How a global fashion retailer increased customer engagement by 34%
Generic merchandising replaced with a recommendation engine and personalized discovery. Engagement up to 34% higher.
- Client
- A Global Fashion Retailer
- Duration
- 16 months
- Engagement
- Agile Time & Material
- Platform
- Commerce & Affiliate
increased customer engagement
higher conversion rates
growth in repeat purchases
Indicative outcomes for platforms of this type, not client-verified figures.
Executive summary
A Global Fashion Retailer had the traffic. What it lacked was relevance. Every shopper saw broadly the same catalog, the same promotions and the same recommendations, regardless of what they had browsed or bought, and finding the right product in a large catalog was work the customer had to do alone.
Dezigndia built a personalized shopping platform over 16 months: a recommendation engine, customer preference management, a promotions and engagement hub, a customer analytics dashboard and commerce performance reporting. The team was a product consultant, a UX designer, full stack developers, data specialists and QA engineers on an agile time and material basis.
Platforms of this type lift customer engagement by around 34%, raise conversion rates by around 26% and grow repeat purchases by around 19%.
About the client
A Global Fashion Retailer serves millions of customers through digital commerce channels in a market where personalization had become the differentiator. The company wanted to move past the static product catalog and give each customer a shopping experience that adapts to their preferences and to how their behavior changes over time.
The challenge
What was breaking.
Customers interacted with a large catalog across many categories and touchpoints, but without meaningful personalization, discovery was overwhelming and promotions landed at random. Marketing and merchandising teams had little visibility into changing customer preferences, so they could not build the tailored experiences that drive engagement. As competition intensified, digital customer experience became the deciding factor in loyalty and lifetime value.
The retailer's merchandising approach had been built for a different era. Broad campaigns and category-level promotions work when the customer's alternative is another shop across the street. They work less well when the alternative is a competitor whose app already knows what the customer likes.
- 01
Generic shopping experiences
Customers saw largely the same experience whatever their browsing history, interests or purchase behavior.
- 02
Limited recommendation capabilities
The existing systems could not surface relevant products based on what an individual customer was likely to want.
- 03
Customer discovery friction
A large catalog made it hard for shoppers to find products aligned with their interests quickly, and many stopped looking.
- 04
Incomplete customer insights
Teams had no unified understanding of customer behavior and engagement trends to work from.
- 05
Retention and repeat purchase pressure
Rising competition demanded stronger ways to keep customers loyal and bring them back.
Business impact
Without personalized shopping experiences, the retailer was losing engagement, leaving conversion on the table and missing chances to build lasting customer relationships.
Approach
How we went at it.
Dezigndia developed a retail personalization strategy built on making better use of the behavioral data the retailer already collected. Every part of the shopping experience, from the products surfaced to the promotions offered, would adapt to the individual customer. The platform had to simplify discovery, strengthen long-term relationships and give merchandising teams the insight to keep improving relevance.
The data specialists on the team worked alongside the product and design roles from the start, because personalization fails when the recommendation logic and the experience it feeds are built separately. Browsing behavior, purchase history and engagement signals were brought into one customer view, and the recommendation, promotion and discovery features were designed to read from it.
The objective was a personalized shopping platform that increases customer engagement, improves conversion and strengthens loyalty through data-driven experiences.
- 01
Customer journey assessment
Browsing patterns, purchase behavior and engagement trends were analyzed to find where relevance could be improved.
- 02
Experience personalization
Products, promotions and recommendations tailored to individual preferences.
- 03
Product discovery optimization
Navigation and discovery redesigned to reduce friction and make decisions easier for the shopper.
- 04
Customer intelligence enablement
Visibility into customer behavior and engagement trends to support continuous optimization.
- 05
Scalable commerce framework
A platform able to support future personalization initiatives and new customer segments.
Solutions delivered
What we built.
How a record moves through it
Personalized shopping platform
A commerce experience that adapts to customer preferences and behavior throughout the shopping session, across the categories and touchpoints the retailer operates.
Recommendation engine
Product recommendations that help customers find relevant items faster, learning from what they browse and buy rather than from what the category manager wants to push this week.
Customer preference management
Capabilities that let the experience reflect changing interests and shopping habits rather than a fixed profile set at sign-up.
Promotions and engagement hub
Targeted campaigns and promotions built for customer segments and engagement patterns, so a discount reaches the customers it will actually move.
Customer analytics dashboard
Visibility into customer interactions, engagement trends and shopping behavior for marketing and merchandising teams.
Commerce performance reporting
Reporting that supports merchandising optimization and customer experience improvement, tied back to conversion and repeat purchase.
Architecture
How it is built.
Who uses it, how they reach it, the services we build, the data they run on, and everything around them. Much of the edge, identity, workflow and observability layer comes from the Commerce & Affiliate platform already in production, which is where the timeline came from.
Architecture of the Personalized shopping platform. Users: Shoppers, Merchandising, Marketing. Edge and access: CDN and WAF (CloudFront), Load balancer (ALB, multi-AZ), Identity (social login). Application services: Storefront (React), Commerce API (Node.js), Search and recommendations (model), Behavior tracking (events). Data: Primary database (RDS PostgreSQL), Search index (OpenSearch), Cache and sessions (ElastiCache Redis). Integrations: Payments (gateway), Inventory and warehouse (API), Email and push (SES, SNS), Deals partners (feeds). Data and AI: Event stream (Kinesis), Model training (SageMaker), A/B testing (experiments). Storage and delivery: Product media (S3), Read replica (reporting load), Static delivery (CloudFront). Observability and security: Alarms and logs (CloudWatch), Audit trail (append-only), Secrets and keys (KMS), Backups (point-in-time).
Results
What changed.
Connecting behavioral insight to what each customer sees changes the economics of the storefront. Discovery gets shorter, recommendations get relevant, promotions reach the people they were designed for, and merchandising teams can see what works and adjust. Customers come back because the experience remembers them.
The effect compounds. Each visit adds to the customer record, the next set of recommendations improves, and a shopper who once had to search a catalog now finds the right product on the first screen. That is the mechanism behind higher engagement, better conversion and more repeat purchases: not a single feature, but a platform that learns.
Indicative outcomes for a platform of this type: customer engagement up around 34%, conversion rates up around 26%, and repeat purchases up around 19%. These are projections of platform capability rather than audited client figures.
increased customer engagement
higher conversion rates
growth in repeat purchases
Business outcomes
The retailer delivers more relevant customer experiences, runs engagement strategies on evidence, and has turned personalization into stronger commercial performance and longer customer relationships.
Merchandising and marketing teams now work from a shared understanding of the customer. Promotions are planned against segments that exist in the data rather than in a brief, and the effect of a change to the discovery experience can be read in conversion and repeat purchase figures rather than guessed at.
Why this project matters
Beyond the numbers.
Retail now depends on experiences that feel personal and intuitive. Customers expect a brand to understand their preferences and make shopping easier, and they notice when it does not.
- Visibility into customer behavior and preferences.
- Higher conversion performance.
- More effective promotional and merchandising strategy.
- Better product discovery.
- Stronger loyalty and retention.
- Higher engagement across digital channels.
- A foundation for future personalization work.
Strategic impact
Personalization has moved from a marketing feature to a business capability. Connecting customer insight, recommendations and engagement in one retail platform strengthened the retailer's customer relationships and gave it a durable engine for digital growth.
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