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Bidnamic

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"Boosts revenue by optimizing Google Shopping"

Case Study
Frequently Asked Questions
Bidnamic employs advanced machine learning and human expertise to maximize retailers' return on investment by optimizing the performance of Google Shopping adverts.
BlurbSTAR Case Study
LD Mountain Centre & Bidnamic
Boosted slow-moving inventory with data-driven Google Shopping strategy.
36%
Decrease in overall campaign costs
62%
ROAS improvement for underperforming items
1.
Situation
Struggles with Underperforming Inventory
→
Slow-moving products lacked visibility and traction.
→
Performance Max heavily favored bestselling items.
→
New strategy needed without inflating costs.
LD Mountain Centre was facing significant challenges with their inventory management, particularly in getting traction for slow-moving products. The primary issue was that their Performance Max campaigns were disproportionately favoring bestselling products, which left a substantial portion of their inventory underperforming. This not only led to a lack of visibility for these items but also meant potential revenues were being left on the table. The task was to devise a strategy that could uplift these overlooked products without disrupting the performance of the top sellers or incurring additional costs, while continuing to run their current Performance Max management.
2.
Task
Enhance Visibility for Hidden Items
→
Develop campaign for slow-moving items.
→
Work alongside existing Performance Max setup.
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Implement strategic bidding and optimization.
The primary task set for the collaboration between LD Mountain Centre and Bidnamic was to develop and implement a campaign that would increase the visibility and performance of the slow-moving products. This required a solution that could work alongside the existing Performance Max management structure without causing any disruptions. The aim was to utilize strategic bidding and optimization techniques to breathe new life into these overlooked items, ensuring they contributed positively to the total catalog's performance. This called for an innovative approach that provided attention to SKU-specific bidding based on real-time purchase intent.
3.
Action
Launch Targeted Incremental Campaigns
→
Applied machine learning-driven Google Shopping campaigns.
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Utilized SKU-level bidding for precision.
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Enhanced visibility for underperforming items.
Bidnamic implemented machine learning-driven Google Shopping campaigns specifically aimed at uplifting the slow-moving inventory. By employing SKU-level bidding based on purchase intent, Bidnamic was able to fine-tune the visibility strategies for these underperforming items. This precision targeting ensured that 'no-click' products, previously neglected by Performance Max campaigns, were brought to the forefront. The campaigns were structured to optimize every aspect of visibility and awareness, enabling a more robust performance from the entire product catalogue. This was done all while maintaining the efficiency of the existing top-performing items.
4.
Result
Recorded Substantial Performance Gains
→
62% increase in ROAS.
→
36% reduction in campaign costs.
→
Conversion rates up by 29%.
As a direct result of the initiatives undertaken by Bidnamic, LD Mountain Centre experienced significant improvements across various performance metrics. Comparing April to August 2024 with the same period in 2023, there was a remarkable 62% increase in ROAS for previously underperforming items, accompanied by a 36% reduction in overall campaign costs. Total revenue saw a 4% increase, evidencing the efficacy of the targeted strategy. Furthermore, conversion rates across the full catalogue rose by 29%, coupled with a 14% decrease in CPC, resulting in budget savings. This transformative outcome assured the client in expanding the partnership, allowing Bidnamic to now manage all Google Ads, including Search Ads and Bing Ads.
Keywords
GOOGLE SHOPPING
PERFORMANCE MARKETING
INVENTORY OPTIMIZATION
ROAS IMPROVEMENT
SKU-LEVEL BIDDING
DIGITAL ADVERTISING
SEO STRATEGIES
ECOMMERCE SCALE
Bidnamic
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