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MulMul is a leading D2C apparel brand in India, known for its premium, handcrafted fashion collections. Focused on sustainable growth and high-quality products, MulMul leverages the D2C model to connect directly to consumers, cutting through retail channels and providing a seamless shopping experience. Their innovative designs and focus on craftsmanship have established them as a strong brand in the fashion brand category. With a dedicated customer base, MulMul continues to expand in the D2C space, aiming to optimize their online presence and enhance their customer experience.
While MulMul had a solid foundation in the D2C market, they were facing several key challenges that hindered their growth potential:
1. High Bounce Rates and Checkout Funnel Leakage
Visitors were leaving early in the shopping process, causing significant checkout funnel leakage. While traffic was steady, many users weren’t progressing from the product pages to completing their purchases, hurting overall sales and conversion potential.
2. Limited Discovery of Exclusive Collections
Despite having high-end exclusive collections, these products weren’t receiving the visibility necessary for customers to discover and engage with them. MulMul needed a better way to elevate these collections and make them more accessible to potential customers without over-relying on paid advertising.
3. Low Add-to-Cart Rates
Though the website was attracting visitors, add-to-cart rates were low, limiting product discovery and purchase intent. There was a disconnect between the products displayed and the actual customer journey, resulting in missed opportunities for conversion.
To address these challenges, MulMul partnered with Cooee, a leader in AI-driven personalization. Together, we implemented a suite of data-driven marketing strategies to optimize the website’s user experience and boost engagement. Here's how we tackled the issues:
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By integrating Cooee’s AI-driven platform, we analyzed customer data in real time to track microbehaviours like:
This enabled us to serve personalized product recommendations at strategic moments in the customer journey. For example, if a visitor lingered on a particular category, we dynamically displayed relevant items to keep them engaged, boosting the likelihood of a purchase.
This approach helped us increase add-to-cart rates by 20% while also improving the overall shopping experience.
To solve the issue of underexposure of exclusive collections, we introduced dynamic product embeds to highlight high-margin items at key touchpoints:
This strategy significantly improved the visibility of exclusive collections, driving a 30% increase in views of these items. Through organic marketing and content marketing, MulMul could focus on customer engagement rather than relying heavily on paid advertising.
When exclusive products were promoted via Cooee’s embedded product campaigns for most viewed collections on landing pages, the results were striking. The average order value (AOV) for items promoted this way increased by 30%, contributing to greater revenue per visit. This shift was largely driven by presenting related products to the right audience at the right time.
We also focused on improving cart abandonment rates and streamlining the customer journey. By targeting customers who had left items in their cart with retargeting ads and personalized reminders, MulMul saw a 24% improvement in abandoned cart conversions.
Additionally, the session-to-conversion time was reduced. What previously took 5–6 sessions to convert now took only 4, resulting in a faster, more efficient path to purchase.
By incorporating customer feedback from email marketing, influencer marketing, and direct interactions, we refined our approach continuously. This helped ensure that customer-centric marketing campaigns resonated with the right audience, driving higher customer retention and encouraging repeat purchases.
After implementing these strategies, MulMul saw significant improvements in critical eCommerce metrics:
Through AI-driven personalization, real-time product recommendations, and cross-selling efforts, we increased add-to-cart rates by 20%, leading to higher engagement and purchase intent.
Dynamic embeds for exclusive products led to a 30% increase in views, driving more interest and brand awareness without relying heavily on paid advertising.
When exclusive products were promoted through Cooee’s embedded product campaigns, AOV grew from Rs 10,000 to Rs 13,000, a 30% increase, which significantly improved revenue per visitor.
Through retargeting and personalized campaigns, cart abandonment dropped, resulting in a 24% boost in abandoned cart conversions.
The time required for visitors to convert was reduced from 5–6 sessions to just 4, accelerating the customer journey and increasing conversion speed.
This case study underscores the power of AI-powered personalization and data-driven strategies for D2C apparel brands aiming to optimize eCommerce performance. By addressing checkout funnel leakage, boosting add-to-cart rates, and improving abandoned cart conversion, MulMul achieved a substantial increase in revenue and customer retention.
In today’s competitive D2C space, brands must leverage customer data and provide personalized, seamless experiences to drive organic growth and sustainable expansion. For D2C brands looking to grow in the fashion brand market, investing in AI-driven personalization and customer-centric strategies is essential for long-term success.
At Cooee, we help eCommerce brands unlock the full potential of their online presence through AI-driven insights and personalized strategies that drive higher engagement, boost conversions, and deliver strong ROI.
Get in touch today to learn how we can help optimize your customer journey, increase lifetime value, and accelerate your brand’s growth.