The project involved working with the business SMEs to understand the data and customer usage behavior, support, cases, and external business drivers.
- Sales conversion ratio is less than 20%
- Pipeline quality is less than optimal
- High sales cost as a % of revenue and stagnant revenue over the past few years
- Mirketa built the Salesforce Einstein Prediction model for predicting customer churn for SaaS company.
- Analyze more than 35 variables from CRM and customer data to understand the DNA of winning deals.
- We created derived data to be used for modeling and created AI models with 90+ confidence score to predict the likelihood of a customer leaving beforehand.
Sales reps now deprioritize deals that have low probability. Sales processes are being enforced using next steps recommendation
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