
The overall campaign performance, across all devices and media spend, demonstrates a significant focus on driving total conversions, with 309 conversions achieved for a total media spend of £12,579.77. The average Cost Per Acquisition (CPA) stands at £40.71, indicating the average cost to secure a desired action. A lower CPA is generally better for an e-commerce brand focused on sales, as it means more conversions for the same budget.
Examining the data with a filter for media spend above £0.50 highlights an interesting dynamic. While the total media spend decreases to £6,742.08, and total conversions drop to 181, the overall CPA actually improves to £37.25. This suggests that a considerable portion of the initial budget was allocated to lower-value, highly fragmented placements that, while contributing to the overall conversion count, were less efficient on a per-conversion basis. This aligns directly with the goal of driving sales in a cost-efficient manner, indicating that filtering out low-value spend improves overall efficiency.
Significant Budget Fragmentation: Approximately £5,837.69 (about 46.4%) of the total media budget was allocated to placements that individually received £0.50 or less.
Impact on Conversions: This fragmented budget contributed 128 conversions, showing that even micro-spends can collectively drive results, albeit at a higher average cost per conversion.
Efficiency vs. Reach Trade-off: The unfiltered data provides a broad reach, while the filtered data emphasizes efficiency. The improvement in CPA from £40.71 to £37.25 when excluding low-spend placements points to a clear opportunity for optimizing budget allocation.
Device Performance Nuances: While Smart Phone drives the highest volume of conversions, Desktop offers a significantly lower CPA, suggesting high-value intent. Tablets show higher CPA and lower conversion volume, indicating less efficiency.
Time of Day Optimization: Identifying peak conversion hours (e.g., 9 AM, 10 AM, 19 PM, 23 PM) and inefficient periods (e.g., 6 AM, 17 PM) is crucial for bid adjustments and budget scheduling.
Site/App Level Granularity: The detailed App/URL and Device Type analysis reveals specific "Conversion Powerhouses" and "Strategic Reach & Volume Drivers" that are highly efficient and scalable, alongside "Potential Waste" areas that require immediate attention.
Performance by Device Type
This section provides a summary of campaign performance across different device types.
Device Performance Chart
Device Type
CPC
CTR
CPM
CPA
Total Conversions
Media Spend
Desktop
£9.74
0.05%
£4.55
£21.10
78
£1,645.57
Smart Phone
£3.44
0.10%
£3.43
£48.87
214
£10,459.22
Tablet
£4.85
0.09%
£4.48
£27.94
17
£474.98
Grand Total
£3.81
0.09%
£3.58
£40.71
309
£12,579.77
Device Performance Analysis
Performing Well:
Desktop: While Desktop has a higher CPC (£9.74), it boasts the lowest CPA (£21.10), indicating highly valuable conversions. It accounts for a significant portion of conversions (78) at a lower cost per acquisition than smartphones.
Smart Phone: Exhibits the lowest CPC (£3.44) and highest CTR (0.10%), leading to the highest volume of conversions (214). This makes it crucial for overall conversion volume and efficient traffic generation.
Not Performing Well:
Smart Phone: Despite its high conversion volume, its CPA (£48.87) is considerably higher than Desktop (£21.10) and Tablet (£27.94), implying that while it drives many conversions, they are more expensive individually. We need to analyze this high CPA on mobile devices further to understand the root cause (e.g., lower intent users, poor mobile site experience, specific low-performing mobile placements). Our goal will be to reduce bids on mobile strategically to find the best balance between conversion volume and cost efficiency.
Tablet: While its CPA (£27.94) is better than Smart Phone's, it shows the lowest conversion volume (17), suggesting lower impact or inefficient targeting. Further investigation into the tablet user experience or targeting may be beneficial, or consider reallocating budget.
Analysis for Media Spend > £0.50:
When filtering for media spend above £0.50, the CPA for Smart Phone improves from £48.87 to £44.85, indicating that a portion of the high CPA in the unfiltered data comes from low-value, fragmented mobile placements.
Desktop's CPA also improves from £21.10 to £17.36, and Tablet's from £27.94 to £20.72. This consistency across devices suggests that low-spend placements, regardless of device, tend to be less efficient.
This highlights significant budget fragmentation, particularly on mobile, where numerous low-value placements contribute to overall spend without efficient conversion. Optimizing bids or excluding these highly fragmented, low-performing placements could significantly improve overall campaign efficiency.
Performance by City
This section identifies geographical areas where the campaign resonates most effectively and areas that might require different strategies or reduced spending. Cities with '#DIV/0!' for CPA indicate zero conversions.
Overall City Performance Summary
Across the campaign, 72 cities recorded at least one conversion, accounting for a total media spend of £1137.67. This represents 9.04% of the total campaign media spend. Conversely, 42 cities reported zero conversions despite having a media spend of £1 or more, resulting in a total wasted budget of £4301.85. This amounts to 34.20% of the total campaign media spend being ineffective in these areas.
Note on "Unknown" City Data: A significant portion of both converting (£771.29 on Smart Phone, £148.42 on Desktop) and non-converting (£2178.98 on Smart Phone, £358.43 on Desktop) media spend is attributed to "Unknown" cities. This lack of geographical granularity makes precise geo-targeting and optimization a considerable challenge and is a critical area for data improvement.
City Performance Analysis
Top 5 Performing Cities (by CPA, then conversions) with at least one conversion:
Smart Phone: Great Yarmouth: CPA £1.68 (0.09 conversion, £0.09 spend)
Worst 5 Performing Cities (by no conversions and highest media spend > £1):
Smart Phone: Unknown: £2178.98 spend, 0 conversions - Extremely significant wasted spend. This requires immediate investigation into why such a large budget yields no conversions, and potentially a re-evaluation of its classification or targeting.
Smart Phone: Unknown: 8.21 conversions, CPA £2.94, Media Spend £771.29 - Highest volume. This indicates a highly effective but unclassified segment of the audience that needs to be identified for better targeting and scaling.
This table pinpoints optimal time blocks for ad delivery, ensuring budget is spent when the audience is most receptive and likely to convert. Hours are in 24-hour format.
Note on Attribution Model: The attribution model used to assign conversions to a specific time of day is not known. It's important to understand that the "Time of Day" shown here likely represents the time the conversion occurred, rather than the time the ad was exposed to the user. For instance, a user might see an ad at 10 AM, click it, and convert later at 7 PM. Without a defined attribution model, it's challenging to precisely correlate ad exposure time with conversion time, which could impact real-time bid adjustments for specific hours.
Time of Day
CPC
CTR
CPM
CPA
Total Conversions
Media Spend
0
#DIV/0!
0.00%
£6.05
#DIV/0!
0
£0.11
6
£3.22
0.10%
£3.19
£108.08
5
£540.40
7
£3.45
0.10%
£3.43
£54.06
15
£810.93
8
£3.89
0.09%
£3.62
£45.55
18
£819.97
9
£4.32
0.09%
£3.74
£25.44
28
£712.34
10
£4.02
0.10%
£3.89
£27.12
23
£623.80
11
£4.62
0.09%
£4.02
£27.30
21
£573.26
12
£4.74
0.09%
£4.04
£35.25
18
£634.58
13
£4.50
0.09%
£4.00
£37.78
18
£680.04
14
£3.80
0.11%
£4.04
£49.41
14
£691.75
15
£4.48
0.09%
£4.05
£57.52
13
£747.78
16
£4.01
0.10%
£4.04
£53.94
16
£863.04
17
£4.26
0.09%
£3.98
£67.48
14
£944.77
18
£3.82
0.10%
£3.83
£59.46
16
£951.33
19
£3.65
0.10%
£3.72
£32.97
30
£988.99
20
£4.01
0.08%
£3.18
£35.14
22
£773.17
21
£2.88
0.09%
£2.62
£29.31
21
£615.51
22
£2.56
0.10%
£2.36
£47.78
9
£430.04
23
£2.20
0.10%
£2.25
£22.25
8
£177.96
Grand Total
£3.81
0.09%
£3.58
£40.71
309
£12,579.77
Time of Day Performance Chart
Time of Day Performance Analysis
Peak Conversion Periods: We see particularly strong conversion efficiency and volume around 9 AM (CPA £25.44, 28 conversions) and 7 PM (CPA £32.97, 30 conversions). This pattern suggests that your customers are likely converting either after their morning routine (commute, breakfast, settling into work) or after their workday has concluded and they have some free time in the evening.
Optimizing for Customer Routine: Based on these insights, we recommend implementing a granular day-parting strategy. We can increase bids before and on these time windows, such as morning and afternoon commuting hours, to capture more valuable traffic when your customers are most likely to convert. Conversely, consider reducing bids during less efficient hours, especially during mid-day lulls or very early morning/late night periods where CPA is higher or conversions are minimal, to optimize overall budget efficiency.
Addressing Attribution Model Limitations: As noted, the current attribution model for "Time of Day" data is not known. It's crucial for future analysis to understand if these times reflect ad exposure or conversion completion. If possible, implementing a more sophisticated attribution model that links ad views/clicks to eventual conversions will allow for even more precise day-parting and budget allocation.
Performance by App/URL and Device Type
This section identifies specific App/URL and Device Type combinations where our ads are most effective at driving conversions or engagement, allowing for precise strategic budget reallocation and whitelist creation. This analysis is based on App/URLs where the total media spend for the App/URL (summing across all device types) is above £10.
App/URL Performance Summary
There are 55 App/URLs with at least one conversion, contributing £9942.64 in media spend. The average KPIs for these converting App/URLs are: CPC £8.49, CTR 0.10%, CPM £4.86, CPA £35.50.
For App/URLs with zero conversions and media spend above £10, there are 54 entries, totaling £1560.63 in wasted media spend. The average KPIs for these non-converting App/URLs are: CPC N/A, CTR 0.08%, CPM £4.71, CPA N/A.
App/URL Media Spend: Converting vs. Non-Converting
App/URL and Device Type Performance Analysis
Top 10 Performing Combinations (by CPA, then conversions) with Total Conversions > 1:
standard.co.uk: CPA £6.99 (2 conversions) - Very efficient for multiple conversions.
uk.yahoo.com: CPA £8.30 (2 conversions) - Good efficiency for multiple conversions.
metoffice.gov.uk: CPA £8.95 (3 conversions) - Good CPA and volume.
outlook.live.com: CPA £12.73 (16 conversions) - Excellent balance of volume and efficiency.
ebay.co.uk: CPA £14.72 (16 conversions) - Strong efficiency with good volume.
walesonline.co.uk: CPA £15.94 (4 conversions) - Good and volume.
preloved.co.uk: CPA £18.34 (2 conversions) - Good efficiency with some volume.
mail.aol.com: CPA £18.76 (2 conversions) - Good efficiency with some volume.
Worst 5 Performing Combinations (by no conversions with highest media spend > £10):
toocool2betrue.com: £170.33 spend, 0 conversions - Highest wasted spend among zero-conversion App/URLs. Immediate review and possible bid reduction or exclusion.
eatthis.com: £59.25 spend, 0 conversions - High wasted spend.
High Volume & Efficiency Leaders:
thesun.co.uk: 68 Conversions, CPA £41.89 - Overall top performer by volume.
express.co.uk: 22 Conversions, CPA £54.46 - Second highest volume.
mail.yahoo.com: 19 Conversions, CPA £16.00 - Excellent balance of volume and efficiency.
outlook.live.com: 16 Conversions, CPA £12.73 - Very efficient with good volume.
ebay.co.uk: 16 Conversions, CPA £14.72 - Strong efficiency with good volume.
High-Performing Whitelists
Conversion Powerhouses (App/URL - All Devices)
Logic: We focused on App/URL and Device Type combinations that provide the lowest CPA with recorded conversions. These are where the audience is highly motivated and the campaign is most profitable.
High Engagement & Efficient Traffic (App/URL - All Devices)
Logic: We focused on App/URL and Device Type combinations with very strong CTR and/or low CPC, especially those that drive substantial traffic regardless of immediate conversion counts. These are excellent for reaching an engaged audience, building brand visibility, and driving interested users at a low cost.
Strategic Reach & Volume Drivers (App/URL - All Devices)
Logic: We focused on App/URL and Device Type combinations that provide a good balance of performance (acceptable CPA if applicable, good CTR/CPC) and substantial Total Conversions (>1) or generally higher overall volume, indicating potential for scaling campaign reach effectively. These are reliable platforms to expand our presence.
Potential Waste List
Logic: We focused on identifying App/URL and Device Type combinations that generate clicks (indicated by CTR > 0.00%) but fail to produce any conversions, especially when a significant amount of media spend (over £10 for that specific combination) is allocated to them. These are candidates for further review; consider decreasing bids before outright exclusion to test for improved efficiency.
High Engagement, Zero Conversion Waste List
Logic: This list highlights App/URL combinations that generate a high click-through rate (CTR > 1.00%) and have accrued significant media spend (above £0.50), but have yielded zero conversions. This indicates that while the ads are engaging users and driving traffic, this traffic is not converting. These sites represent a clear opportunity for optimization—either by adjusting bids, refining targeting, or improving the landing page experience—before outright exclusion. This may also look like fraud or click-optimized content that doesn't perform.
Recommendations for Campaign Optimization
Based on our in-depth analysis of the campaign performance data, we present the following strategic recommendations to significantly improve overall CPA and maximize conversions. Our approach focuses on refining budget allocation, leveraging high-performing assets, mitigating waste, and enhancing data granularity for smarter future investments.
Strategic Budget Reallocation: Prioritizing Desktop for Conversion Efficiency
Desktop consistently demonstrates the lowest CPA (£21.10), making it our most cost-efficient conversion driver. We strongly recommend significantly increasing budget allocation to Desktop campaigns to capitalize on this high-value traffic.
While Smart Phone delivers the highest conversion volume, its CPA (£48.87) is considerably higher. We advise a strategic reduction in bids on mobile, particularly for placements contributing to high CPA, to find the optimal balance between volume and cost. Our aim is to achieve better mobile efficiency without sacrificing crucial reach.
Tablets show the lowest conversion volume and a higher CPA than Desktop. We suggest evaluating the value of tablet traffic and considering either reduced bids or pausing campaigns on these devices to reallocate budget to more efficient channels like Desktop.
Aggressive Whitelist and Exclusion List Management: Sculpting Our Ad Placements
Whitelist Application: We strongly recommend immediately applying all "Conversion Powerhouses" (e.g., devonlive.com, cheatsheet.com, standard.co.uk) to our campaigns. Increasing bids on these proven placements will maximize volume from our most efficient sources. We should also closely monitor and consider slight bid increases for "High Engagement & Efficient Traffic" (e.g., MailOnline - iOS, somersetlive.co.uk) as they drive engaged users at a low cost, offering future conversion potential.
Exclusion List (Blacklist) Management: Our priority is the immediate exclusion of "Potential Waste List" and "High Engagement, Zero Conversion Waste List" App/URLs (e.g., toocool2betrue.com, theaa.com). These are consuming significant budget without driving conversions, and removing them will prevent further wasted spend. Furthermore, we must investigate "Unknown" cities in the "Worst 5 Performing Cities" and, if their source or value cannot be improved, we advise excluding them from geo-targeting.
Precision Day Part Optimization: Aligning with Customer Behavior
Given the clear peak conversion hours around 9 AM and 7 PM, we propose implementing a granular day-parting strategy. This means increasing bids before and on these time windows, specifically targeting morning and afternoon commuting hours. This strategy aims to capture high-intent traffic when our customers are most likely to convert after their morning routines or after work.
Conversely, we recommend decreasing bids during less efficient periods, such as mid-day lulls or very early morning/late night hours, where CPA is higher or conversions are minimal, to optimize overall budget efficiency.
To further refine this, we need to review the attribution model for time of day data. Cross-referencing conversion time with ad exposure time, if possible, will allow for even more precise bid adjustments.
Data Enhancement and Advanced Testing: Unlocking Future Growth
Enhancing Geo-Targeting with Customer Match: The substantial spend attributed to "Unknown" cities is a critical data visibility gap. We strongly recommend utilizing customer match data (e.g., email lists from your e-commerce platform). By uploading this data, we can potentially identify the geographical locations of these "Unknown" converters, allowing us to create precise geo-targeting strategies for high-value areas, exclude un-converting segments more effectively, and significantly improve campaign granularity.
Publisher Data Deals & Premium Inventory: For our best-performing App/URLs, we suggest exploring direct deals with specific publishers to gain access to their proprietary data. This could provide deeper insights into audience behavior on those platforms, enabling even more precise targeting and optimization. Additionally, we could explore setting up Programmatic Guaranteed deals or Private Marketplaces (PMPs) with these publishers. This secures premium inventory at potentially better rates and allows for direct data sharing or exclusive targeting features not available on the open exchange.
Conversion Rate (CR) Analysis: Beyond CPA and total conversions, we will conduct a deeper analysis of Conversion Rate (CR) across different segments (device, time of day, App/URL). A high CR indicates highly relevant traffic and an effective landing page experience, offering a more holistic view of performance.
A/B Testing for Continuous Improvement: We recommend setting up robust A/B tests for various creatives and device targeting strategies. This will allow us to rigorously test hypotheses about which ad variations and device-specific approaches consistently yield the best CPA and highest conversion volume, driving continuous improvement.
Holistic Attribution Modeling: To gain a comprehensive understanding of campaign effectiveness, we must move beyond last-click attribution where feasible. We recommend exploring multi-touch attribution models (e.g., U-shaped, W-shaped, data-driven) to understand how different touchpoints contribute throughout the customer journey. Furthermore, for a broader strategic view, we can consider integrating this data into a Marketing Mix Model (MMM). This will help quantify the incremental impact of programmatic advertising alongside other marketing channels and external factors, optimizing overall marketing spend for the e-commerce brand.