Zia offers suggestions for requests that are created through email, web form, preventive maintenance tasks, and V3 API. With adequate training, Zia can be advanced to auto-apply certain predictions.

To access the request prediction features,

• Go to Setup > Zia > Artificial Intelligence > Predictive Features.

• Select Requests from the drop-down.

• Enable the required feature.

Template Prediction  

Suggests the relevant request template based on the subject and description when a request is created, edited, or its type is converted into an incident or service.

Prerequisite

View the prediction accuracy by clicking the Prediction Rate on the Template Prediction card.

Category Prediction  

Suggests the top three relevant categories based on the request's subject and description when a request is created or edited.

Prerequisite

After Zia is trained properly, click and enable Auto Apply Prediction to auto-apply the predicted category when requests are created.

Subcategory Prediction  

Suggests the top three subcategories based on the subject, description, and category when a request is created or edited.

Prerequisite

Item Prediction  

For requests with category and sub category, Zia provides suggestions for items.

The top three items are suggested based on the request's subject, description, category, and subcategory. Suggestions are shown when a request is created or edited.

Prerequisite

Priority Prediction  

Suggests the top three priorities based on the request's subject, description, impact, and urgency when a request is created or edited.

Prerequisite

After Zia is trained properly, click and enable Auto Apply Prediction to auto-apply the predicted priority when a request is created.

Group Prediction  

Zia analyzes request data and assigns the relevant technician group to the request.

During training, Zia analyzes the interdependent relationships between the technician group assigned to a request and the request details such as subject, description, and category. Zia then uses this relationship history to allocate the right technician group to incoming requests.

Prerequisite

After successful training, Zia suggests the top three groups based on the request's subject and description. Suggestions are shown when a request is created, edited, or when assigning technicians or groups from the right pane.

After Zia is trained properly, click and enable Auto Apply Prediction to auto-apply the group when a request is created.

Technician Prediction  

Zia analyzes request data and assigns a technician with relevant skills to the request.

During training, Zia analyzes interdependent relationships between the technician assigned to a request and the request details such as subject, description, group, and category. Zia then uses the relationship history to allocate the right technician to incoming requests.

Prerequisite

After successful training,

• Zia suggests the top three technicians who are potentially the right fit to handle the request when requests are created or edited.

• Zia's suggestions will be listed in the technician field drop-down in the Add/Edit Request form and in the right pane.

• Suggestions are provided dynamically based on the request's subject, description, category, and group.

You can track the source of technician assignments in the request history.

While suggesting technicians, technician availability will be calculated using the Due by Time of the request, by default. If technician auto-assign is enabled, the configured technician availability model will be considered.

Auto-apply Predicted Technicians

You can enable Zia to assign the predicted technicians to the request.

Go to Setup > Automation > Technician Auto Assign and choose Artificial Intelligence (Zia) as the technician auto-assign model.

When a request is created, Zia will assign the relevant technician to the request instantly. You can find the details in the request history.

When Zia is unable to make suggestions, the Load Balancing technique will become the fallback model in allocating the technician to the request. However, if you disable technician prediction on the Zia Artificial Intelligence page, the technician auto-assign model will automatically switch to Round Robin.

Problem Prediction  

Use Problem Prediction to monitor incident requests and detect emerging trends or spikes in similar incidents over a short span of time. Receive alerts on potential problems before they escalate and take necessary actions and maintain seamless operations.

 Currently, problem prediction is supported only in English language setups.  

Enable Problem Prediction

• Go to Setup > Zia > Artificial Intelligence > Predictive Features.

• On the Problem Prediction card, use the toggle button to enable or disable the feature.

• On enabling the feature, both the Manual and Automatic modes of problem prediction will be enabled.

• You will be directed to configure problem auto prediction.

Alternatively, after enabling the problem prediction, you can click Configure on the card to set up or edit the configuration.

Configure Problem Auto Prediction

Incoming Request Threshold: Set a threshold for requests after which a problem identification should be triggered. Choose a threshold between 25 and 250. For instance, if you set the threshold to 200 requests, the application will start to look for potential problems when 200 incident requests are received. The application will also suggest the recommended threshold based on request inflow in the last two months.

Technicians to Notify: Select the technicians to be notified when a potential problem is identified. Only technicians with edit permissions for problems and requests will be listed. You can select a maximum of 100 technicians.

Click Save.

Run Problem Predictions Manually

After enabling the problem prediction, click the Quick Actions icon and select the preferred customer. The following options are displayed in the Quick Actions header menu: Run Prediction and View Predictions.  

Run Prediction: Use this option to initiate problem prediction manually. Once you initiate this, the system will analyze the last 500 requests for any patterns and trends and send alerts on potential problems. To start the prediction, click Run Prediction and choose the required request filter. For instance, you can choose the My Pending Requests filter to predict problems from requests and click Run.

After the prediction is completed, you will receive a bell notification. Clicking it will take you to the predicted problems list view.

 When a user initiates Run Prediction, only the particular user will be notified of the potential problems. 

If no potential problems are detected, the following screen will be shown.

View Prediction: Lists all the problem predictions performed by the system. Click a prediction to view its details.

Requests predicted with potential problems can be associated with existing problems or with a new problem.

 Predictions will be displayed for a maximum of 48 hours. 

Sentiment Analysis  

The Zia Sentiment Analysis feature examines request conversations to determine whether the emotion expressed is positive, negative, or neutral.

Zia analyzes the first 2000 characters of a requester's conversations and places appropriate emojis. Then, the overall sentiment score is calculated and displayed in the right panel of the request details page.

Uses of Sentiment Analysis

This feature enables technicians to:

• Prioritize requests.

• Send personalized responses.

• Assess user satisfaction.

Enable Zia Sentiment Analysis

• Go to Setup > Zia > Artificial Intelligence.

• Enable Sentiment Prediction.

View Analysis Details: Shows the number of conversations and sentiments predicted by Zia, along with the overall sentiment score. The score will be displayed even if the sentiment prediction is disabled.

The sentiment score is calculated using sentiment points.

Sentiment Points

Positive - 1

Negative - 0

Neutral - 0.5

Formula to calculate sentiment score

Overall_sentiment_score = [(positive_sentiment_count + (neutral_sentiment_count*0.5)]/total_sentiment_count) * 100

Sentiment Score

0%-30% - Dissatisfied -

31%-60% - Neutral -

61%-100% - Satisfied -

You can view the overall sentiment score and the sentiment of the recent conversation from the right pane of the request details page. Hover over the overall score to view the emotion of each conversation.