This article is the first in a three-part series about using analytics to drive documentation efforts and achieve business goals. The next two parts will explain what makes data meaningful, show how to use it effectively, and share concrete examples of how to turn data into content strategy.
In almost every profession, it has become essential to use metrics to track the impact of your work. Marketing experts measure click-through rates, manufacturers must measure production downtime, and salespeople are measured by adherence to sales targets.
And yet, in the documentation world, teams continue to deliver thousands of pages of content without any meaningful data upon which to measure their success.
So how can they measure the performance of their content? How do they know if they’re doing their jobs well, or if there’s something they could be doing better? And how can they demonstrate their team’s impact on the organization?
During my time at Zoomin, I’ve met dozens of companies who want to start collecting useful data about their documentation and better understand how their content is being used.
In the fall of 2019, I attended CIDM Best Practices in Austin, Texas, where the focus of the conference was on metrics and outcomes that define success for a technical communications department. Together with Joe Gelb, President of Zoomin, I spoke to a full room of people eager to learn how they can implement a data-driven approach in their documentation.
Our talk proved both engaging and rewarding, so I’d like to share some key concepts and examples that you can take back to your team so you can start building your own data-driven strategy today.
Why gather data?
However you choose to collect data, whether by employing third-party software or (especially) if you intend to build your own solution, it can be extremely complex and resource-intensive. So to get everyone on board, you’ll need to establish what you and your company have to gain from it.
Let’s consider it from three perspectives:
Benefits to Writers
Collecting quality data can help you do your job better. Having the right data helps you optimize your content efforts by focusing your limited resources on the most important tasks. For example:
- You can use data to easily find content that hasn’t been updated in the past 12 months, and focus your efforts there.
- Let’s say most of your content is aimed at admins, but your data indicates that most of your readers are end users. So you can use your data to direct resources towards creating content that’s more targeted to your user base.
- Your data may show that you have gaps in the Japanese localization of your content, while your audience data shows that 30% of your audience is located in Japan. This is an obvious call to action to prioritize closing the most important localization gaps.
- Your data may show that your audience is searching for subjects that are not covered in your content. This data can help you focus your limited resources on closing those content gaps.
- By checking patterns of documentation usage and navigation, you can determine whether you should be devoting more resources towards improving your API documentation or creating better navigation to reach the most important content.
Benefits to Managers
Data can be vital to your place in the company and the budgets allocated to your department. You’ll finally be able to quantify and share your successes and ongoing improvements. For example:
- Show the importance and impact of your content by demonstrating a rise in page views and returning users over time.
- Show increased audience engagement with feedback, ratings, scores and likes.
- Show your effectiveness at keeping content up to date, and more frequent content updates.
- Measure the impact of any cutbacks, new hires, or new tool purchases, and back up your claims with hard data. This can be a positive cycle, as wiser spending leads to greater trust and bigger budgets over time.
Benefits to your Audience
Use data to improve the user experience and help your audience get the most out of your content. For example:
- Help users find the right content quickly by reducing failed searches. If you can track all search queries and track the effectiveness of those queries, you can tweak the behavior of your search engine to direct users to the correct content (by using tools like synonyms).
- Drive recommended content from data that identifies the most popular topics.
With insights into how people use and engage with your content, you can start creating a more personalized and streamlined digital experience. This benefits not only end users, but also the people who are responsible for improving the usefulness of documentation, driving customer self-service, and increasing employee productivity.
In the second part of this series, we’ll categorize the different types of content analytics, show how to make your data meaningful, and demonstrate how establishing content and support goals can provide direction to that data.
If you’d like to explore this topic further along with interesting commentary and intelligent questions from our viewers, check out our recent webinar about data-driven documentation featuring Lawrence Orin and Joe Gelb!
Lawrence Orin is Product Evangelist and Customer Implementation Expert at Zoomin, where he lends his experience in documentation to help new customers with implementation, create their taxonomies, and develop their content strategy. He previously led documentation teams at Radvision and Riverbed, in addition to heading up other teams in technical support and customer services.
This article is an excerpt from “Becoming a Data-Driven Documentation Team” which was published in the December 2019 issue of Best Practices, a publication of CIDM.