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Homeβ€ΊCustomersβ€ΊGIVA Case Study: Elevating Conversions Through Personalized Shopping Journeys with CustomFit.ai
Case Study

GIVA Case Study: Elevating Conversions Through Personalized Shopping Journeys with CustomFit.ai

GIVA Elevating Conversions Through Personalized Shopping Journeys with CustomFit.ai

GIVA Case Study: Elevating Conversions Through Personalized Shopping Journeys with CustomFit.ai
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GIVA, a fast-growing jewelry brand, partnered with CustomFit.ai, website personaization tool, to transform its online shopping experience with personalization at scale. By testing multiple variations across product discovery, category visibility, checkout flow, and promotional nudges, GIVA unlocked significant improvements in engagement and conversions.

‍

Brand Background

GIVA is a leading D2C jewelry brand, offering elegant silver and gold collections designed for everyday luxury. With a strong focus on customer delight, GIVA leverages technology and personalization to simplify buying journeys and increase sales velocity.

‍

Experiment 1: Two-Row Category Display

**Objective:**Improve product discovery and category clicks by showing more visible options upfront.

Methodology:

  • Personalized Variant: Two visible rows of categories placed prominently on the homepage.
  • Default Variant: A single row requiring users to scroll for more options.

Categories are now displayed in two visible rows

‍

Results:

  • Higher visibility of categories led to more clicks across product collections.
  • Customers explored beyond top categories, improving cross-category discovery.

**Insights:**Making categories instantly accessible reduces friction, encourages exploration, and sets the stage for higher conversions.

‍

Experiment 2: Toaster for Add-to-Cart

**Objective:**Streamline the cart experience and reduce friction after adding items.

Methodology:

  • Personalized Variant: Replaced the popup modal with a toaster notification at the bottom, auto-closing after 3 seconds, while still offering β€œView Cart” and β€œCheckout Now.”
  • Default Variant: Popup overlay interrupting the browsing experience.

Remove pop up on Add to Cart and show Toaster

‍

Results:

  • Customers preferred the lightweight, non-intrusive experience.
  • Improved continuation of browsing and faster checkout decisions.

**Insights:**Minimizing interruptions creates smoother flows, keeping users engaged with the product catalog.

‍

Experiment 3: PDP Variants β€” Model vs Mood

**Objective:**Test which product display style resonates more with customersβ€”lifestyle images with a model or clean, product-focused β€œmood” shots.

Methodology:

  • Split traffic evenly between the Model Variant and the MoodMood Variant.

Mood vs Model vs Standard

‍

Results:

  • Clear differences in engagement patterns:

    • Model images boosted emotional connection and aspiration.
    • Mood images gave clarity on product details.

Mood vs Model vs Standard - 50% of the users will see the model image as the hero image and rest of them will see the mood image

‍

**Insights:**Different product categories may require different PDP approaches. Lifestyle imagery builds desire, while mood/product shots work best for detail-oriented shoppers.

‍

Experiment 4: Nudges on Cart & Wishlist

**Objective:**Increase conversions by re-engaging shoppers who had items in cart or wishlist.

Methodology:

  • Cart Nudge: Personalized banner reminding users of treasures waiting in their cart.
  • Wishlist Nudge: Gentle push for customers with saved items, encouraging checkout.

cart nudge vs Wishlist Nudge

‍

Results:

  • Both nudges drove strong re-engagement and improved conversion rates.
  • Cart nudges performed particularly well by reducing abandonment.

**Insights:**Timely reminders, especially contextual ones, act as effective triggers without feeling pushy.

‍

Experiment 5: Search-Driven Dynamic Banners

**Objective:**Personalize discovery by aligning banners with user intent.

Methodology:

  • Banners dynamically changed based on user’s search term (e.g., β€œBracelets” β†’ bracelet-related creative).

Search Banner: Based on user's search products

‍

Results:

  • Increased click-through on banners as users connected with relevant visuals.
  • Improved product discovery and intent alignment.

**Insights:**Dynamic personalization strengthens relevance and captures shopper attention at the right moment.

‍

Experiment 6: Pincode-Based Same-Day Delivery

**Objective:**Boost urgency and trust through delivery promise personalization.

Methodology:

  • Before 1 PM: Banner and category displayed β€œGet it Today.”
  • After 1 PM: Automatically switched to β€œGet it by Tomorrow.”
  • Shown only to eligible pin codes.

Pincode-Based Same-Day Delivery

Results:

  • Improved conversions in eligible cities where delivery speed influenced buying decisions.
  • Shoppers responded strongly to same-day delivery assurance.

**Insights:**Delivery-based personalization builds urgency and competitive advantage, especially in fast-moving gifting categories like jewelry.

‍

Experiment 7: Shoppable Celebrity Reels

Objective:
Enhance product discovery and engagement by leveraging celebrity-driven video content and making it directly shoppable.

Methodology:

  • Personalized Variant: Integrated video reels featuring celebrities wearing GIVA products. Each reel was made shoppable, allowing customers to add items to their cart directly from the video.
    ‍
  • Default Variant: Standard product listings and banners without interactive video content.

shoppable Celebrity video reels

‍

Results:

  • The personalized variant drove significantly higher engagement rates, as shoppers interacted with video content more than static images.
    ‍
  • Customers showed greater intent to purchase when seeing products styled by celebrities, validating the influence of social proof in luxury and fashion categories.
    ‍
  • Shoppable reels bridged the gap between inspiration and action, reducing the steps required to purchase.

Insights:

  • Video-first experiences are highly effective in jewelry and fashion e-commerce, where lifestyle appeal is critical.
    ‍
  • Making reels shoppable in real-time reduces drop-offs by enabling immediate product selection.
    ‍
  • Celebrity-driven content builds trust, aspiration, and brand authority, creating a stronger pull toward conversions.

‍

‍

Conclusion

Through a series of personalization experiments powered by CustomFit.ai, no code a/b testing software, GIVA achieved tangible improvements in engagement and conversions:

  • Category clicks increased with two-row display.
  • Smoother checkout experience via toaster ATC flow.
  • Higher emotional connect with PDP image testing.
  • Re-engagement uplift through cart and wishlist nudges.
  • Relevance-driven discovery using search-personalized banners.
  • Urgency and trust boosts through pincode-based delivery messaging.

GIVA’s journey highlights how multi-layered personalization across the shopping funnel can significantly improve customer journeys and business outcomes. With actionable insights from each test, GIVA continues to refine its strategy to meet customer expectations and lead in the competitive D2C jewelry space.