Topic covered
CUSTOMER STORIES
How Amar Chitra Katha Increased AOV by 13.76% with Anphonic
Amar Chitra Katha partnered with Anphonic to drive higher-value purchases through personalized discovery and bundling, achieving a 13.76% increase in Average Order Value, influencing 17.60% of total orders, and generating 12.09% of total revenue through personalized shopping journeys.
Results
13.76% increase in AOV through personalized experiences
17.60% of total orders influenced by Anphonic
12.09% of total revenue generated through personalization
28.76% of revenue driven by Product Discovery
Use Cases
Content-Led Product Discovery helped users discover relevant titles and collections, increasing cross-category purchases.
Dynamic bundles encouraged users to purchase multiple books from the same series or theme.
Product add-ons nudged users to include more titles in their cart.
Cart Upsells helped increase final basket value before checkout.
Industry
Stationery and Lifestyle Goods
Amar Chitra Katha is an iconic Indian publishing brand known for its illustrated stories rooted in mythology, history, and culture. With a vast catalog of titles, enabling discovery and encouraging collection-based purchases is key to driving higher order values.
Challenge: Increasing Basket Size in Content-Driven Commerce
Amar Chitra Katha operates in a category where users often purchase individual titles rather than exploring the full catalog.
This leads to:
- Single-book purchases
- Limited discovery of related titles
- Missed opportunities for collection-based buying
The brand’s key objectives were to:
- Increase AOV through multi-book purchases
- Improve product discovery across titles
- Encourage themed and collection-based buying
- Influence more orders through personalization
They needed a solution that could guide users toward discovering and purchasing more.
Strategy: Discovery-Led Personalization with Smart Bundling
Anphonic was implemented across the customer journey with a focus on:
- Personalized discovery of relevant titles
- Bundling books into themes and collections
- Nudging users toward multi-item purchases
Using behavioral signals, Anphonic ensured users were consistently guided toward higher-value baskets.
Key Approaches
Product Discovery
Product discovery contributed 28.76% of total revenue, acting as the primary driver of exploration and cross-selling.
AI recommendations helped users find relevant books across categories, increasing engagement and basket size.
Rebuy Product Recommendations
Returning users contributed 25.06% of revenue, driven by personalized suggestions based on past purchases.
Dynamic Product Bundles
Bundles contributed 18.55% of revenue, encouraging users to purchase multiple titles together.
Product Add-ons
Add-ons contributed 15.39% of revenue, helping increase order value through low-friction upsells.
Cart Upsells
Cart-based recommendations contributed ~12% of revenue, driving incremental value at checkout.
Results: Measurable Impact
Within a short span, Amar Chitra Katha saw clear improvements:
- Increased multi-book purchases across the catalog
- Higher AOV driven by bundling and discovery
- Strong contribution from returning users
- Balanced revenue across multiple personalization levers
The results highlighted the importance of guiding users in content-heavy catalogs.
Impact: Driving Collection-Based Purchases
Anphonic enabled Amar Chitra Katha to shift from single-title purchases to collection-based buying behavior.
Instead of relying solely on user intent, the brand increased revenue by:
- Encouraging users to explore related stories
- Bundling books into meaningful collections
- Personalizing recommendations across the journey
This resulted in higher basket sizes and deeper engagement with the catalog.
Looking Ahead
With strong gains in AOV and order influence, Amar Chitra Katha can expand personalization into:
- Age-based and interest-based recommendations
- Subscription and repeat reading journeys
- Curated collections for gifting and learning
FAQs
1. Did Amar Chitra Katha rely on discounts for growth?
No. Growth was driven through discovery, bundling, and personalization.
2. Which feature contributed the most revenue?
Product Discovery was the highest contributor, followed by returning user recommendations.
3. Why is bundling effective for publishing?
Because users are more likely to purchase multiple related titles when grouped meaningfully.
4. Did personalization improve user experience?
Yes. It made it easier for users to discover relevant content.
5. How quickly were results visible?
Results were observed within weeks of implementation.
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