About the Company.
A US-based health and wellness testing provider, offering a wide range of diagnostic products and services. The client focuses on customer-centric growth and relies heavily on proactive outreach and personalized engagement to drive repeat purchases.
Solution Summary.
To support their sales acceleration goals, the client partnered with Algoscale to unify data across two CRM platforms (NetSuite and Close.io), automate real-time synchronization, and implement a sales forecasting engine. The solution enabled seamless operations, reduced data fragmentation, and delivered actionable sales insights through advanced analytics and predictive modeling.
Customer Challenges.
The client struggled to achieve timely customer engagement due to disconnected CRM systems and unreliable sales forecasting. This complexity led to data duplication, hindered visibility, and inconsistent outreach strategies - ultimately impacting conversion rates and sales growth.
Algoscale Solution.
Algoscale delivered a seamless data integration and forecasting solution using modern ETLM orchestration, and machine learning tools. The system ensured CRM synchronization, intelligent forecasting, and dashboard-based insights, all built to scale with growing customer data and complexity.
Data Integration with Apache Airflow.
Orchestrated secure and fault-tolerant ETL workflows using Apache Aiflow, ensuring continuous and incremental sync of leads, opportunities, and order data from both CRMs.Real-Time CRM Sync with Django & PHP.
Engineered lightweight services using Django and PHP to establish two-way sync between NetSuite and Close.io . This enabled real-time consistency across systems, eliminating duplication and version mismatches,Backend Interoperability.
Integrated seamlessly with the client’s existing backend APIs and data services, supporting both REST and SOAP endpoints for flexible ingestion and write-back compatibility.ML-Driven Forecasting in BigQuery.
Processed unified data in Google BigQuery, applying regression models to forecast sales trends based on enriched variables like transaction history, segment attributes, and customer engagement scores,Modular Data Enrichment.
Extended core CRM fields using third-party data ingestion modules, supporting market tags, behavior flags, and category classifiers- used as model features in forecasting.Forecast Visibility Dashboard.
Built interactive views powered by BigQuery + Looker Studio, surfacing real-time sales predictions, anomalies, and trend deviations to business teams with minimal latency.
Algoscale Differentiators.
Plug-and-Play Data Sync Framework.
Our integration architecture was designed to be modular and CRM-agnostic, enabling future expansion to other platforms like Salesforce or Hubspot with minimal rework,Real-Time Consistency Without Manual Sync Jobs.
Unlike traditional ETL that runs in batches, our real-time microservice-based sync keeps both CRMs aligned continuously without lag-reducing manual reconciliation by 90%.Forecasting with Domain-Specific Feature Engineering.
Our ML approach involved custom feature pipelines built for healthcare and wellness sales behavior- ensuring domain relevant insights, not just generic predictions,Zero-Disruption Deployment.
The solution was containerized and deployed with zero downtime into the client’s Azure environment, using isolated environments for testing, staging, and production.Built for Scale and Extensibility.
With modular Airflow DAGs and a metadata-driven architecture, the system can ingest millions of records and onboard new data streams with configuration-only changes.
