ATLASSIAN ACCESS AWARENESS
RESPONSIBILITIES
UX / UI / Interaction Design
Quant Research
Early Signal Testing
Product Management
ATLASSIAN ACCESS AWARENESS
RESPONSIBILITIES
UX / UI / Interaction Design
Quant Research
Early Signal Testing
Product Management
The team and I decided to run a series of growth experiment to understand the top of the funnel of our feature product Atlassian Access. Particularly, we are measuring awareness (from product discovery to starting a trial) and activation (trial to feature enablement). The hypothesis is that by increasing the number of Jira/Confluence site admins who are aware of Atlassian Access, we’ll see a significant increase in the trial of Access in the test group versus the control group.
The team and I decided to run a series of growth experiment to understand the top of the funnel of our feature product Atlassian Access. Particularly, we are measuring awareness (from product discovery to starting a trial) and activation (trial to feature enablement). The hypothesis is that by increasing the number of Jira/Confluence site admins who are aware of Atlassian Access, we’ll see a significant increase in the trial of Access in the test group versus the control group.
WHAT IS THE PROBLEM?
WHAT IS THE PROBLEM?
WHAT IS THE PROBLEM?
Through deep diving into the analytics with a data scientist, we knew the problem is that currently approximately 94.3% (of our targeted cohorts of 11-500 employees) of the Jira/Confluence site administrators are not aware of Atlassian Access today. That meant that only 5.7% of our targeted cohort even discovered the Atlassian Access value prop page.
Through deep diving into the analytics with a data scientist, we knew the problem is that currently approximately 94.3% (of our targeted cohorts of 11-500 employees) of the Jira/Confluence site administrators are not aware of Atlassian Access today. That meant that only 5.7% of our targeted cohort even discovered the Atlassian Access value prop page.
WHAT IS THE GOAL?
WHAT IS THE GOAL?
WHAT IS THE GOAL?
The North Star goal of this project was to hit our company FY19 OKR of 350,000 paid Atlassian Access users (clear business goal). We’d reached 85% of our goal, but needed to improve the funnel through in-product awareness. We hypothesized that targeting customers specifically with Identity Providers would close the data gap. The reason we chose Identity Providers is due to the fact that the few who made it all the way through the funnel and become paid users, approximately 67% used SSO or SCIM (Identity Provider features). Our project goal was to deliver high impact with the least amount of dependencies.
Working close with the data scientist, we determined our metrics for success were:
Increase awareness of the Atlassian Access discovery value prop page by 8%
Increase of trial creations in the penetration rate of the test group versus the control group by 5%
Ultimately, for myself as a designer, the learning goal of this project is incredibly important as it’s the first time our Atlassian Access team has done a growth project. We’re hoping to gather data so even if we don’t get users to trial Atlassian Access (one of our key metrics for success), we’d like to learn if users,
Don’t have an Identity Provider
Don’t know what an Identity Provider is
Have an identity Provider, but aren’t interested right now
The reason being that we can leverage this information to inform designs later in the funnel or revise our existing awareness designs.
The North Star goal of this project was to hit our company FY19 OKR of 350,000 paid Atlassian Access users (clear business goal). We’d reached 85% of our goal, but needed to improve the funnel through in-product awareness. We hypothesized that targeting customers specifically with Identity Providers would close the data gap. The reason we chose Identity Providers is due to the fact that the few who made it all the way through the funnel and become paid users, approximately 67% used SSO or SCIM (Identity Provider features). Our project goal was to deliver high impact with the least amount of dependencies.
Working close with the data scientist, we determined our metrics for success were:
Increase awareness of the Atlassian Access discovery value prop page by 8%
Increase of trial creations in the penetration rate of the test group versus the control group by 5%
Ultimately, for myself as a designer, the learning goal of this project is incredibly important as it’s the first time our Atlassian Access team has done a growth project. We’re hoping to gather data so even if we don’t get users to trial Atlassian Access (one of our key metrics for success), we’d like to learn if users,
Don’t have an Identity Provider
Don’t know what an Identity Provider is
Have an identity Provider, but aren’t interested right now
The reason being that we can leverage this information to inform designs later in the funnel or revise our existing awareness designs.
WHAT IS THE PROCESS?
WHAT IS THE PROCESS?
WHAT IS THE PROCESS?
Much like other projects at Atlassian, the design process was broken down into a variant of the design thinking process all dependent on product scope, roadmap and timelines,
Much like other projects at Atlassian, the design process was broken down into a variant of the design thinking process all dependent on product scope, roadmap and timelines,
Discovery Phase (understanding & gathering)
Used this time to gather context and understanding by collecting and ingesting quantitative (redash) and qualitative (user interviews and feedback) data
A lot of whiteboarding, summarizing past research and design efforts, and documenting current state (Confluence)
Creating user journey and flows to help empathy mapping
Ideate Phase
Explored the problem space and began high fidelity designs due to time constraints
Did several prototypes and put them in front of stakeholders feedback
Wrote a test plan and did early signal testing to validate comprehension and discovery
Refine Phase
Put a prototype in front of live users one final time
Finalized design based on stakeholder & customer feedback
Discovery Phase (understanding & gathering)
Used this time to gather context and understanding by collecting and ingesting quantitative (redash) and qualitative (user interviews and feedback) data
A lot of whiteboarding, summarizing past research and design efforts, and documenting current state (Confluence)
Creating user journey and flows to help empathy mapping
Ideate Phase
Explored the problem space and began high fidelity designs due to time constraints
Did several prototypes and put them in front of stakeholders feedback
Wrote a test plan and did early signal testing to validate comprehension and discovery
Refine Phase
Put a prototype in front of live users one final time
Finalized design based on stakeholder & customer feedback
↳ DISCOVERY PHASE
A bulk of my time was spent on dissecting existing data and user behaviors. I dove into quant data with a data scientist. I began breaking down the problem with my triad through whiteboading as well as work with another designer on journey mapping to empathize with the customer through their experience.
A bulk of my time was spent on dissecting existing data and user behaviors. I dove into quant data with a data scientist. I began breaking down the problem with my triad through whiteboading as well as work with another designer on journey mapping to empathize with the customer through their experience.
Quantitative Data (deep diving into the data using Tableau to understand the top of the funnel)
Quantitative Data (deep diving into the data using Tableau to understand the top of the funnel)
Whiteboarding (First session of whiteboarding with my triad was around our collective assessment of the quant data, we'd collectively and individually had synced with different key members of data science, analytics and PMM. Goal now was to put our hypotheses together)
Whiteboarding (First session of whiteboarding with my triad was around our collective assessment of the quant data, we'd collectively and individually had synced with different key members of data science, analytics and PMM. Goal now was to put our hypotheses together)
↳ IDEATION PHASE
I created high fidelity mocks that I sparred in person and async-ly (Mural app) with key stakeholders to gather feedback. I also did early signal testing to validate discovery and comprehension. I then re-iterated the designs accordingly based on the feedback and learnings.
I created high fidelity mocks that I sparred in person and async-ly (Mural app) with key stakeholders to gather feedback. I also did early signal testing to validate discovery and comprehension. I then re-iterated the designs accordingly based on the feedback and learnings.
Whiteboarding (Subsequent whiteboarding sessions with my triad led to ideations and which part of the funnel we wanted to approach)
Whiteboarding (Subsequent whiteboarding sessions with my triad led to ideations and which part of the funnel we wanted to approach)
Customer Journey Map (the discovery and whiteboarding sessions helped to create a customer journey map where I could focus on conversion & churn)
Customer Journey Map (the discovery and whiteboarding sessions helped to create a customer journey map where I could focus on conversion & churn)
High Fidelity Designs (since this was a growth experiment, we wanted to explore and get to stat sig quickly to determine whether we should move forward or pivot quickly. The goal was to move a rapid pace with all existing design system existing components to build and measure quickly)
High Fidelity Designs (since this was a growth experiment, we wanted to explore and get to stat sig quickly to determine whether we should move forward or pivot quickly. The goal was to move a rapid pace with all existing design system existing components to build and measure quickly)
Early Signal Testing (I then wrote EST plan with a subset group of users and shared out the results with design and product leadership)
Early Signal Testing (I then wrote EST plan with a subset group of users and shared out the results with design and product leadership)
↳ REFINE PHASE
The final refine phase consisted of gathering all internal and external feedback and doing a proper handoff for engineering as well as future instrumentation and plan for a go-to-market plan with marketing.
The final refine phase consisted of gathering all internal and external feedback and doing a proper handoff for engineering as well as future instrumentation and plan for a go-to-market plan with marketing.
User testing & feedback (after several stakeholder and customer feedback sessions, I put the final designs into an inVision prototype to put in front of live users one last time. Designs are then finalized and are prepared for handoffs)
User testing & feedback (after several stakeholder and customer feedback sessions, I put the final designs into an inVision prototype to put in front of live users one last time. Designs are then finalized and are prepared for handoffs)
CONCLUSION
This Awareness piece of this project was in development by Q3 2019 release date (Australian quarter, end of March 2019) right before I left Atlassian for to work at Chegg. Con-currently happening was the Activation piece of the project (current project I worked on after the awareness piece). Since this project is a growth initiative, it was very important to track the data very closely. Aside from our metrics for success, we were opting for learn to understand user behaviors as well.
This Awareness piece of this project was in development by Q3 2019 release date (Australian quarter, end of March 2019) right before I left Atlassian for to work at Chegg. Con-currently happening was the Activation piece of the project (current project I worked on after the awareness piece). Since this project is a growth initiative, it was very important to track the data very closely. Aside from our metrics for success, we were opting for learn to understand user behaviors as well.