Artificial Intelligence Boosts Top Hat's Sales Engagement and ROI
Technology Category
- Infrastructure as a Service (IaaS) - Cloud Databases
- Platform as a Service (PaaS) - Application Development Platforms
Applicable Industries
- Education
Applicable Functions
- Sales & Marketing
Use Cases
- Smart Campus
- Time Sensitive Networking
About The Customer
Top Hat is an interactive, cloud-based teaching platform that enables professors to engage students inside and outside the classroom with compelling content, tools, and activities. The platform is used by millions of students at 700 leading North American colleges and universities. Launched in 2010, Top Hat aims to improve learning and increase graduation rates. The cloud-based software allows educators to build innovative, must-attend courses while supplementing their material with educational resources from the Top Hat Marketplace.
The Challenge
Top Hat, a cloud-based teaching platform, was facing a significant challenge in managing and responding to the large backlog of inbound leads in their database. The company was receiving thousands of leads every month from multiple channels, and it was the responsibility of the sales reps to manage these leads as they came in. However, due to the high volume of leads and the limited number of sales reps, it was not always possible to promptly follow up on these leads. Furthermore, the marketing team, which was small in size, found it nearly impossible to track the progress of each sales rep's inbound leads. The leads were assigned to a sales rep based on the location of the school at which they taught, but sometimes the rep was unable to follow up right away due to being on campus, meeting professors, or on vacation. This resulted in the leads sitting idle and the list of inbound leads growing continuously.
The Solution
To address this challenge, Top Hat turned to Conversica, an AI Automated Sales Assistant with unlimited bandwidth. They launched their Conversica AI Sales Assistant, named Lauren McKinnon, on one channel at a time to test her effectiveness before a marketing-wide roll-out. Lauren was tasked with following up on inbound leads generated through organic search first, and then additional channels were added to the mix. When the marketing Business Development Representative (BDR) team was unable to establish contact via phone or email, which happened approximately 50% of the time, these leads were handed off to Lauren to reach out to. This AI-powered process made the monthly average of 5,000 inbound leads much more manageable for the sales team. The transparency of interactions between Lauren and Top Hat’s leads was also valued, as the sales reps could see that Lauren was working in the background tying up loose ends whenever necessary.
Operational Impact
Quantitative Benefit
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