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  • Writer's pictureAna Gomez

The Value of Data in Support Operations

Customer support teams are responsible for a variety of tasks, such as creating help documentation, providing feedback to product teams, and answering customer questions. While these activities have a significant impact on the business, they may not be immediately clear to others. A vital way to demonstrate this impact is by using customer support data. Data can provide a universal language to communicate the value of customer support to those who are not directly involved in day-to-day operations.

In this article, we will discuss different types of data to collect, common sources of this data, relevant metrics to consider, and effective ways to present this data to make a strong impact.

Quantitative vs Qualitative

There are two main types of customer support data: quantitative and qualitative. Quantitative data can be expressed as numbers and is often used to create charts and graphs. Qualitative data, on the other hand, cannot be quantified and is typically concerned with a sentiment.

For example, a customer satisfaction survey that asks customers to rate their experience on a scale of 1-10 would provide quantitative data. The additional comments that customers may leave on the survey would provide qualitative data. In customer support, a customer satisfaction (CSAT) score is an example of quantitative data, while the comments on a CSAT survey are an example of qualitative data. It's vital that your reports include both quantitative and qualitative data.

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Collecting customer support data has several benefits for customer support teams. First, data can be easily presented to a larger group to help them understand the team's activities and contributions. Data can provide insight into the team's performance and identify areas for improvement. This can be used to set performance goals and measure success. Furthermore, data can help support teams gain approval for additional resources and tools from senior leadership.

Sources for data

Help desk software

  • Volume of conversations

  • Busiest times

  • Busiest channels

  • Performance metrics (e.g. first reply time, average handle time)

  • Average resolution time

  • First contact resolution rate

  • Number of tickets resolved per day/week/month

  • Time to close tickets

  • Number of tickets reopened by customers

  • Customer satisfaction ratings

  • Agent productivity metrics, such as number of tickets handled and response time

  • Channel-specific metrics, such as the number of tickets received via email, phone, chat, or social media.

Knowledge base software

  • Search queries: The search terms and phrases that customers use when searching for information in the knowledge base can provide insight into their specific questions and pain points.

  • Article views: The number of views for each article can indicate which topics are most relevant and important to customers.

  • Time on page: The amount of time customers spend on each article can indicate the complexity and usefulness of the content.

  • Feedback and ratings: Customers can provide feedback on individual articles or the knowledge base as a whole, which can be used to improve the content and user experience.

  • Clickstream data: This tracks the pages and links that customers click on when using the knowledge base, and can reveal patterns in customer behavior and usage.

Online reviews

  • Overall satisfaction ratings

  • Ratings for specific aspects of the product or service (e.g. ease of use, customer support, value for money)

  • Comments and feedback on specific features or aspects of the product or service

  • User demographics (e.g. age, location, occupation)

  • Sentiment analysis of the reviews (i.e. whether they are positive, negative, or neutral in tone)

  • Comparison with competitor products or services

  • Trends over time in terms of the number and tone of reviews

Survey responses

  • Customer satisfaction scores

  • Net Promoter Scores (NPS)

  • Customer Effort Scores (CES)

  • Demographic information, such as age, gender, location, etc.

  • Feedback on specific products or services

  • Feedback on overall customer experience

  • Suggestions for improvement or new features

  • Pain points or areas of frustration

  • Customer loyalty and retention rates

  • Likelihood to recommend the product or service to others

  • Comparison with competitors

  • Usage and adoption rates of products or services.

To use customer support data effectively:

  • Choose the right metrics that reflect the customer experience and provide insights into areas for improvement

  • Avoid "vanity metrics" that may sound good but do not provide meaningful insights

  • Present data in a clear and meaningful way, such as through dashboards or graphs, to make it easily digestible and impactful.

Making the most of your data

To make the most of customer support data, it is important to tie this information back to larger company goals. This can help to demonstrate the impact of customer support on the business as a whole, and make the data more relevant to people outside of the customer support team. When presenting customer support data, focus on how it relates to revenue growth and cost reduction, and present the information in a way that shows its impact on the company as a whole. This can help to make the data more meaningful and persuasive. When presenting customer support data to different audiences, it is important to tailor the information to the specific needs of the audience. This may involve including or excluding certain metrics, and presenting the data in a way that is relevant and meaningful to the audience. For example, while a support agent may need detailed, weekly performance metrics, a CEO may only be interested in broader, long-term trends. By tailoring the data to the specific needs of the audience, it is more likely that the information will be understood and valued.


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