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Digital sales platforms increasingly face challenges related to customer journey friction, which can lead to drop-offs and lower conversion rates. This paper presents a data-driven approach combining data engineering and machine learning techniques to detect and mitigate friction points in digital customer interactions. By analyzing data from various digital touchpoints, our models identify, predict, and provide actionable insights for high-friction interactions, leading to a more seamless experience and improved conversion metrics. Experimental results show that our approach significantly improves engagement and conversion rates.

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