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Apparel brand storefront with seasonal collection

  • 19%

    Increased Transaction Volume

  • 7%

    Increase in Spends

  • 37%

    CPQL Dropped By

AI-Powered Performance Optimization for Multi-Channel E-Commerce Growth in Apparel

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ProblemStatement

  • Tracking Transactions Across Sales Models

    Operating across online and offline channels created tracking and attribution challenges. Without a unified view, budget allocation was inefficient and scaling transactions across both models was difficult.

  • Optimizing Media Mix for Higher Conversions

    With multiple marketing channels, the brand struggled to identify the most effective media mix to drive transactions. Poor cross-channel coordination led to missed revenue opportunities and an inconsistent customer experience.

  • Reducing CPL & CPQL

    Rising acquisition costs made it harder to keep CPL and CPQL low while ensuring lead quality. Inefficient targeting resulted in wasted ad spend on low-intent users, increasing costs and reducing ROI.

  • Centralized Data Measurement

    Data was scattered across platforms, making it difficult to measure performance accurately. Without a centralized data framework, reporting inconsistencies led to ineffective optimizations and budget allocation.

Maino’s Approach

Marketing team reviewing omnichannel performance

  1. 1.Unified Data Integration for Smarter Decision-Making

    Maino.ai consolidated data from CRM systems, marketing platforms, and analytics tools to create a centralized, real-time view of transactions and leads across both online and offline sales channels. Advanced statistical models analyzed the relationship between marketing spend and transaction volume, helping the brand optimize budget allocation on data-driven insights.

    Impact

    Improved marketing efficiency and better transaction tracking across channels.

  2. 2.AI-Powered Audience Creation for Higher Conversions

    Maino.ai leveraged offline power-user data to create highly targeted performance channel cohorts. By analyzing past high-value customers, the AI identified shared characteristics and behavioral patterns to build refined audience segments, directing ad impressions toward users with a higher likelihood of conversion.

    Impact

    Increased lead quality and higher conversion rates with optimized targeting.

  3. 3.Persona & Interest-Based Insights for Precision Targeting

    Maino.ai conducted a deep analysis of online and offline user personas to identify interest clusters on Meta and other advertising platforms. By comparing behavioral trends and engagement levels, the brand created a highly tailored, channel-specific media plan that improved relevance, engagement, and conversion rates.

    Impact

    Higher ad relevance, stronger engagement, and improved conversion rates.

  4. 4.Predictive Analytics for Lead Optimization

    Using AI-driven correlation analysis of CRM data, Maino.ai identified lead attributes with the highest likelihood of conversion. These insights powered real-time bid adjustments, audience refinements, and budget reallocations — continuously lowering CPL and CPQL while maintaining lead quality.

    Impact

    37% reduction in CPQL while sustaining lead volume.

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