System development to aid salespeople throughout the sales process

• Grant number: 22/11042-5
• Support Opportunities: Research Grants - Innovative Research in Small Business - PIPE
• Start date: February 01, 2023
• End date: November 30, 2023
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computer Systems
• Principal Investigator: Gustavo José Querino de Vasconcelos
• Grantee: Gustavo José Querino de Vasconcelos
• Company: Nupedido Soluções Digitais Ltda
• CNAE: Desenvolvimento e licenciamento de programas de computador customizáveis
• City: Campinas
• Pesquisadores principais: Gabriel Schubert Ruiz Costa
• Associated scholarship(s): 23/02978-0 - System development to aid salespeople throughout the sales process, BP.PIPE

Abstract

More than 90% of B2B companies have switched to a virtual or hybrid sales model as a result of COVID-19, according to a recent McKinsey report. This shift illustrates a growing trend of converting field sales to inside sales. However, without the proper assistance, inside sales reps can be overwhelmed due to the large number of customers they have to manage and the quantity of product catalogs they have to keep track of. Each customer has their own buying cycles and patterns, some of which may have changed since the start of COVID-19. Sales are often lost when reps don't account for variations between these profiles. In this sense, the objective of this research project is the development of tools to support internal sales representatives using artificial intelligence to make predictions about products and the ideal moment of purchase for customers based on information from previous interactions. To make accurate predictions about what customers want, the recommendation engine will use order history, customer profiles, location, time period, and intent data. Inside sales reps equipped with this innovation will be able to close more deals due to greater assertiveness in suggestions, and also increase average order values through upsells, cross-sells and data-driven complementary recommendations. (AU)