Aviso’s AI-powered Compass is designed to help sales and go-to-market teams find their revenue True North. Enhancements to Aviso’s AI-Guided Selling platform aim to provide mission critical deal insights, drive accurate forecasting and unify deal execution.
The Aviso platform uses auto machine learning to independently assess the probability that a deal will close based on modeling the attributes of comparable past deals won or lost. With auto machine learning, Aviso’s compass is designed to help managers direct reps so they do not waste time chasing bad deals.
Aviso’s technology scores each opportunity and forecasts the probability of each deal closing within the quarter. It also recommends specific deals to focus on and highlights opportunities that are at risk, which can help leaders reallocate resources to pursue the most critical opportunities. Aviso analytics are designed to forecast what deals will close within the first three weeks of the quarter with more than 95% accuracy, according to the company.
The Aviso solution is designed for sales reps, sales managers, operations & planning leaders, sales enablement and chief revenue and financial officers at both Fortune 500 companies and mid and SMBs. Aviso works across industries, including high tech, financial services, telecom, pharma, healthcare and transportation.
Aviso integrates with CRM systems (e.g. Salesforce, Microsoft Dynamics) and multiple data sources, including calendars, customer success and support systems, data lakes, conversational data and any other systems with APIs.
Aviso uses a Software-as-a-Service delivery model with annual and multi-year pricing options. Pricing starts at around $75 per user, per month, with consumption-based pricing options. Aviso is hosted on AWS.
Aviso customers include Dell, Honeywell, Splunk, MongoDB, Apptus, RingCentral and FireEye.
Designed to provide a forecasting solution and tool that offers accurate scoring, Aviso is a true revenue compass that unifies forecasting and deal execution with falsifiable WinScores, insights and actions on a time-series database that works across disparate data sources in the revenue cycle.
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