Ecommerce & D2C

Use Case / Problem Statement

Manual spreadsheet-based pricing and periodic competitor checks were too slow for thousands of SKUs.

• Pricing lagged competitor moves by days
• No price elasticity analysis
• Cross-elasticity effects unmodeled
• Demand-supply dynamics ignored
• Pricing team spent 70% of time gathering data

Solution & Development

• Competitor benchmarking with real-time scraping.
• Price elasticity modeling per SKU and category.
• Cross-elasticity and portfolio optimization.
• Demand-supply price prediction system.
• Pricing API + Power BI dashboards with guardrails.

Use Case / Problem Statement

Catalogues were bloated and gaps were identified anecdotally rather than systematically.

• No competitor assortment benchmarking
• Vendor intelligence siloed
• Product tagging inconsistent
• Basket analysis infrequent
• BI dashboards disconnected from decisions

Solution & Development

• Competitor universe analysis to identify white space.
• Vendor intelligence layer for diversification.
• ML-based product tagging system.
• Basket mix analysis for cross-sell and bundling.
• Real-time data streams into BI with proactive alerts.

Use Case / Problem Statement

Static reorder points and manual procurement created stockouts and dead stock.

• Reorder points ignored seasonality and promotions
• Manual procurement scheduling inconsistent
• No multi-supplier fallback modeling
• Waste and sustainability not factored
• Dashboards showed issues without automation

Solution & Development

• Predictive demand forecasting with real-time correction.
• Dynamic reorder thresholds and safety stock optimization.
• Vendor and lead-time optimization across suppliers.
• Real-time alerts integrated with BI.
• Scenario planning for seasonal peaks and disruptions.
• Sustainability and waste reduction signals built in.

Use Case / Problem Statement

Markdown decisions were reactive and based on gut feel, destroying margin without guaranteeing clearance.

• No model for optimal markdown depth
• Timing was reactive after aging
• Channel allocation manual
• No recovery rate visibility

Solution & Development

• Predictive clearance demand modeling.
• Proactive liquidation candidate identification.
• Markdown optimization per SKU.
• Channel selection intelligence for clearance.
• Scenario simulation for recovery outcomes.
• Recovery tracking with real-time alerts.

Use Case / Problem Statement

BI teams were overwhelmed by ad hoc requests, while business teams lacked self-serve insights.

• 65% of BI time consumed by repetitive queries
• Business teams couldn’t self-serve
• Dashboards couldn’t answer “why”
• Data discovery slow and manual
• Industry KPIs not pre-wired

Solution & Development

• Multi-agent BI architecture (DGFE + MKI + Core BI).
• Natural language interface with context-aware follow-ups.
• Domain-tuned KPI reasoning beyond text-to-SQL.
• Instant visualization per query.
• API layer for embedding into tools and workflows.

Use Case / Problem Statement

Cross-channel attribution was broken, and budgets were allocated by habit rather than performance.

• Last-click attribution overstated paid search
• No dynamic budget reallocation
• Static audience segmentation
• Performance issues found late
• Manual post-campaign analysis

Solution & Development

• Unified cross-channel performance dashboard.
• AI-driven budget allocation recommendations.
• ML-based audience segmentation and personalization.
• Real-time performance monitoring with anomaly detection.
• Mix Media Modeling for true attribution.
• Automated post-campaign analytics.

Use Case / Problem Statement

Fraud losses scaled with volume while manual review and static rules produced high false positives.

• Fraud losses grew with order volume
• Manual review unable to scale
• Static rules caused false positives and negatives
• Promo abuse and return fraud rising

Solution & Development

• Behavioral anomaly detection models.
• Network graph analysis for fraud rings.
• Promo abuse detection and identity linkage.
• Return fraud scoring with risk thresholds.
• Sub-100ms real-time scoring API.
• Case management dashboard with full context.

Use Case / Problem Statement

Broadcast campaigns treated all customers the same, eroding margin and missing churn signals.

• No RFM-based segmentation
• LTV not measured or tracked
• Churn detected only after it happened
• Campaign personalization manual and infrequent
• WhatsApp/email campaigns broadcast-only

Solution & Development

• RFM + behavioral segmentation with dynamic membership.
• LTV prediction and channel attribution modeling.
• Churn risk scoring with proactive retention triggers.
• Automated campaign workflows via n8n.
• Cohort and segment migration analytics.