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Better customer service that eventually results in long-term engagement and builds stronger relationships with the customers is an asset that every business wants to invest in.

While on the surface, customer support service may seem just about providing appropriate solutions within the stipulated time. There is a lot that happens in the backdrop which facilitates quicker resolutions. For example, support agents may not always be equipped with the information needed to resolve an issue on their own.
  1. They may have to seek approval from seniors to proceed.
  2. Share the ticket with other team members to get details.
  3. Get a confirmation on the solution from peers.
Sometimes, even then, the issue is not completely resolved due to technical or business limitations. At times, missing or inaccurate information in the tickets can also delay the resolution.
While keeping up customer expectations and satisfaction is challenging, an important measure that businesses can take to optimize the resolution time and give appropriate solutions is by having accurate and necessary information in the tickets during creation.
These details mentioned in the tickets play a crucial role in determining: 
  1. who should be assigned the ticket
  2. what action and solution is needed
  3. how long would it take to resolve?
Notes
Note: Currently, Zia supports only English, and we are working on adding more language support soon.

Zia's role in reducing resolution time and improving customer satisfaction

Support agents have multiple responsibilities and perform a spectrum of activities as part of their routine.

These activities take up a considerable amount of time and attention, that makes it challenging for the agents to ensure every detail is included in a ticket especially when they receive high volume of requests from various channels.

Entering accurate details during ticket creation is crucial because based on this information the tickets are segmented, severity and service cost is determined, and SLAs are implemented.

Zia's predictive ability can take the load off from the agents by skillfully analyzing the historical data and predicting the right value for various fields. Its learning, analytical, and reasoning abilities are constantly enhanced and get more accurate over time.

Let's find out how Zia's field predictions can help businesses enhance agent productivity and improve customer satisfaction.

Ticket segmentation and auto assigning them to the right agent

Every customer support team has a dedicated group of people that handle specific issues; this facilitates appropriate resolution within the stipulated time. Issues are often categorized based on:
  1. their severity
  2. problem or product type
  3. support tier
  4. subcategory
Zia can analyze the incoming tickets and predict values for the above fields based on its learning from past tickets that are present in the database. If Zia's prediction matches the accuracy score, then it will auto-update the predicted value in the respective fields.
This will automatically trigger the workflow rule (configured for the fields) and the tickets will be assigned to the respective teams or individuals. It saves time and rules out inappropriate handling of tickets ensuring timely action and quick ticket closure.
Alert
Note that, the workflow will be triggered only if the criteria is set as Create and/or Customer reply.

Identifying customer requirement from the ticket description

Customers raise support tickets to request a service or report an issue. A detailed account of the issue or requirement is usually provided in the ticket description.

Sometimes the subject of the ticket is generic and doesn't provide much information for eg.,"Urgent requirement", "Following-up on subscription issue" or "Transaction inquiry".  

The support agents have to read through the descriptions carefully and identify the exact requirement. This can be time-consuming, especially when there are large volumes of tickets to be attended. Zia can help by analyzing the "subject" and "description" fields in a ticket and suggesting the exact values for fields such as "service requested" or "issue type" and so on.

Understanding field predictions

Zia is Zoho's AI-powered tool that harnesses data to unveil valuable business insights. By analyzing a vast amount of historical data and leveraging algorithms, Zia can identify patterns, detect trends, and make accurate predictions about future outcomes.

Field predictions can help agents by predicting and auto-updating the right values in different picklist fields such as ticket category, priority, issue type, etc. based on its analysis of existing tickets that have similar information in them. It can also predict the ticket owner based on its learning.

Zia can train using the data from the existing tickets in the Desk account and create a pattern, interpreting values for different fields in experimental tickets.
Info
Points to remember
  1. Zia can predict only picklist values. It can predict both system and custom picklist field values.
  2. You must ensure that the picklist fields are added to the default layout because prediction can be executed only in the default layout.
  3. A department must have at least 500 tickets for Zia to train. It is recommended to have at least 500 tickets for each picklist value for Zia to predict effectively.
  4. Zia will predict based on the tickets that are available at the time of configuration. Tickets received after this period must be manually added to the prediction in order to retrain Zia.

Training Zia to analyze tickets and predict values

Zia can predict picklist values and also determine the ticket owner. Once field prediction is enabled in the Desk account, Zia begins its training using the existing tickets. During configuration, as you set the field(s) and the picklist values that you want to predict. Also, you can decide whether Zia should train only using specific tickets.
  1. Training using specific tickets - Consider you want Zia to classify the issue and predict if it's a bug fix, feature request, incident, or data loss, then it's recommended to use specific tickets for training. This will allow Zia to analyze relevant tickets and interpret the values based on the pattern.
  2. Training using all tickets - Likewise, if you want to assign tickets to a particular person and want Zia to predict the owner, then it's best to train Zia using all the tickets so that the training data is vast, versatile, and provides a wider scope to learn, analyze, interpret, and predict.

Determining prediction accuracy

Zia can learn and train using the tickets present in the database. It learns and creates a pattern on about 80% of total tickets and then evaluates its prediction abilities on the remaining tickets. Based on the result, it calculates the probability and an average accuracy score is estimated. The higher the score, the more accurate the prediction will be.    

For example, in the image below, Zia correctly predicted "Sam" as the ticket owner for 484 records, giving a probability of 97%. The average accuracy score is then estimated as 100%.

Best practices to achieve a better accuracy score

Some recommendations that can help improve accuracy score.
  1. Set a criteria to train Zia only on specific tickets if you want explicit predictions. Eg., predict problem type for tickets from the IT sector.
  2. Give a wide range of training data to enhance Zia's learning ability. Eg., predict the product type.
  3. Retrain Zia using the latest tickets to append the prediction.
  4. Set the criteria as created time to train on specific data. Eg., predict issue type based on tickets that were received from January to June.