We 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
to maximize the chances of closing a new opportunity, minimize cycle time and drive sales.
Problem/Objective:
Whenever a new opportunity came up in the CRM, it was assigned to different sales reps. The sales rep was
not aware of the best way to approach the customer company. They were unaware of the way previous
deals were closed with the customer and had limited access to top performing senior sales reps who could
guide them with qualitative suggestions on how best to close a deal.
The client was looking for a recommendation engine that learns from historical sales data and suggests the
best sales reps to talk for a new opportunity based on similar opportunities – deal size, customer and service
sold such that the chances of winning the new opportunity is maximized with optimal cycle time.
Solution:
They approached Intuceo, an enterprise AI and machine learning company, with this problem. On analyzing
the problem, we promised a solution that would meet their needs.
Using Machine learning and clustering algorithms on historical sales data, the recommendation engine was
developed to suggest the top 3 agents to talk for a new opportunity based on deal size, gross profit,
customer and service sold. The sales rep had the choice to assign different weightage to the sales
parameters depending on the business opportunity.
This solution was deployed in a front-end client application (both mobile and web) for sales reps where they
had to select the new opportunity assigned to them, choose the weightage for key parameters and let the
machine do its job of recommending the best reps to talk for the given opportunity.
The application can be connected to your CRM and will have enterprise grade security and access control for
sales reps to see only opportunities assigned to them.
Business Impact
- Engineered attributes for customers whose first-month purchases were similar to the historical transactions and then filtered customers into classes by revenue generated over the two years after their first purchase.
- Provided guideline such as typical cycle time and number of touchpoints for a given opportunity.
- Identified best sales reps who had won similar deals in the past; improved success rates by 10-15%
- Reduction in cycle time by around 20%
If you have a similar problem with your sales teams, contact us here or send an email to Contact us at info@intuceo.com.
with subject “Sales Recommendation Solution”.
