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Worldwide Salesforce Customers

$13 Billion

Global Salesforce Services Market Size in 2022


Compound Annual Growth Rate (CAGR) Market for Salesforce Services



TensorIoT enhanced GRAX's data strategy with a predictive ML model on AWS, transforming their extensive Salesforce (SFDC) data into actionable forecasts for opportunity outcomes.


How can we transform our customer's extensive Salesforce data into accurate predictive forecasts?


The integration of the machine learning model yielded a marked improvement in forecasting accuracy. GRAX now leverages detailed predictive insights, enabling strategic, data-driven decision-making for sales opportunity management.


GRAX, a leader in SaaS data backup and management serving sectors from hospitality to manufacturing, required an innovative approach to leverage their extensive Salesforce data. Their platform needed to predict Salesforce opportunity outcomes to enhance strategic decision-making. Integrating machine learning into their existing infrastructure was crucial to unlocking the predictive potential of their historical data.

The Challenge

The primary challenge was the effective utilization of GRAX's vast Salesforce data to forecast sales opportunity close dates. The complexity of Salesforce data structures demanded a sophisticated solution that could process and analyze large volumes of data while adapting to the dynamic nature of sales forecasting. GRAX needed a predictive model that could integrate seamlessly with their Salesforce application and provide accurate, actionable insights.

The Solution

TensorIoT's solution was a comprehensive machine learning pipeline, built on AWS, to process and analyze GRAX's Salesforce data. The pipeline included data preprocessing, feature extraction, and model training components, designed for robustness and scalability. AWS services like Lambda, S3, and SageMaker created a flexible architecture that could easily accommodate updates and expansions. This end-to-end solution provided GRAX with tools to predict outcomes and understand the underlying factors driving those predictions.

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