How to use data and analytics in your documentation team

Introduction

Producing good-quality technical documentation is a skill that is mastered by talented technical content writers. The technical content writers shall know the customer’s needs before composing a technical article. Understanding customer needs is very tricky when you are composing new articles for yet-to-be-released product features for a public knowledge. However, for an internal knowledge base, it is easy to understand the customer needs as consultation can happen to gather internal customer requirements.

Understanding customer needs can be done through feedback received from the customer support team and customer success team as they manage customer relationships. Once there is clarity on customer needs, it becomes easy to produce technical documentation to meet them. However, the customer needs change over time. How do you keep up with your changing customer needs?

Why do we need data analytics?

Say “hello” to data-driven decision-making. We need to collect data to understand the customer intent and published article performance. The articles need to be monitored to improvising their quality over time such that articles are always living up to changing customer expectations. Data helps to perform analytics to derive key metrics for decision-making. The documentation team shall embrace the data-driven culture and have the mindset to use evidence to substantiate their opinions. The data provides a scientific approach to problem-solving and empowers the documentation team to make the right decision at the right time.

Data-powered documentation team also can derive business metrics to quantify the business value proposition of their efforts. This includes customer outcome metrics such as

• Reduction in customer support tickets
• Reduction in customer support phone calls
• Increase in customer satisfaction
• Increase in knowledge retention

What kind of data are needed?

The documentation team needs data that is fit for the purpose and helps them to make the right decision. These datasets can be grouped into themes such as

• Product usage data
• Customer support tickets
• Google Analytics
• Feature requests
• Customer feedback

Product usage data

This dataset includes how customers are using the software product/ tool; This includes product feature usage data, customer usage behavioural data, customer recurring steps, and habitual data. This data is very rich in understanding the pain points customer are facing in configuring the product features. This data also unearths how customers are using the product features to solve their business problems. Understanding the product features and usage trends based on different customer personas can help technical content writers to form a good narrative and design the content structure accordingly.

Customer support tickets

The customer support tickets are raised as the customer wishes to resolve an issue relating to the software product. The qualitative data from the customer support team helps technical content writers to write appropriate troubleshooting guides. Understanding general themes in customer support tickets help technical writers to prioritize their effort and focus on recurring issues. The volume of customer ticket data over time shows the efficacy of technical writers’ effort to solve customer issues via self-service knowledge base.

Google Analytics

Google analytics provides a plethora of insights into how customers are navigating your knowledge base to find the information they seek for. It also provides rich analytics into customer demographics, behaviour, engagement, and traffic referrals. Technical writers need to understand how customers are consuming the content and help to optimize content. The other tools, such as hotjar provides powerful insights on how intuitive your content design is.

Feature requests

The number of product feature request raised by customers provides actionable insights into addressing untapped customer value. Technical writers may need to tweak the existing documentation article such that product features can be explained more intuitively from customer perspective. The technical writer’s team can exhibit empathy towards customer needs based on these feature requests.

Customer feedback

The customer feedbacks are very focused on improvising the documentation quality. These feedback datasets are authentic and help content writers to author content better. The likes/dislikes data, along with feedback, helps with the continual improvement of documentation quality.

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Also read: Data Analytics Skills for Technical Writers

What metrics are required for decision-making?

Since we have a plethora of data, what kind of metrics we need to build? The three principles for creating good metrics for data-driven decision making are

• Intuitive
• Interpretable
• Actionable

Based on the above principles, a few metrics for the documentation team are

1. Number of customer support tickets deflected

One of the objectives of the documentation team is to produce documentation that helps customers to self-serve. Thus, monitoring the number of the customer support ticket over time and how documentation helps to reduce customer support ticket is a good metrics. The documentation team manager can own the Key Performance Metrics to gauge the effectiveness of the documentation in deflecting customer support tickets.

2. Ratio of views/reads

The ratio of the number of views and reads of a specific documentation theme article gives an indication on utilization of those articles. For frequently utilized articles, this ratio should be ideally one; For articles that are rarely used, this will be greater than one. We need to focus our attention on keeping this ratio between 1 and 10.

3. Number of customer feedback and its sentiments

The number of customer feedback should be normalized over time and kept within a range. This range differs based on your product domain, customer feedback frequency, and product market conditions. The count of likes, dislikes, and their respective ratios are good measures. If customer feedback increases, it is a sign that your documentation quality needs to improve.

4. Bounce rate and engagement time

If the bounce rate for each article needs to be optimized to serve your customer needs. Ideally, the engagement time should be between 2 – 3 minutes as time is a scarce resource!

5. Readability score

The readability score should be kept optimal to ensure that content is easily readable and interpretable. Content writers need to spend time in simplifying things for customers to consume content.

How to empower your documentation team to be data-driven?

Empowering your documentation team to be data-driven changes the mindset of your technical writers. The data literacy of the technical writers shall be enhanced through various training and upskilling programs. The data culture helps the documentation team to focus efforts to maximise customer outcomes and enhance the product experience.

Conclusion

Data and analytics play a vital role in helping to improvise technical content quality. The measures and key performance metrics provide a scientific approach to decision-making among documentation team members. Promoting data culture helps the documentation team to focus efforts on improvising customer outcomes.