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

Result:

19%

Increased Transaction Volume

7%

Increase in spends

37%

CPQL dropped by

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Problem Statement:

Tracking Transactions Across Sales Models

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

Optimizing Media Mix for Higher Conversions

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

Reduced CPL and CPQL

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

Centralized Data Measurement for Smarter Marketing

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

Maino’s Approach:

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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. This eliminated data silos and provided a holistic performance tracking system. Additionally, advanced statistical models were built to analyze the relationship between marketing spend and overall transaction volume, helping the brand optimize budget allocation based on data-driven insights rather than assumptions.

Impact - Improved marketing efficiency and better transaction tracking across online and offline channels.

2 . AI-Powered Audience Creation for Higher Conversions

To ensure marketing efforts reached the right users, 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. This allowed ad impressions to be directed toward users with a higher likelihood of conversion, improving both reach and lead quality while minimizing wasted ad spend.

Impact - Increased lead quality and higher conversion rates with optimized targeting

3 . Persona and Interest-Based Insights for Precision Targeting

Understanding audience behavior was critical to optimizing ad performance. 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 system provided a more granular view of audience preferences, allowing the brand to create a highly tailored, channel-specific media plan. This improved relevance, engagement, and conversion rates across different ad placements.

Impact - Higher ad relevance, stronger engagement, and improved conversion rates across platforms.

4 . Predictive Analytics for Lead Optimization & Cost Efficiency

Using AI-driven correlation analysis of CRM data, Maino.ai identified lead attributes that had the highest likelihood of conversion. These insights were fed back into marketing platforms, enabling real-time bid adjustments, audience refinements, and budget reallocations. By continuously learning and optimizing, the system lowered CPL and CPQL while maintaining lead quality, ensuring marketing dollars were spent on the most promising prospects.

Impact - 37% reduction in CPQL, ensuring higher-quality leads at lower cost.

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