Customer Success Story

Transforming Customer Support with an Intelligent AI Retail Assistant

CrossML helps a leading retail enterprise to enhance customer experience through an AI retail assistant, automating responses, personalizing recommendations, and driving faster resolutions across stores.

faster response time during peak hours

0 %

lower support handling costs

0 %

increase in daily sales

0 %

challenges

Delayed responses during high traffic periods caused customer dissatisfaction
Generic and repetitive replies lacked personalization and context
Poor system integration led to data silos across CRM, POS, and online channels
High staffing requirements made scaling costly during seasonal demand

phased journey

Understanding customer support challenges and identifying automation opportunities
Building an AI retail assistant for intelligent query handling
Integrating CRM, POS, and e-commerce data pipelines for context-aware interactions
Implementing analytics dashboards and sentiment monitoring for real-time performance tracking

results

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Industry: Retail & E-commerce

HQ: Australia

Size: 525,000+

Reimagining Retail Customer Support with an AI Retail Assistant

Retail customer support had become increasingly complex due to omnichannel engagement and growing service volumes.

Agents were overwhelmed with repetitive tasks, delayed responses, and limited visibility across systems.

To address these challenges, CrossML designed an AI retail assistant capable of understanding intent, learning context, and providing instant, human-like interactions.

The solution aimed to automate routine tasks, personalize engagement, and scale service delivery seamlessly across all digital and in-store platforms.

Phase 1: Understanding Support Workflows

CrossML began by studying the retailer’s existing support operations across stores, chat, and e-commerce platforms.

The analysis uncovered key inefficiencies like slow query resolution, repetitive requests, and disjointed customer data.

This phase defined the roadmap for automation, focusing on improving speed, consistency, and scalability in service delivery.

Phase 2: Building the AI Retail Assistant

With defined goals, CrossML developed an AI retail assistant to automate support and engagement:

Conversational Core: Trained on retail datasets to understand customer tone, intent, and query context.
Recommendation Engine: Delivered personalized product suggestions based on browsing and purchase history.
Query Routing: Automatically escalated complex requests to human agents with contextual details attached.

This phase established a conversational foundation that could respond intelligently, adapt to tone, and maintain consistent brand communication.

Phase 3: Seamless Data Integration and Automation

CrossML integrated the AI retail assistant with all core business systems to ensure live, accurate data flow:

CRM Integration: Accessed historical customer interactions and loyalty data for better personalization.
POS & Inventory APIs: Enabled real-time visibility of stock levels, prices, and order status.
Automation Pipelines: Synced every data update instantly, ensuring consistency across channels.

This integration allowed the AI retail assistant to deliver reliable, real-time responses, bridging the gap between digital and in-store service.

Phase 4: Performance Analytics and Sentiment Monitoring

In the final phase, CrossML implemented real-time dashboards and analytics to monitor service performance:

Sentiment Analysis: Tracked emotional tone and customer satisfaction in every conversation.
Live Dashboards: Provided visibility into key metrics such as resolution time, query volume, and escalation trends.
Continuous Optimization: AI models self-improved through customer feedback, enhancing accuracy and relevance over time.

This continuous feedback cycle turned customer service into a proactive, insight-driven function.

Delivering Intelligence That Redefines Retail Support

CrossML built a unified, intelligent platform that allowed the client to scale customer service efficiently while maintaining quality and personalization.

Key Deliverables

Together, these deliverables helped the enterprise achieve faster resolutions, stronger customer relationships, and a scalable support model built for the future of retail.

Centralized Support Interface: Unified all communication channels into a single AI-powered system.
Personalized Query Resolution:Delivered tailored responses using real-time purchase and interaction data.
CRM and POS Integration: Created seamless connectivity for consistent information flow across systems.
Automated Query Management: Reduced manual workload by automating repetitive inquiries and follow-ups.
Real-Time Monitoring Dashboard:Offered visibility into agent performance, sentiment, and resolution speed.
Scalable Architecture: Supported high query volumes during peak demand without increasing staffing.
Adaptive AI Models: Continuously improved accuracy through live data learning and feedback loops.

Benefits and ROI

Delivering measurable speed, personalization, and cost efficiency.

50% Faster Response Time

Automation of repetitive customer queries, order lookups, and product inquiries reduced average response time by half. Instead of waiting for manual agent intervention, customers received instant replies through the AI retail assistant that were powered by intelligent conversational logic.
This improvement not only boosted satisfaction during high-traffic periods but also allowed teams to handle significantly more queries per hour.
By improving speed and availability, the system ensured that every customer interaction felt immediate and effortless.

20% Reduction in Support Costs

The automation of CrossML’s AI retail assistant’sframework dramatically lowered operational expenses by minimizing manual intervention.
Routine tasks, such as ticket classification, FAQs, and order status updates, were handled autonomously, allowing staff to focus on higher-value tasks.
With reduced human dependency, the same support volume could be managed by a leaner team without sacrificing service quality.
Over time, this translated into significant cost savings, improved resource utilization, and stronger operational resilience during peak seasons.

18% Increase in Daily Sales

The integration of real-time data and personalized product recommendations led to a measurable uplift in daily transactions. Customers were guided toward relevant products, add-ons, and promotions based on their browsing and purchase history. This intelligent engagement through the AI retail assistant increased purchase confidence and reduced cart abandonment, driving consistent revenue growth. Retail teams also gained new upselling opportunities, as the AI retail assistant recognized patterns and proactively suggested complementary items.

35% Improvement in Query Resolution Rate

Context-aware query routing and AI-powered classification ensured that most customer issues were resolved in the first interaction. The AI retail assistant’s ability to understand tone, intent, and context led to more accurate and relevant responses every time. By reducing the number of repeat tickets, the system streamlined the entire support flow, improving both customer satisfaction and internal productivity. The higher first-contact resolution rate also reduced escalation costs and enhanced the overall perception of service reliability.