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07 Dec
11:00 - 11.40 am EST | 05:00 - 05:40 pm CET
Our Latest Talk
By Kanchan Ray, Chaitali Sengupta
60 mins
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success story

Creating intelligent automotive sales paradigms

Machine learning and analytics-driven lead management

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challenge

As the number of leads from multiple online and offline sources rose, the client wanted an easy and seamless system to capture, manage, and convert leads. They wanted to ensure that the sales agents could reach high priority leads without any delays. The client sought insights to create tailored follow-up strategies for different customer groups having unique user journeys and provide end-to-end lead visibility to all stakeholders. 

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solution

A comprehensive assessment report revealed the gaps in the existing systems and the client's data maturity. State-of-the-art dashboards helped visualize the real-time status of leads, reasons for lost sales, time taken to close deals, and other crucial KPIs. We sorted the data stored in multiple systems, combined, cleaned, and enriched it with third-party datasets to create a data warehouse. The team developed machine learning models to accurately predict the conversion probability of the leads and aid sales representatives to prioritize and personalize their follow-up strategy.

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outcome

With lead scoring, prioritization, and segmentation-based ML models, client has access to data-driven insights to better act on leads resulting in improved operational efficiencies and better lead conversion. Predictive, prescriptive, and descriptive dashboards help client with comprehensive visibility on leads-to-sales journey. Based on the outcomes of lead management initiative and the client’s advanced analytics roadmap, we are further planning to refine and develop ML models to achieve higher lead quality and conversions.