Designing Analytics from scratch
Analytics reports and data in general are some of the most complex areas in any product. This was especially true at Teamtailor since we deal with everything related to recruitment — job ads, candidate applications, hiring, career site and more. Moreover, our customers range from large corporation (70,000 employees) to small businesses (1-10 employees).
This article covers how the team comprised of 2 developers and 1 designer (me) tackled the issues and successfully released a new analytics system for Teamtailor.
Discovery: Identifying the use cases and problems
The first step was to review the existing analytics solution.
Through user research and evaluation of existing support material, the following conclusion was made:
The system provided all the numbers, but did not present it in a meaningful, data-driven way.
Most of the desired data was available. Customers needed to use the left navigation to go through various metric and apply filters to see data for specific jobs, departments etc. The problem with this was that, if you wanted to see data about one job, multiples clicks and filters were involved.
Personas & use cases:
Our customers were categorised into these 3 types based on their use cases for this project:
- Recruiters: The person who is actively doing the hiring
Use case example: How is the promotion I just bought performing? Where do potential candidates drop-off in the application process? - Managers: Usually a team lead that manages multiple jobs/departments
Use case example: Who has done the most hires in the team? Are we on track to fill this role by June? What are our top reasons for rejecting candidates? - Management: C-level executives who are mostly interested in the big picture
Use case example: How did our candidate NPS score progress last quarter?
Doing this helped clarifying who I am designing for and what their goals are. It eventually helped define the vision for the project.
The vision:
Build an analytics system that provides ready insights and can be useful to everyone. Our goal was to cater to most, but not all needs. For instance, there will be customers who want more granular data for which an external Business Intelligence tool can be used.
Defining key metrics to be included in reports
In the execution phase, one of the first things I focused on was collecting all the metrics relevant to recruitment. While Teamtailor already had most of it, some key metrics used in the industry were missing, e.g., time to fill.
Next, these metrics were mapped to various topics (jobs, departments, employees). This gave us a starting point for implementation.
Grouping data meaningfully to tell a story
With the metrics in place, the idea was to mimic the way users think about recruitment: in terms of jobs, departments, employees etc. So instead of dumping all the numbers in a table with filtering options, it would be more useful to present data as reports corresponding to the various use cases..
Inspired from e-commerce browsing, each key area such as jobs, departments and employees has a listing page which provides an overview. Clicking on a specific item in this page allows customers to dive deeper into it (detail page).
Sections within each report:
To further increase clarity, data presented in widgets were grouped into relevant sections.
For example, the job report was divided into 3 sections:
- Audience: This section gives insights on the job ad published such as conversion rate, funnel, sources and promotions.
- Recruitment: This section has all metrics related to recruitment such as pipeline speed, average times, reject reasons etc.
- Users: This section is dedicated to showing hiring team activity and is especially useful to track internal goals.
Similar grouping was done for other reports.
Designing the building blocks
Finally, we developed the fundamental components that make up all the charts.
Creating a UI library early on
The UI is perhaps the most obvious difference between the old and the new version. In contrast to the old version, I wanted the UI to be light and un-intimidating. Hence, the use of gradients, simple lines and a balanced color scheme.
It was worth it to define and build a library with all the charts, their variants, colours and typography. More on that in a follow up article. :)
Grids & widgets
The building blocks for the analytics feature needed to be versatile. They needed to be scalable, dynamic and flexible to accommodate different types of charts, lists and other content. Hence, the use of CSS grids with widgets were the perfect solution. A 6-column grid was used for maximum adaptability.
Flexibility is the key
A power-feature in analytics is the custom reports feature: the ability to tailor reports to each user’s needs. If none of the pre-made reports suit a customer, they have the freedom to create their own report from scratch. They can choose any metric they want to see, filter it in any way and get the data they need.
Conclusion
Analytics is never easy. By providing these pre-made reports and the potential to build custom reports, we hope that our customers feel empowered to use data to streamline their hiring process.
Read more about the job reports in Teamtailor’s support article: https://support.teamtailor.com/en/articles/5338993-analytics-jobs-report
A huge thanks to my amazing team who made this happen: André Ligné & Björn Nordstrand.
