Every project.
One business lens.

Customer feedback existed across channels, but leaders needed one view that explained which stores, products, suppliers, and recurring issues were moving the customer experience.

Unified weekly, PTD, and YTD NPS with high- and low-performing stores, raw customer comments, AI summaries, product intelligence, supplier intelligence, and regional comparisons in one executive workflow.

Customer issues and agent activity were trapped in the service UI, limiting historical analysis, workforce planning, and visibility into the problems customers were raising most often.

Integrated Freshdesk through Fivetran, orchestrated dbt models across a Snowflake medallion architecture, and added AI categorization to surface issue themes, service demand, and agent performance.

Marketing teams needed to see more than campaign activity—they needed to understand how investment influenced digital behavior, in-store purchases, customer growth, product choice, and retention.

Automated week-over-week performance reporting, built a Snowflake-to-Meta offline conversion feed, and connected customer listening, product promotion, cross-selling, pricing, and retention signals to marketing decisions.

Product and pricing decisions were being made without a complete historical view of SAP prices or a shared connection between customer feedback, repeat purchase, supplier performance, and store execution.

Reverse-engineered pricing history since inception, joined it with product NPS, price and quality flags, supplier signals, repeat-rate behavior, one-time-seller risk, competitive pricing, and planogram stability.

Operating cost was visible in aggregate, but the product-level choices creating margin pressure were difficult to isolate and act on.

Connected cost-to-serve, assortment, packaging, and sales-volume signals into one decision view, then tested scenarios with commercial and operations partners.

Expanding frozen delivery required reliable store assignment without paying for unnecessary drive-time calculations on every possible route.

Used ZIP geometry to reduce the search area first, then applied selective distance-matrix calls only to viable stores before final assignment.

Leaders were navigating fragmented reports and inconsistent definitions when they needed one clear view of business performance.

Standardized business logic, prioritized decision-ready KPIs, and designed a connected reporting experience for executive and operating teams.

Over-ordering created cost and storage pressure, while under-ordering put frozen fulfillment at risk across stores with different demand patterns.

Combined historical demand with operating context and store-level overrides so the forecast could work in real planning conversations.

Commercial and finance teams needed faster, more consistent profitability visibility across two markets and many source systems.

Unified the inputs behind P&L performance, aligned definitions with stakeholders, and delivered self-service views for repeatable analysis.

The business had rich transaction data but no practical way to translate purchase relationships into merchandising action.

Mapped high-value product relationships and turned them into recommendations teams could use in placement, offers, and assortment decisions.

Broad customer messaging limited relevance and made it harder to differentiate pricing, loyalty, and promotional treatment.

Built interpretable segments, profiled their commercial value, and translated them into targeting and loyalty actions with clear owners.

Millions of invoice rows made recurring overcharges difficult to detect, validate, recover, and prevent.

Created repeatable controls around surcharge patterns, shipment attributes, geography, and service performance, then packaged evidence for recovery conversations.

Core reporting depended on fragmented feeds and repeated manual work, weakening trust and slowing executive response.

Established reusable data products, quality checks, and delivery rhythms around the business questions that leadership needed answered most often.

Legacy flows limited data availability, scalability, and the speed at which teams could answer new business questions.

Mapped dependencies, rebuilt the transformation path in the cloud, and kept reporting continuity at the center of the migration plan.