Queries Resolved by Chat
First Response Time Reduced
Agent Transfer, sales, sales support, multilingual support
In the realm of transportation, managing support calls from distributors and transport personnel poses a formidable challenge. McKinsey Global Institute’s report underscores the tremendous growth anticipated for India’s transportation and logistics industry, projected to surge at a CAGR of over 10% – catapulting from $200 billion in early 2020 to an estimated $320 billion by 2025. This surge, propelled by technological advancements and government initiatives such as the ‘Gati Shakti’ plan, has reinvigorated the logistics sector in India, albeit not without its share of heightened challenges.
However, amidst these obstacles lies a beacon of promise – conversational AI. Its potential benefits for the manufacturing sector are profound. It facilitates multi-disciplinary automation, bolsters maintenance efficiency, optimises inventory management, and automates order processing. This transformative potential found resonance with a prominent player in the Indian transport landscape. Let’s delve into how they harnessed the power of conversational AI to surmount these challenges and usher in a new era of operational efficiency.
WheelsEye, headquartered in Gurugram, Haryana, India, stands as a pioneering logistics-tech startup. Collaborating with an extensive network of businesses across India, they share a collective vision of constructing a dependable, safer, and more predictable transportation infrastructure. WheelsEye provides user-friendly, feature-rich applications tailored to streamline everyday business operations. Their suite of products imbues precision, efficiency, and scales up business triumphs. By optimising fleet operations, propelling business expansion, and elevating safety and security standards, WheelsEye empowers its customers.
At the heart of their offering is a SaaS Platform, empowering large enterprises to make data-driven decisions through actionable insights. This platform is fortified with GPS and Fast-Tag solutions, further enhancing its functionality.
WheelsEye was established in 2017 by Anshul Mimani and Manish Somani.
Challenge – Lack of Streamline Support for Customers and Transport Agents
WheelsEye confronted a formidable challenge: an influx of approximately 5000 to 5500 calls on a daily basis. Managing this sheer volume of incoming queries proved to be an arduous task. To address this, they had implemented a 24/7 call support system, employing a workforce of 120 agents.
Each agent was tasked with handling an overwhelming load of 55 to 60 calls every day. This intensive manual effort, while valiant, was straining the operational capacities of the team.
Solution- An Automated Customer Support System Launched
By leveraging Verloop.io, WheelsEye successfully integrated a conversational AI support system into their mobile application. They commenced with a pilot version, engaging 10,000 users in an initial experiment. Encouraged by the positive response, they swiftly expanded the initiative to include 50,000 users. Presently, this innovative support system is now partially operational, catering to the needs of a staggering 3 lakh users.
The chat interface is adept at addressing a wide array of incoming queries, particularly those related to GPS and Fastag functionalities. Additionally, it accommodates any other queries that users may have. These queries are intelligently directed to the appropriate departmental agent, ensuring a swift and efficient resolution.
To further enhance user accessibility, this solution is available in two languages:
- English and
This bilingual capability ensures that WheelsEye can effectively cater to a diverse user base, accommodating their preferences and linguistic comfort.
Support Calls Reduced
Since implementing automated conversation support, WheelsEye has seen a remarkable reduction in support calls. They now receive an average of 600-700 chats daily, with only 6 dedicated agents handling the incoming queries.
Agent Efficiency Improved
Before the introduction of conversational AI support, agents could resolve approximately 60 calls. With the new chat interface, agents now efficiently handle 70-75 queries. This enhanced efficiency has significantly reduced the number of agents required for the workload. For instance, a task that once necessitated 10 agents can now be effectively managed by only 7.
Bot Deflection Rate
Automated customer support agents have not only saved time but also greatly improved query resolution rates. Over 30% of the queries are now successfully resolved without any human intervention. This notable increase in resolved queries per day is a testament to the effectiveness of the solution.
First Response Time Reduced
With the bot taking the lead in responding to user queries, the first response time has been impressively reduced to a mere 6 seconds.
Support Cost Reduction
With fewer agents needed to handle inquiries, WheelsEye has seen a notable reduction in support costs, estimating a decrease of around 5%. This translates to significant savings in operational expenses, allowing for more efficient resource allocation.
These results showcase the substantial positive impact that the implementation of conversational AI support has had on WheelsEye’s operations and customer support efficiency.
“The USP of Verloop.IO is not only the product features but also the customer service. It’s one of the best. – Aakash Aggrawal, Product Manager, WheelsEye