AI-powered B2B Sales Cycle Optimization
The ability to access and leverage vast amounts of customer information in real time is key to productive business-to-business (B2B) relationships. Traditional B2B sales cycles that rely on disparate data sources and CRM channels present the risk of missed sales opportunities and underperforming client engagement.
KPMG’s Artificial Intelligence (AI)-powered Sales Intelligence Engine addresses this long- standing challenge. With Azure-based technology for its back-end foundation, KPMG Sales Intelligence Engine integrates internal and external data sources and leverages machine learning engines to provide multidimensional customer views, meeting guidance such as “next best steps” and recommended stakeholder contacts, as well as up-to-date talking points related to that client’s business practices. The ability to harness trusted data to make informed relationship decisions drives value, reduces cost, and enhances the sales process overall.
Sales cycle assistance through AI
KPMG Sales Intelligence Engine acts as a sales enablement tool to support three primary functions: targeting and lead generation, guided selling through personalized recommendations, and integrated digital/ mobile assistance. They can be enabled individually or as a combined capability.
Potential Sales Intelligence Engine advantages
Improved customer experience throughout asset management, marketing, and sales life cycle for all B2B sales functions in all industries.
Automated information management
With KPMG Sales Intelligence Engine, the sales cycle is digitally transformed through automated information management systems, removing the need for manual compilation of information sources. Internal and external data points are collected and integrated into the B2B sales cycle via Microsoft big data and advanced analytic technologies. Meeting feedback, outcomes, and sentiments are brought into the system automatically, providing valuable insights for future interactions and overall relationship health. This rich information base is the foundation for data-driven recommendations.
Data-driven cognitive guidance
The machine learning algorithms rely on vast computing power to mine data, find historical patterns, and make predictions. Different models are trained and tested to answer questions such as what is the ideal approach for in-person meetings, what product to recommend to each client, and how to tailor topics on a meeting agenda.
Intuitive user experience design
Built to accommodate the demanding and hectic schedules of sales representatives, the iOS app user interface is designed to be easy to use and intuitive. The information can be accessed through both touch screen and voice control chatbots. Chatbots allow users to communicate and interact with the application in natural language, which helps tailor pragmatic, targeted responses and recommendations based on voice notes.
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