Ebsta Forecasting vs Next Quarter

Ebsta Forecasting helps sales teams examine deal risk, understand forecast changes and submit forecasts supported by deal evidence. Revenue Intelligence includes projected revenue, forecast roll-ups, trends, pacing, submissions and a waterfall chart. Pipeline views add trends, benchmarks, deal scores, positive and negative deal factors, and funnel analytics. Ebsta analyzes pipeline factors to help managers assess the likelihood that forecast deals will close. The product lists deal risk alerts, AI forecasting, recommended actions and forecast customization. Salesforce and HubSpot are CRM integrations; the pricing comparison also lists Gmail, Outlook/Office 365, Zoom, Teams and Slack integration features. The product runs on the web and as an extension. The vendor offers a 14-day free trial with unlimited access to Conversation Intelligence, Relationship Intelligence and Revenue Intelligence. Paid pricing is available on request. Ebsta was founded in 2012 and is headquartered in London, UK. The vendor says the platform and data are hosted on AWS US-East, and that it is ISO 27001 compliant with encryption in transit and at rest.

Next Quarter is an applied AI platform for B2B revenue teams in Sales, Strategic Accounts, Marketing, and Revenue Operations. Its Q-Pilot combines internal account context with external market signals to help teams prioritize opportunities, engage stakeholders, and select next actions. Shared context can include CRM records, documents, meeting notes, product usage, company news, filings, leadership changes, hiring, intent, and technology signals. Recommendations present a proposed move with a stakeholder, an angle, and supporting evidence. Next Quarter lists five products: KAM Q, Sales Q, ABM Q, Events Q, and Fin Q. Workflows cover account research, opportunity discovery, stakeholder intelligence, meeting preparation, personalized outreach, sales playbooks, pitch decks, and strategic account planning. CRM data can include opportunities, contacts, products, account history, and deal stages. Depending on deployment, APIs and MCP connectivity are supported. Customer data is not used to train shared or third-party foundation models, and customer contexts are isolated by permissions. Pricing is on request, with no public per-seat list price.