David P. O'Hara
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Moz Content

Moz Content Audit

 

Moz Content Audit

The content audit was one of the main features for Moz Content. Users could enter a URL and it would perform an audit of the content on the site. This provided insight into specific details such as share count, word count, most shared topics, and much more. Below I outline the process and outcomes of my work on this feature.

 

DELIVERABLES I WORKED ON

SKETCHES  |  WIREFRAMES  |  USER FLOWS  |  DESIGN COMPS  |  RESEARCH

 

The Problem

For many within the content marketing space, the content audit is an integral part of their process. There are varying methods used, but many manually audit the content, analyzing large amounts of data with excel spreadsheets and other tedious and time consuming methods. The goal of Moz Content was to automatically surface key insights through the content audit so users could focus more on strategy and less time on analysis.

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Starting with a prototype

This is where the audit was when I got started. It was a series of widgets showing data. My goal was to simplify the data visually and help the user get immediate value from it. I also needed to design it in such a way that it fit within the Moz product family and took on a personality of its own within that brand.

 

Context

Moz Content was a brand new product and I had the unique opportunity to work on it from the ground up. Our team was made up of 4 developers, a product manager, myself, and the support of a few other resource teams within Moz. We learned a lot and iterated quickly moving toward providing great value to a massively expanding industry. We launched an alpha and beta that we learned a lot from before our full release.

 

PROCESS

The first step for me with the product was asking a lot of questions. At the beginning, I was handed a thick product requirements doc and some really rough wireframes. After asking a lot of "why's" to understand things fully, I spent awhile sketching concepts for how we could bring all this data to life. There were many challenges along the way and I will walk through one specific graph example below.

In our early prototypes the team had used a bubble graph to represent the topics found across the site. Size and weight of the color indicated the dominance of the topic across the site. I didn't like this particular graph because i felt it was hard to get the right value from it. Due to time constraints we had to go with it for our alpha, but I continued to push to iterate.  

Our second iteration showed better distribution for the topics adding the X and Y axis to understand the relevance of the topic within the site and its performance. I still felt like this graph wasn't quite right and we continued with research to understand the value users wanted most.

The third iteration of the graph got to the heart of the value users wanted to know about their site. We changed the title and the visualization to reflect the most shared topics and their share counts. This resonated with our users and felt like it provided much more value within the product.

 

THE Solution

The audit had many changes in layout and format, but this was our final version. There were two views called the "snapshot" view and "trends" view. The user could look at the latest collection reflecting one point in time for how the content was performing or they could connect google analytics and track the performance of the content over time with "trends".

 

Snapshot view

The trends graph allowed power users to mix and match the metrics they wanted to compare. They could zoom in within a specific window of time to get understanding around what was happening with the content during times of spikes or drops in traffic and shares.

 

Trends view

 

Outcome

This latest version of the content audit really nailed a lot of what our customers were looking for and provided a lot of value for users who had traditionally done their audits manually. We had around 400 users with more than 30k in monthly recurring revenue and the app growing 20-30% month over month.

 

What I Learned

I learned a lot about how users were analyzing their data and creating ways to simplify it in a visual manner that is easy to understand and provide a lot of value from. This experience has given me a strong sense for distilling data to a hierarchy leading with the most important info to help reduce cognitive overload.