{"id":8439,"date":"2025-01-11T18:36:20","date_gmt":"2025-01-11T18:36:20","guid":{"rendered":"https:\/\/intuceo.com\/?post_type=case-study&p=8439"},"modified":"2026-09-24T12:10:54","modified_gmt":"2026-09-24T12:10:54","slug":"how-to-empower-large-sales-teams-to-drive-more-sales","status":"publish","type":"case-study","link":"https:\/\/puffertech.com\/clients\/intuceo\/use-cases\/how-to-empower-large-sales-teams-to-drive-more-sales\/","title":{"rendered":"How to empower large sales teams to drive more sales by minimizing cycle time"},"content":{"rendered":"\t\t
\n\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\tWe worked with a major software company who sold different enterprise products and managed services to its clients. The sales team had more than 500 sales agents pursuing around 2-3 opportunities who wanted\nto maximize the chances of closing a new opportunity, minimize cycle time and drive sales.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
\n\t\t\t\t\t
\n\t\t
\n\t\t\t\t
\n\t\t\t\t\t

Problem\/Objective:\n<\/h3>\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\tWhenever a new opportunity came up in the CRM, it was assigned to different sales reps. The sales rep was\nnot aware of the best way to approach the customer company. They were unaware of the way previous\ndeals were closed with the customer and had limited access to top performing senior sales reps who could\nguide them with qualitative suggestions on how best to close a deal.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\tThe client was looking for a recommendation engine that learns from historical sales data and suggests the\nbest sales reps to talk for a new opportunity based on similar opportunities – deal size, customer and service\nsold such that the chances of winning the new opportunity is maximized with optimal cycle time. \t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
\n\t\t\t\t
\n\t\t\t\t\t

Solution:<\/h3>\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\tThey approached Intuceo, an enterprise AI and machine learning company, with this problem. On analyzing\nthe problem, we promised a solution that would meet their needs.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\tUsing Machine learning and clustering algorithms on historical sales data, the recommendation engine was\ndeveloped to suggest the top 3 agents to talk for a new opportunity based on deal size, gross profit,\ncustomer and service sold. The sales rep had the choice to assign different weightage to the sales\nparameters depending on the business opportunity.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\tThis solution was deployed in a front-end client application (both mobile and web) for sales reps where they\nhad to select the new opportunity assigned to them, choose the weightage for key parameters and let the\nmachine do its job of recommending the best reps to talk for the given opportunity.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\tThe application can be connected to your CRM and will have enterprise grade security and access control for\nsales reps to see only opportunities assigned to them.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
\n\t\t\t\t\t
\n\t\t
\n\t\t\t\t
\n\t\t\t\t\t

Business Impact<\/h3>\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t\t\t\t