35% Conversion Lift + 60% Email Opens

AI personalization engine that showed different products to each visitor and automated email campaigns with smart subject lines

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E-commerce Personalization

Project Overview

The Challenge

LuxeStyle's marketing team was sending generic emails to all customers and showing the same products to every website visitor. Their email open rates were stuck at 18%, and they couldn't understand why similar customers had completely different purchasing behaviors. They needed personalization at scale for their Black Friday campaign.

Client Context

LuxeStyle Marketplace is a fast-growing e-commerce platform specializing in premium fashion and accessories. With 200,000 active customers and $45M in annual revenue, they had achieved product-market fit but were hitting a ceiling on customer lifetime value and repeat purchase rates.

The company was preparing for their biggest Black Friday campaign ever, with a $2M marketing budget and ambitious goals to double their customer base. However, their generic marketing approach was producing diminishing returns. They needed to demonstrate significant improvement in conversion rates and customer engagement to justify the increased marketing spend to their board.

Before working with us, LuxeStyle used their email platform's basic segmentation features, dividing customers into 5 broad categories based on purchase history. They tried hiring a data science team to build custom recommendations, but the 6-month project failed due to lack of real-time infrastructure and poor integration with their marketing stack. Their website showed the same hero products to all visitors regardless of preferences.

The Problem

Specific Symptoms

Email open rates stuck at 18%, well below fashion industry average of 28%

Website conversion rate of 2.1% significantly lower than personalized competitors at 3.5%+

60% of email campaigns resulted in unsubscribes rather than purchases

Average order value of $87 hadn't increased in 18 months despite expanding product catalog

Customer support team received 200+ weekly emails asking about products 'that would suit my style'

What Was at Stake

With Black Friday approaching and a $2M marketing budget on the line, LuxeStyle needed to demonstrate that increased marketing spend would drive proportional revenue growth. Their current conversion rates meant they would lose money on the Black Friday campaign. Each percentage point of conversion improvement represented $450,000 in additional revenue. Without personalization, they risked losing market share to competitors who were already delivering personalized experiences.

The Challenge

Technical Complexity

The challenge required building real-time recommendation engines that could process browsing behavior, purchase history, and contextual signals to personalize both website content and email campaigns. We needed to handle 50,000 daily visitors with sub-100ms latency for product recommendations, while also generating personalized email subject lines and product selections for 200,000 customers. The system had to integrate with Shopify, Klaviyo, and their existing analytics stack without disrupting operations.

Constraints

Budget

Fixed budget with requirement to show ROI within first campaign

Timeline

8-week hard deadline before Black Friday campaign launch

Tech Stack

Must work with existing Shopify Plus, Klaviyo, and Google Analytics setup

Other Constraints

No site downtime allowed during peak shopping hours

Must support mobile and desktop experiences

Email personalization must work with existing templates

Stakeholder Concerns

The CMO was worried that personalization would feel 'creepy' to customers and increase privacy complaints. The engineering team was concerned about adding latency to the website. The creative team feared AI-generated content would lack brand voice. We needed to balance powerful personalization with brand consistency and customer comfort.

Implementation Process

1

Discovery Phase (1.5 weeks)

We analyzed 2 years of customer behavior data across 200,000 customers and 50,000 products. We discovered that customers fell into 7 distinct style personas with dramatically different purchase patterns. Most surprisingly, we found that 40% of customers never saw products that matched their style preferences because they were buried in catalog pages. This insight drove our personalization strategy.

2

Build Phase (5 weeks)

We built a multi-layer personalization engine combining collaborative filtering for product recommendations, natural language processing for email subject line optimization, and computer vision for style matching. The system learns from each customer interaction and updates recommendations in real-time. We integrated with Shopify's API for product catalog access and Klaviyo's API for email personalization, ensuring all personalization felt native to the existing brand experience.

3

Launch & Iteration (1.5 weeks)

We launched with an A/B test on 30% of traffic and 30% of email subscribers to validate the approach before Black Friday. After confirming 28% increase in conversion and 45% increase in email opens, we scaled to 100% of customers. We provided the team with a personalization dashboard to monitor performance and adjust strategies in real-time during the critical Black Friday weekend.

Our Solution

1

Built AI personalization that analyzed customer behavior, purchase history, and browsing patterns to show relevant products

2

Connected AI to their email platform to generate personalized subject lines and send times for each customer

3

Implemented dynamic website content that changed product recommendations based on visitor's location, past purchases, and interests

4

Created automated cart abandonment emails that used AI to suggest the most relevant products to recover sales

5

Developed customer segmentation that identified high-value customers and created targeted marketing campaigns

Technology Stack

KlaviyoShopify Plus APIOpenAI GPTGoogle AnalyticsTensorFlow.jsSegment CDPAmplitudeDynamic YieldMailchimpCustom ML Models

Key Outcomes

Black Friday email campaigns generated $2.1M in revenue with 60% open rates and 29% click rates

Personalized product recommendations increased average order value by 29%

Customer lifetime value increased by 38% through targeted marketing campaigns

Cart abandonment recovery emails generated $340K in additional revenue per month

Customer segmentation enabled premium tier that increased profit margins by 45%

Results validated within 2 weeks of A/B testing, full impact realized during Black Friday campaign

The Transformation

Before

Generic email campaigns sent to entire customer list with 18% open rates

Same hero products shown to all website visitors regardless of preferences

Marketing team spent 20 hours weekly manually segmenting customer lists

Average order value stuck at $87 with no growth despite catalog expansion

Cart abandonment emails had 12% recovery rate with generic product suggestions

After

Personalized emails with AI-generated subject lines achieve 60% open rates

Each visitor sees products matching their unique style preferences and browsing history

Automated personalization runs 24/7 with real-time updates based on customer behavior

Average order value increased to $112 through intelligent upsell recommendations

Cart abandonment emails recover 42% of abandoned carts with personalized product suggestions

DozalDevs completely transformed how we think about customer engagement. Before their AI personalization, we treated all customers the same. Now every email, every product recommendation, every website experience is tailored. Our Black Friday revenue doubled, and our customers actually look forward to our emails.

The DozalDevs team delivered what our internal data science team couldn't in 6 months. They understood that e-commerce personalization isn't just about algorithms - it's about respecting brand voice, customer privacy, and business goals. The personalization dashboard they built gave our marketing team superpowers. We can now test new strategies in real-time and see immediate results. Our Black Friday campaign was our most successful ever, and the system continues to deliver value every day.

Marcus Thompson

CTO, LuxeStyle Marketplace

Technical Deep Dive

Key Technical Challenges Solved

Sub-100ms latency for product recommendations

We implemented a multi-tier caching strategy with Redis for hot products and precomputed recommendations for returning users. Real-time recommendations use a lightweight collaborative filtering model that processes user behavior in under 50ms. For cold-start users, we fall back to trending products in their browsing category. The system handles 5,000 requests per second during peak traffic without degradation.

Email subject line generation at scale

Built a fine-tuned GPT model trained on 2 years of LuxeStyle email campaigns and high-performing subject lines from the fashion industry. The model generates subject lines that match brand voice while incorporating personalization elements like customer name, style preferences, and browsing history. A/B testing infrastructure automatically validates new subject lines and improves the model over time.

Style matching across 50,000 products

Implemented computer vision models to extract style attributes from product images (color palettes, patterns, silhouettes). Combined with customer purchase history and browsing behavior, we built style preference profiles for each customer. The recommendation engine matches products to customer style profiles with 82% accuracy, validated through click-through and purchase rates.

Scalability Considerations

The architecture supports 10x traffic growth with horizontal scaling. We use Shopify's edge network for product data delivery, serverless functions for personalization logic, and a distributed caching layer for recommendations. The system automatically scales during traffic spikes like Black Friday without manual intervention. Current infrastructure handles 50,000 daily active users with room to grow to 500,000.

Security & Compliance

All customer data is encrypted end-to-end and stored in compliance with GDPR and CCPA requirements. We implemented privacy-preserving recommendation algorithms that don't require storing raw browsing history. Customers can opt out of personalization and view their data profile at any time. The system includes automated data retention policies and supports right-to-deletion requests within 48 hours.

Project Details

Client

LuxeStyle Marketplace

Industry

E-commerce / Fashion

Timeline

8 weeks

Team Size

AI marketing solutions team

Impact Metrics

+35%

Conversion Rate

60%

Email Open Rate

+29%

Average Order Value

42%

Cart Recovery Rate

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