From data to solutions

More efficiency and satisfaction in customer service with BI

Application areas of BI in customer support

The use of business intelligence in customer support opens up a wide range of opportunities to significantly improve the quality and efficiency of customer service, e.g. in the following areas:

1. Customer satisfaction analysis

Customer satisfaction can be accurately measured and analyzed by analyzing feedback and survey data so that trends and patterns can be easily identified.
For example, BI can show which service areas are most frequently criticized or which factors contribute particularly to customer satisfaction.
These findings help companies to make targeted improvements.

2. Increased efficiency in the support team

Another area of application for BI in customer support is analyzing the efficiency of the support team. BI tools can create detailed reports on the processing times of inquiries, the frequency of escalations or the number of open tickets. By analyzing this data, bottlenecks and inefficient processes can be identified. This way, measures can be taken to process customer inquiries faster and with better quality.

3. Prediction of customer behavior

By analyzing historical data and recognizing patterns, customer behaviour can also be predicted. By using predictive analytics, companies can identify trends in behavior in advance and prepare for them. For example, it can be determined when certain products or services cause more problems in certain seasons, which leads to an increase in support requests. This means that problems can be avoided in the future.

4. Personnel planning

At the same time, BI can be used to optimize staff planning in customer support. By analyzing historical data on support requests and their processing times, companies can predict staffing requirements more accurately, anticipate peak times for requests, and plan better. This ensures that there are always enough qualified employees available to process requests promptly and effectively, without overloading or unused resources.

5. Identification of training needs

By analyzing support data, such as frequent escalations or recurring errors, it is also possible to identify where there is still a need for training in the support team.

6. Personalization of customer service

Analyzing of individual customer data can also be used to personalize customer service to a greater extent. For example, specific needs and preferences can be derived, such as preferred communication channels or frequently asked questions. This can be used to tailor the service to the needs of the individual customer, resulting in an improved customer experience and higher satisfaction.

7. Optimization of the service channels

By analyzing data from the various service channels (telephone, email, chat), it is also possible to determine which channels are most effective and which may need improvement.
Optimizing the service channels also improves the customer experience with the company and its products.

8. Improvement of product development through support data

In addition, insights gained by analyzing customer service data can be used for product development. For example, analyzing support data can show which products or functions regularly cause problems or which customer requests are repeatedly expressed. This information can then be fed directly into product development or improvement so that products are better tailored to the needs of customers. This not only reduces future support requests, but also increases overall customer satisfaction.

Data sources for BI in customer support

Companies collect data from a wide variety of sources to carry out well-founded analyses. The following data sources are particularly important for customer service:

• Customer, sales and transaction data, e.g. from CRM systems
  CRM (Customer Relationship Management) systems are central data repositories that contain all the information about a company’s interactions with its customers.
  This data includes customer details such as contact details, previous purchases, subscriptions, contract renewals, interaction history and preferred communication channels.
  BI software can use this data to create customer profiles and analyze trends in customer interactions.
  In addition, correlations between purchasing behavior and support needs can be analyzed, for example if there is an increase in support requests after a product launch.
• Support tickets and case management systems
  Support tickets and case management systems record all inquiries, complaints, and problems reported to support by customers. These systems store detailed information about each ticket, including the date of the request, the processing time, the employees involved and the resolution details. BI systems can analyze this ticket data to identify patterns in the requests, such as common problems, bottlenecks or recurring technical difficulties.
• Customer feedback and survey data
  Customer feedback is often collected through various channels, including post-interaction surveys, reviews and direct feedback.
  This data provides insights into customer satisfaction and their opinion of the service they experienced.
  BI tools collate and analyze this information in real time to provide immediate insights into service quality and measure customer satisfaction.
• Social media and external reviews
  Social media and external review platforms are important sources of data, as they usually contain unfiltered and often spontaneous feedback from customers.
  Customers use these channels to publicly share their opinions and experiences, whether positive or negative.
  By analyzing this data in BI systems, customer sentiment and public opinion can be evaluated, for example using text mining techniques.
  For example, frequently mentioned problems and frequent praise can be filtered out.
• Interaction data from communication channels
  Communication channels such as email, telephone, live chat and chatbots generate a wealth of data about the interactions between customers and the support team.
  This data includes, for example, the duration of the conversation, response times, content of the communication or the success rate of problem solutions.
  This data can be used to evaluate the efficiency and effectiveness of the various communication channels.
  For example, it can be determined if customer queries are resolved faster via chat than by email, which could indicate that chat support should be expanded further.
  Voice recordings from telephone calls can also be analyzed to identify frequent problems or monitor the quality of support.
• Web analytics and usage data
  Web analytics data contains information about the behavior of users on a company’s website.
  This includes page views, click paths, abandonment rates and the use of self-service offerings such as FAQs or knowledge bases.
  By analyzing this usage data, companies can identify how effective their online support offerings are and where there may be a need for optimization.
  For example, if many users visit a support page but leave without a solution, this indicates that the information provided may be inadequate.
  This enables companies to improve their self-service offerings.
• Product usage data and IoT
  Product usage data comes from devices that customers use or from Internet-of-Things (IoT) systems.
  This data provides information about how customers use products, including the frequency, duration and type of use.
  From this, conclusions can be drawn about when and how often customers encounter difficulties, which in turn can be used to improve the user experience and reduce support costs.

Conclusion

The use of business intelligence in customer support gives companies the opportunity to optimize their service. The targeted analysis of customer data can not only increase the efficiency of support teams but also sustainably increase customer satisfaction. BI offers a wide range of applications, from predicting customer behaviour to personalizing customer service, and makes it possible to make well-founded decisions that both contribute to improved service quality and can be used for product development.
To fully exploit these advantages, the BI software myPARM BI^act BI software offers a comprehensive solution that can be seamlessly integrated into existing systems. With myPARM BI^act companies can combine data from different sources and present it in user-friendly dashboards. This makes it easier to make data-based decisions that directly contribute to improving customer service. myPARM BI^act enables companies to respond efficiently to their customers’ needs and continuously increase satisfaction.

Would you like to get to know myPARM BI^act in a demo presentation? Then make an appointment with us right away!