Scaling insights: A business intelligence solution for franchise ecosystems

Data silos cost companies up to $15M a year, according to Gartner, and franchise operators - where 54% of all franchises are multi-unit operations - feel this acutely: a disconnect between POS and accounting systems drives manual intervention, delays insight, and increases operational costs and financial losses. Zemoso worked with the client's CTO and CPO to align on the business problem, the user problem, and the solution quickly and iteratively, then build and launch a data unification and business intelligence platform for franchise operators.

The customer started this journey with “Connect the Dots”, a unique program designed for CPOs and innovation leaders to visualize the product's end success state in two weeks with a design prototype. After that, the product and engineering team delivered a product roadmap for minimum viable product (MVP), architecture decision records (ADRs), and a technology proof of concept (POC) in about six weeks. Our customer started early testing with prospects and partners using the design and tech POCs. As a result, they quickly garnered business confidence and traction, moving into MVP deployment and expansion.

• An intuitive frontend: One of the core requirements was a solution anyone could use, even someone with no technical or only partial business knowledge. The intuitive front-end was built with React, with Zustand for state management and Material UI and Tailwind CSS for styling. Detailed dashboards surface near real-time insights across 30+ KPIs, giving site and operational managers visibility over transactions, labor costs, labor cost per hour, and more, with data that can be sliced and analyzed by location, time, and other factors. Plotly generates the graphs, while Amazon Redshift and Python run the analytics workflow.

• A robust ETL workflow: Whether data comes from POS or accounting systems via an API or batch transfers from a database, an automated rules engine analyzes, maps, and organizes it for cohesiveness within the platform. Apache Airflow handles the entire extract, transform, load (ETL) workflow, with scheduled jobs syncing data between client and server systems and a built-in backfill process pulling historical data. A Python-based rules engine classifies transactions, such as identifying new membership sales.

• Data management and syncing: Amazon Redshift stores and manages the sales and accounting data. It provides a powerful tool to run aggregation queries and scales easily. We usedThe multi-tenant storage pattern, specifically the bridge model, stores each customer's data in individual schemas within a single database, centralizing the ETL workflow and ensuring data privacy without duplicating resources. PostgreSQL manages users and operational data, while Amazon SQS and Apache Kafka enable real-time synchronization: SQS provides flexibility for timely POS updates and Kafka handles event-driven, continuous data flow, with sync schedules tailored to customer needs and defaulting to once per hour.

• The fundamentals: Data integrations and workflows sometimes depended on specific automations that enabled batch processing and such, and we built Sentry into the platform to ensure alerts for job failures were caught. Karate and Postman automate API testing, and Cypress handles UI testing. GitHub Actions are used for Continuous Integration/Continuous Development (CI/CD). We used Elastic Beanstalk for APIs for easy deployment and management. FastAPI bridges the backend and frontend, streamlining data flow.

P.S. We have strict Non-disclosure agreements (NDAs) with many of our clients. The data, insights, and capabilities discussed in this case study have been anonymized to protect our client’s identity and don’t include any proprietary information.

By unifying POS and accounting data into a single, real-time analytics layer with an automated ETL pipeline, Zemoso helped franchise operators cut manual reconciliation work dramatically and turn scattered transaction data into fast, near-real-time insight for site and operational managers.