Assigning cases are a common phenomenon in service centers. However, should you wish to implement a more efficient and streamlined workflow to this process, then Artificial Intelligence is the way forward.
On a typical day, an agent receives a myriad of customer tickets. Unfortunately, it is not always possible to manually extract, search, or sort these cases at once. And this calls for more profound, machine-assisted analysis, which can automate the process.
Dispatcher AI, a functionality in ThinkOwl Helpdesk, employs advanced AI capabilities to classify incoming cases to the correct categories and displays the performance of its classifications. The salient functionalities of Dispatcher AI are:
- It continuously learns from experts how the cases are handled, and triggers required actions by automatically classifying new cases into the relevant category.
- Helps the system categorize cases as per the content of the message.
- Provides a report about auto-categorization, so that, users can decide which categories require more training and which auto-categorization has worked well.
- Users can easily train the system, even with large training sets.
Training Data to Teach AI Models
Machine Learning algorithms rigorously learn from data. The same applies to ThinkOwl’s Dispatcher AI.
The quality and quantity of training data directly correlate with the overall success of intelligent automation, i.e., categorizing customer cases. They develop understanding, find relationships, make proper decisions, and evaluate the overall performance depending upon the quality of data the system is fed with.
For example, when customers write in about their issues, machine learning algorithms analyze the content of their messages in real-time. Consider you received a support request email with this description: “Hey, I’m Alex. I have ordered a smartwatch that surpassed the due date of delivery. Do let me know the status of its shipment.”
In such a scenario, the AI comes into play which intercepts the intent of the message and accordingly tags the case in the subsequent categories, such as “shipping” or “delivery” or “smartwatch”. Obviously, to make it happen, users need to train the AI models with relevant data sets.
AI-powered case categorization depends on the data you are collecting in your tickets. Utilize or add custom fields to collect the exact information, like capturing data about your customers, product, or issues. Agents use these fields to indicate the problem for each ticket and accordingly train the AI models, which thereby helps to categorize tickets easily.
Also Read: How ThinkOwl Delivers a Superior Customer Experience with Guided Mode
Furthermore, you can route all the tickets tagged with these categories to someone from the service team designated to handle such cases at large. And, having these cases auto-categorized in real-time, helps to:
- Save time
- Facilitate prompt resolution, and
- Result in customer and employee satisfaction.
Simply put, after an adequate training cycle, the AI module populates meaningful results on the overall case categorization process. It means that even before you open a case, it is already assigned a category by the Dispatcher AI, intelligent fields auto-extracted relevant data, and helpful knowledge articles and intelligent responses are already suggested for you.
As a system administrator, you can use the Dispatcher AI to get a 360-degree view of category classification and see which case categories need improvement. You can also evaluate the quality and status of AI training for your desk.
Analyze the Performance of Case Classification
ThinkOwl’s Dispatcher AI comes with a Summary Page. It displays the accuracy of automatic classification across all categories. In fact, you can also review whether cases have been correctly classified and accordingly reclassify them as needed.
The Summary page displays the following:
- The latest trained date, time, and the last usage time, i.e., when was the last case correctly predicted.
- The average accuracy of category classifications on a timely basis – last 24 hours, last 7 days, or last 30 days – with various indicators that signify an upward trend, downward trend, or no trend change.
- The count of unclassified cases in the Inbox where you can optimize those and manually classify cases into correct categories to improve AI performance.
- The amount of time saved by Dispatcher AI in the last 24 hours or the last 30 days.
- The health of the training data, which signifies the state of AI learning.
Optimize Category Predictions
In ThinkOwl, system administrators can optimize category predictions by reviewing and classifying the unclassified cases from the Inbox. They can select from the list of available categories to attribute relevant cases to that category. Also, users can create a new category for a case.
The overall progress and session history of category optimization is displayed, which includes:
- the number of cases accepted; that are correctly predicted.
- the number of cases ignored; that are incorrectly predicted or skipped.
It helps the user to analyze, evaluate, and take the necessary actions.
Verifying the cases is required to attribute tickets to the correct category or remove them from incorrect categories. There are instances where categories are assigned by the Dispatcher AI or manually by an agent. But category assignment for such cases is not checked by admins and thus needs verification.
Likewise, some cases are categorized correctly, which are listed and marked as verified.
Another instance could be like cases that have no category, but the Dispatcher AI can suggest one. Also, there might be few cases for which the dispatcher cannot indicate any category. These cases will remain in the Inbox. However, for such cases, it is advised that you assign them the correct category so that the AI can learn more effectively.
Note: Dispatcher AI records the accepted cases as correct predictions and rejected cases as incorrect predictions. The rejected cases are not a part of the training data.
Also Read: 10 Essential Customer Service KPIs You Need To Track
One of the greatest attributes of Dispatcher AI is its ability to process and categorize a mountain of customer cases in a way that is beyond the limits of the human brain.
The mechanism constantly uses historical data to improve the quality of decision-making and implies:
- Automatic skill matrix detection
- Automatic case assignment process backed by AI
- Factor agent availability or their previous experience with similar cases
Leverage the power of Dispatcher AI to classify cases with precision. And build a service process that is streamlined, automated, and devoid of workload. Explore more on ThinkOwl's Dispatcher AI.
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