Introduction
In today’s retail landscape, customers expect every interaction to feel personal from the emails they open to the offers they receive in-store. Generic, one-size-fits-all campaigns no longer capture attention, often leading to disengagement and declining loyalty.
To meet these rising expectations, a leading omni-channel retailer implemented Salesforce Marketing Cloud. By unifying customer data, automating engagement, and leveraging AI-powered recommendations, the company achieved a 32% uplift in personalised revenue while also improving customer loyalty and marketing efficiency.
Industry & Reach
Multinational financial services provider with 10M+ customers and 200+ branches.
Services
Retail and corporate banking, insurance, loans, investments, and wealth management.
Digital Presence
Strong online platforms but struggled with seamless, personalized service.
Strategies
Used AI chatbots to cut waits, link systems, and personalize service.
Unified Customer Data
Personalisation
AI Chatbot Support
Insights
Project Objectives
The initiative aimed to drive revenue growth and loyalty by unifying customer data and scaling personalisation.
The retailer’s key goals were to:
Boost personalised revenue by 30%+.
Unify customer data into a single source of truth.
Automate campaigns across all channels.
Increase retention and loyalty through tailored engagement.
Achieve 3x ROI within the first year.
Project Challenges
Despite its scale, the retailer faced barriers that made it difficult to deliver truly personalised experiences. Systems were disconnected, processes were inefficient, and marketing lacked precision.
Data Silos
Information scattered across CRM, POS, loyalty, and e-commerce systems.
Generic Campaigns
Mass promotions failed to connect with individual preferences.
Manual Processes
Campaign setup was slow, limiting responsiveness.
Retention Gaps
Inconsistent personalisation contributed to churn.
Limited Analytics
Lack of a central dashboard for measuring performance.
Scaling Difficulties
Growing customer base made engagement harder to sustain.
Solutions
To address these challenges, the retailer launched a comprehensive personalisation strategy powered by Salesforce Marketing Cloud. The solution focused on unifying data, scaling automation, and delivering AI-driven experiences across all touchpoints.
Key solutions included:
Customer Data Platform: Unified customer profiles by integrating CRM, POS, e-commerce, and loyalty data.
Journey Builder: Automated lifecycle campaigns across email, SMS, push, and in-app channels.
AI-Powered Recommendations (Einstein): Delivered product suggestions based on browsing history and past purchases.
Dynamic Content: Real-time personalisation for emails, push notifications, and web banners.
Cross-Channel Consistency: Seamless experiences across online and offline touchpoints.
Automation: Reduced manual workload, accelerating campaign deployment.
Advanced Machine Learning Models: Used LSTM and XGBoost to incorporate sales, seasonal, and external data (like weather and campaigns) for highly accurate demand forecasting, enabling smarter inventory allocation and minimizing inefficiencies.
Automation in Procurement & Logistics: Accelerated procurement workflows, optimized warehouse allocation based on regional demand trends, and leveraged AI-driven route optimization with IoT-enabled tracking to cut costs and delivery times.
Centralized Analytics Platform: Unified ERP, CRM, and WMS data into a single source of truth, providing predictive alerts, real-time performance tracking, and data-driven decision support.
Development Process
The project followed a phased rollout designed to ensure smooth adoption and measurable impact. It combined robust data integration, AI model deployment, and iterative campaign optimisation.
Data Integration
Unified CRM, POS, e-commerce, and loyalty data.
Segmentation
Built dynamic groups by demographics and behavior.
Journey Design
Built journeys for acquisition and retention.
Pilot Campaigns
Tested messaging, timing, and targeting through A/B experiments.
Analytics Dashboard
Enabled real-time reporting to track ROI and performance.
Continuous Optimisation
Enhanced campaigns using AI insights and customer feedback.
Development Process
The project followed a phased rollout designed to ensure smooth adoption and measurable impact. It combined robust data integration, AI model deployment, and iterative campaign optimisation.
Data Integration
Unified CRM, POS, e-commerce, and loyalty data.
Segmentation
Built dynamic groups by demographics and behavior.
Journey Design
Built journeys for acquisition and retention.
Pilot Campaigns
Tested messaging, timing, and targeting through A/B experiments.
Analytics Dashboard
Enabled real-time reporting to track ROI and performance.
Continuous Optimisation
Enhanced campaigns using AI insights and customer feedback.
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The Results
The implementation of AI-driven predictive maintenance delivered measurable results:
- Failure Prediction Accuracy: Increased to 95%.
- Unplanned Downtime: Reduced by 40%, saving thousands of production hours annually.
- Maintenance Costs: Lowered by 25%, saving millions in operational expenses.
- Equipment Lifespan: Extended by 20%, reducing replacement investments.
- Production Efficiency: Improved by 15%, ensuring consistent throughput.
- Customer Satisfaction: On-time delivery performance rose to 96%.
- ROI: Achieved 200% return on investment within the first year.
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SDLC CORP guided our team through an AI discovery sprint, mapped key use cases, fixed messy data, and delivered a clear step-by-step roadmap. Thanks to their work, executives now green-light projects faster and engineers move from idea to pilot without delays.
Overall Satisfaction
We hired SDLC CORP’s AI consultancy to automate document review with NLP. They built and trained a model in weeks, plugged it into our workflow, and walked staff through daily use. The system now flags errors on its own and cut processing time by more than half.
Overall Satisfaction
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Overall Satisfaction
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