D2L support agents use Agatha Assist to pull relevant past tickets, articles, and templates (macros) in the sidedock app to quickly answer incoming tickets, leveraging the collective knowledge of the team.
Desire2Learn (D2L) is a cloud-based online and blended classroom learning software used by schools, higher education, and businesses across the globe.
D2L wanted to help their support agents be more efficient while still delighting customers. They wanted to reduce ticket handle time and increase the average number of cases their agents closed in an hour. However, they did not want to risk lowering their high customer satisfaction (CSAT) score.
D2L’s support process mirrored that of many support teams: with agents needing to toggle between many windows and tabs to find help articles, past tickets, or other resources to help them resolve customer support cases.
With unstructured data sources and different mappings for each source, D2L struggled to find a partner who could work with their current data and knowledge base. They also ran into a problem with the tools they used. The tools they relied on to search through their knowledge sources relied heavily on keyword search, which often returned a long list of irrelevant search results.
Ultimately, agents were spending more time looking for information than answering customer tickets.
With Agatha Assist, D2L didn’t have to choose between reducing ticket time and CSAT.
D2L support agents use Agatha Assist, which sits in a sidebar app right in their helpdesk, to help resolve customer issues as they come in. Implementing Agatha did not require the team to do any of the indexing and updating of the team’s data.
When an agent opens a new case in their helpdesk, Agatha automatically pulls relevant past cases, knowledge articles, and macros relevant to the new case for the agent to use as reference, leveraging the collective knowledge of the entire support team.
As it turned out for D2L, oftentimes, the answer on a past case is exactly the right response for new cases with the same concern. Using Agatha’s “Add to Reply” button, agents can add a past response to a new response with a single click.
Agatha also helps agents who are often encouraged to reference help articles in their responses to customers. Since Agatha pulls relevant help articles to a case immediately when the agent opens it, the agent can easily add the help article into the response as well.
Having access to information quickly helped maintain customer satisfaction and met D2L’s goals.
With over 90% accuracy, Agatha significantly outperforms other AI. Agatha uses Natural Language Understanding (NLU), which means that she understands language way better than any bot ever could.
Forethought’s Agatha helped D2L index their data and learned from D2L’s unique data, pulling relevant information for agents while answering cases.
D2Ls customer support manager even found that agents who leverage Agatha in every one of their interactions are 3.5 times more likely to meet their weekly goals.
Agents who are in the habit of leveraging Agatha on every one of their interactions are 3.5 times more likely to meet their weekly efficiency goals.
With Agatha at their agents’ fingertips, D2L’s end user support team:
Furthermore, D2L found that those support agents who used Agatha were significantly more likely to hit their ticket quotas compared to the support agents who did not use Agatha.
Not only did Agatha exceed our expectations in these areas, but Forethought has also become an important partner of ours as we continue to look for new ways to innovate the highest quality support for our clients.
Using Agatha’s cutting edge machine learning, D2L was able to identify gaps in their knowledge center so they could provide the right resources to help agents tackle the most common support issues in less time.
Next, D2L wants to leverage Forethought’s spam detection model to help them close spam cases, allowing agents to spend more time helping customers in need.