Understanding the importance of Data - Example: Data in Customer Support

Everybody across different domains should be aware of the importance of data in enhancing business operations and decision-making. It is also important to contact an Artificial Intelligence (AI) specialist to evaluate the data and explore the potential of AI-driven solutions that can be implemented. Here an example.

Understanding the Role of Data and Datasets in Customer Support

You may have heard that data is important for building AI systems, but what is data really? Let's have a look at an example to understand this better.

What is Data?

Data is information translated into a format suitable for efficient transmission or processing. For example, if you want to price houses, you can create an Excel spreadsheet with one column for house size (in square meters) and another for the price. To build an AI system for setting house prices, you would use the house size as the input (A) and the price as the output (B), allowing the AI to learn this input-to-output mapping.

Importance of Data in Machine Learning

In the context of machine learning, data is the foundational element. For example, in real estate, beyond just the size of the house, you might also collect data on the number of bedrooms. Here, the inputs (A) are the size of the house and the number of bedrooms, while the output (B) is the price. Thus, the selection of A and B depends on the business use case, making data highly unique to your business needs.

Data in Customer Support

Now, let’s apply this concept to customer support. The goal here is to automate solutions for customers based on the tickets or cases they raise. The products involved are complex, and the responses from customer care agents are equally complicated. Here's how data can be structured for this purpose:

Ticket Metadata

Ticket ID, Customer ID, Product ID, Date and Time, Priority, Status o the ticket.

Customer Information

Customer Name and Email, Customer Location, Customer Type.

Product Information

• Product Name: Name of the product related to the ticket.
• Product Version: Version of the product.
• Product Category: Category of the product.

Ticket Details

• Issue Description: Text description of the issue reported by the customer.
• Product Module: Specific module or feature of the product involved.
• Error Codes: Any error codes reported by the customer.
• Attachments: Any files or screenshots provided by the customer.

Interaction Data

• Agent Responses: Text of the responses provided by customer care agents.
• Solution Provided: Final solution or resolution provided to the customer.
• Response Times: Time taken for each response by the agent.
• Interaction History: Full history of interactions between the customer and the support team.

Classification and Tags

• Issue Type: Classification of the issue (e.g., technical issue, billing issue, feature request).
• Tags: Relevant tags or keywords associated with the ticket.
• Resolution Category: Category of the resolution (e.g., workaround, bug fix, information provided).

Outcome and Feedback

• Resolution Time: Total time taken to resolve the ticket.
• Customer Feedback: Customer satisfaction rating or feedback after ticket resolution.
• Follow-up Required: Indicator if follow-up is needed.

Building the Dataset

Data can be collected in several ways:

1. Manual Labeling: Agents may initially manually tag and classify tickets to build a robust dataset.
2. Customer Support Software: Use CRM and ticket systems like Microsoft Dynamics or Salesforce to collect ticket and interaction data.
3. Surveys: Post-resolution surveys to gather customer feedback.
4. Logs and Analytics: Collect data from system logs and usage analytics to capture error codes and other relevant product data.

Data Acquisition Methods

• Manual Labeling: Manually label each data point with the appropriate category or tag.
• User Behavior: Observe user behaviors, such as ticket submission and interaction patterns.
• Partner Data: Collaborate with partners who may already have relevant datasets.
• Public Datasets: Utilize publicly available datasets, keeping in mind licensing and copyright constraints.

Avoiding Common Pitfalls

While data is crucial, there are common misuses to avoid:

• Delayed AI Integration: Waiting to build a perfect dataset before starting AI development can be a disadvantage. Instead, start using the data you have and iterative improve it based on feedback and new data.
• Data Misinterpretation: Simply having large amounts of data doesn’t guarantee valuable insights. Ensure the data collected is relevant and properly interpreted.
• Data Quality: Garbage in, garbage out. Ensure data quality by handling incorrect labels, missing values, and ensuring data consistency.

Conclusion

Data is the backbone of AI systems, also in customer support automation. By carefully collecting, processing, and analyzing data, businesses can create robust AI systems that enhance customer satisfaction and operational efficiency. Whether it's structured data like ticket metadata or unstructured data like issue descriptions, a well-created dataset is the first step towards successful AI deployment. Always remember to integrate AI feedback early and focus on data quality to achieve the best outcomes.

see less