How to use data and analytics in your documentation team
Published: 2022-09-27 09:08:37

## 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.

## 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

**Continue reading the full article at [Document360](https://document360.com/blog/data-driven-documentation-team/)**