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Case Study

What if generative AI could automate customer service inquiries while reducing costs, response times and escalations, and improve CSAT scores?

CLIENT

African Training and Innovation Incubator

Industry

General AI

LOCATION

Africa

Automating Customer Service with GenAI for a Fast-Growing Ed-Tech Company

According to the World Economic Forum’s Future of Jobs Report, technology will change the core skills of 44% of workers in the next five years. Those who reskill will be best prepared for new opportunities.

In Africa, a leading technology training incubator and innovator ecosystem is doing its part to meet the global economy’s changing needs and shape a new generation of tech leaders across the continent. Many savvy learners seeking opportunities to gain new skills have turned to this dynamic company.

As a result, the organization scaled rapidly, with five times the expected increase of interested learners in the pipeline — from one million to five million from 2023 to 2024. This growth came with a similar increase in the expected number of support tickets. As they scaled up, the company needed to ensure all learners had a good customer service experience, regardless of location. To achieve this, they focused on improving their contact center operations. This strategy was essential to reduce the manual effort required while effectively addressing customer needs and providing valuable insights to the organization.

Challenges to Overcome

  • The company needed a comprehensive and unified approach to improve the escalation process and contact center/learner support.
  • Teams required the ability to track data and share insights with other teams to improve the talent experience and reduce the number of queries. 
  • The company wanted to analyze daily data to support product/process challenges, see the initiatives’ impact and enable experimentation to drive change.

Solving Problems with AI and Data

To efficiently support its customers at scale, the company partnered with Sand Technologies. Within approximately eight months, the team leveraged generative AI to create a Learner Experience Assistant (LEA). The solution included automation to resolve repetitive inquiries. With LEA handling most routine inquiries, the existing team could handle the queries that needed human resolution.

In addition, the team built an ongoing training and learning program to create a customer-centric mindset within the organization. New tools and ways of working captured key customer insights, and the system allowed experimentation and identification of recommendations to improve the customer experience.

Results

  • Efficient data capture, tracking and reporting provided actionable insights to the organization.
  • Elevating contact center issues in strategic discussions reduced inquiries by 70% and improved customer experience team effectiveness.
  • The AI tool resolved 80%+ queries without manual intervention, creating a solid foundation for further scaling.
  • Escalations requiring human intervention reduced from 30% last year to 2-4% with the new model.
  • The average wait time for ticket resolution went from 24 to under 6 hours.
  • Self-service for 188,000 community inquiries in the first three months translated into potential savings of $120K annually.
  • CSAT scores improved by 13% from the previous year, showing the effectiveness of the AI-driven model. For AI-resolved tickets, CSAT was 98%.

Looking Ahead

The team is working with Sand Technologies to develop a new tool that can provide deeper insights into the types of queries coming in. A highly granular view of all inquiries can provide real-time insights to improve the customer experience and identify potential emerging demand for new technical skills.

CSAT scores improved by 13% from the previous year, showcasing the effectiveness of the AI-driven model.

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