How DeepM Helped a Leading Sweat-Control Brand Strengthen Visibility Across Search Clusters: Facial, Body, and Peripheral Care
A U.S.-based brand specializing in sweat control and absorption products partnered with DeepM to improve its visibility across Amazon’s highly competitive personal-care landscape. The company develops dermatologist-tested, skin-safe antiperspirants and sweat-absorbing products for multiple body areas — from underarms, face, and scalp to hands, feet, and sensitive regions — helping users feel confident and comfortable in any situation. Its catalog includes lotions, wipes, and powders designed to address the full spectrum of sweat challenges, combining clinical effectiveness with everyday usability.
The Challenge
Although the brand already offered one of the most comprehensive sweat-control portfolios on Amazon, its visibility and conversion performance didn’t yet reflect the scope of its solutions. Advertising budgets and attention were spread broadly across keywords, rather than being concentrated in the search clusters with the strongest short-to-medium-term ROI potential.
The DeepM Strategy: Targeting What Moves the Needle
DeepM’s work began by mapping the brand’s full keyword landscape across antiperspirant and sweat-control categories. Through its predictive modeling framework, the system ranked search terms by volume, competitive density, and ranking elasticity — identifying where small investments could yield measurable position and share gains. This analysis surfaced three functional clusters that best reflected the brand’s product line and search potential:
- Facial & Targeted Sweat Control – terms like face antiperspirant, scalp antiperspirant, and sweat lotion showed strong responsiveness and clear ranking mobility.
- Body & Gender-Specific Care – including antiperspirant for women and breast deodorant for women, which had healthy volume but slower elasticity.
- Peripheral & General Hygiene – such as anti sweat wipes and foot odor eliminator, supporting awareness across adjacent product types.
DeepM guided a selective reallocation of sponsored spend and optimization effort, ensuring that high-elasticity clusters received proportionally higher visibility. The brand implemented these recommendations directly, adjusting campaigns and keyword targeting to better mirror Amazon’s internal relevance signals.
This data-driven reprioritization transformed how resources were deployed — shifting focus from even distribution to performance-weighted investment, setting the stage for measurable gains in both ranking and share.
Results
The refined spend allocation and cluster prioritization produced a clear upward trend across nearly all targeted terms, validating DeepM’s predictive identification of high-elasticity opportunities. Across the board, the brand’s products improved their organic positions by an average of 8–12 ranks.
Market share gains reflected the same pattern: most targeted keywords, previously registering negligible presence, now captured low- to mid-single-digit shares, while several strategic terms achieved double-digit growth. Examples:
- “Face antiperspirant” advanced from #19 to #10, gaining 14% in market share.
- “Antiperspirant for women” rose from #50 to #28.5, gaining 3% in market share.
- “Antiperspirant for face” moved from #20 to #10, gaining 5% in market share.
- “Anti sweat wipes” improved from #10 to #6, gaining 6% in market share.
- “Scalp antiperspirant” advanced from #30 to #16, gaining 4% in market share.
Conclusion
DeepM’s structured, data-driven prioritization helped a leading sweat-control brand achieve measurable visibility gains across facial, body, and peripheral-care categories. By pinpointing the search clusters with the highest ROI potential and guiding smarter spend allocation, DeepM enabled the brand to channel resources where ranking elasticity was strongest. The outcome was a broad repositioning — elevating visibility, driving market-share gains, and laying the groundwork for sustained growth across the full body-care spectrum.
Why It Worked
- Cluster intelligence – DeepM identified three functional ecosystems — facial, body, and peripheral — allowing resources to concentrate where organic lift was most achievable.
- Data-led spend efficiency – Optimization focused not on increasing spend, but on directing it toward terms with quantifiable ranking responsiveness.
- ROI-driven decisioning – Predictive modeling quantified elasticity per cluster, ensuring every investment decision was grounded in data rather than intuition.
- Brand agility – The brand implemented adjustments quickly and precisely, translating DeepM’s strategic guidance into measurable performance improvements within a single optimization cycle.
