Lead to Win: Recipes for Inside Sales Success

• Main Author: Ohiomah, Alhassan
• Other Authors: Benyoucef, Morad
• Format: Others
• Language: en
• Published: Université d'Ottawa / University of Ottawa 2020
• Subjects: Sales Success; Lead Conversion; Machine Learning; Inside sales; PLS-SEM; Meta-analysis
• Online Access: http://hdl.handle.net/10393/41490; http://dx.doi.org/10.20381/ruor-25714

The role of sales has evolved over the last decade, mostly driven by technological and social changes. One major shift is the growth in importance and in size of the inside sales function. Inside sales are sales that are conducted remotely or virtually using one or more Information Technology (IT) tools without the traditional in-person interaction. Despite the growing importance of inside sales, studies that show the key determinants and practices that can improve inside sales success are rare and have tended to focus on determinants affecting individual salesperson performance. Moreover, existing approaches to customer acquisition in inside sales often rely on subjective intuition, expert suggestions and gut feeling, which often hurts the chances of qualifying and converting leads to sales, and eventually diminishes inside sales success. This research aims to address such shortcomings by: 1) investigating the key dimensions and determinants of inside sales success by summarizing the existing body of knowledge using a systematic review and meta-analysis; and 2) develop comprehensible lead conversion models that integrate the interplay of relevant determinants in the lead conversion process. This doctoral research aims to: (1) Synthesize the literature and draw a big picture of B2B sales success by providing practitioners and scholars with a comprehensive state-of-the-art understanding of the determinants of B2B sales success and their significance in inside sales. (2) Identify and validate several key lead engagement factors crucial for inside sales success. (3) Demonstrate the potential of data-driven analytics by collecting multiple historical datasets from several companies representing different industries. This includes investigating and discovering new insights that will improve lead conversion outcomes, which contributes to the literature on the ongoing discussion of effective sales pipeline management. (4) Provide sales practitioners with comprehensible lead conversion models that integrate industry specific behavior and performance of salespeople, characteristics of leads and/or prospects, and workflow strategy aspects. This will help sales practitioners to better understand problems and opportunities in lead management, take proactive actions, and improve their sales success.