Customer Company Size
Large Corporate
Region
- America
Country
- United States
Product
- AI-powered virtual assistants
Tech Stack
- Conversational AI
- Deep Learning Algorithms
- ServiceNow
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Productivity Improvements
- Digital Expertise
Technology Category
- Application Infrastructure & Middleware - API Integration & Management
Applicable Functions
- Human Resources
- Procurement
Use Cases
- Chatbots
Services
- Software Design & Engineering Services
About The Customer
The customer is an American data management company that supports its customers with hybrid cloud data storage. The company has been ranked in the Fortune 500 list since 2012, indicating its significant presence and influence in the industry. With over 10,000 staff worldwide, the company operates on a global scale, serving a diverse range of clients and industries. The company's annual revenue is around $6.2 billion, further highlighting its substantial size and financial capacity. The company's services are critical in the modern digital age, where effective and efficient data management is key to business success.
The Challenge
The company provides its global workforce an internal common portal that allows them to access products and services to perform their day-to-day job functions. The main HR webpage, owned and maintained by the HR team, is accessed through this common portal. Finding, accessing, and updating information through this portal was cumbersome. Both employees and HR staff struggled to provide and get services through this portal. The company wanted an innovative, self-serve yet scalable solution to serve its employees for HR or procurement-related queries. It wanted to free up the support staff time to focus on important issues while improving employee experience overall.
The Solution
Seeking advice from a technology analyst firm, the company decided to develop AI-powered virtual assistants for various use cases. They began with the HR department to develop use cases for the HR virtual assistant solution to provide employees with a self-serve option for routine queries. The company built the HR FAQ bot and encouraged employees and HR staff to access information for the appraisal system called “Thrive”. The bot was trained to answer about 80 questions in the first deployment. The bot also used deep learning algorithms to improve its intent recognition over a period of time. After gaining confidence from the first rollout, the enterprise added more FAQs to cover talent acquisition, benefits & compensation, questions on the internal server, other generic HR queries, and diversity training, besides the Thrive program. The HR FAQ bot is deployed on the web channel and is accessible via Microsoft Teams - a messaging channel as well. It sits on the company’s website on the HR webpage. It is also integrated with ServiceNow ticketing system. If employees don’t get adequate responses from the bot, they can raise a ticket on ServiceNow through the bot itself. This not only provides a resolution for employees but also enables the team to update the bot with new intents periodically and improve the containment rate, which sits at 92% currently.
Operational Impact
Quantitative Benefit
Case Study missing?
Start adding your own!
Register with your work email and create a new case study profile for your business.
Related Case Studies.
Case Study
HSBC's Transition to Conversational Banking through Intelligent Automation
HSBC, one of the world's largest banking and financial services organizations, was facing a challenge with its customer service operations. With over 19,000 customer service agents, the bank was dealing with a high volume of repetitive tasks that put pressure on its agents. The traditional career path in the contact center world was also leading to inevitable attrition, as it was defined as agent → team manager → department manager → operations manager → head of contact centre. This lack of opportunity as the field narrows held back the chance of reaching the highest possible customer satisfaction with every interaction. Furthermore, HSBC was planning to shift towards Conversational Banking, which was expected to grow interactions considerably and require conversational experts to manage the chatbots.
Case Study
Tronergy's Transition to Alibaba Cloud for Enhanced Scalability and Performance
Tronergy, a company that provides creative IT solutions to small and medium-sized businesses, faced a challenge with its clients who were accustomed to using traditional web hosting services. As these clients' projects evolved, they found it difficult to dynamically and automatically increase bandwidth or improve performance using traditional web hosting solutions. Additionally, these clients often required the integration of several other business solutions to quickly customize their projects, such as adding text messaging (SMS), optical character recognition (OCR), email push, and chatbot features. Building a new solution from scratch was not a viable option due to the high time and cost implications.
Case Study
SAP's Accelerated Deal Closure through Process Automation
SAP was grappling with the challenge of managing an increasing number of cloud deals. The rise in configurable cloud component deals necessitated a reduction in manual activity and a scaling up and automation of processes for order management, master data maintenance, and financial reporting. The high volume of cloud deals required a swift and efficient response, which was difficult to achieve with the existing manual processes. The challenge was to automate these processes to meet the growing demand and improve efficiency.
Case Study
7-Eleven Philippines Enhances Customer Support with Freshdesk
7-Eleven Philippines, a pioneer of 24-hour convenience stores in the country, was facing challenges with its customer support system. The customer support was outsourced to an external vendor, which resulted in limited visibility into customer query patterns and performance metrics. The company had no transparency into the vendor's analytics and could not validate their reports. They also had no visibility into the volume of calls, number of abandoned calls, etc. due to the use of the vendor’s telephony solution. This lack of control and visibility into customer support led the team to decide to bring the entire function in-house. They began exploring helpdesk solutions that would enable them to build a strong in-house support team.
Case Study
MISA's Success in Achieving 50% Query Deflection Rate with Freshdesk
MISA, an online fashion retailer, faced a significant challenge in managing customer support and communication during the COVID-19 pandemic. The shift in consumer behavior towards online shopping led to an explosive growth in digital footfall, increasing MISA’s average number of orders from 30-50 a day to about 70-100 a day. This surge in orders resulted in higher support volumes, with customers frequently enquiring about their orders and delivery status. The team struggled to prioritize issues as their existing email systems ordered conversations based on the latest response rather than the urgency of the queries. Additionally, the shift to remote work due to the pandemic raised concerns about effective team collaboration and communication. MISA also faced the challenge of dealing with repetitive customer queries, which were time-consuming and redundant.
Case Study
Transforming Customer Service in Financial Services with AI Chatbots
The customer, a leading US financial services company and one of the largest credit card providers in the world, was facing challenges with their existing customer service model. They had over 8000 customer service agents handling queries on phone and digital channels, but their first foray into digital customer service in 2006, which involved live chat support on their website, was not as effective as they had hoped. Customers had to wait in a chat queue and could not switch tabs or leave the page without missing their turn and having to start over. The company wanted to improve this experience by adopting a more intuitive and familiar flow, similar to popular messaging applications like iMessage and WhatsApp. They also wanted to maintain the context of past issues the customer has faced to provide a more personalized experience.