ADTHENA: INSIGHTS
Adthena is a market-leading search intelligence solution for paid search advertising. Adthena's mission is to empower marketers to optimally reach, acquire, and retain consumers with unique data-driven search intelligence. The data is driven through Ai technologies and delivered to our customers through a web application, business intelligence reports, and consultancy team.
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The design role was to create features in the web application to help search experts optimise their Google Ads campaigns. We also ensured our customers have a consistent experience across the business from sales and onboarding to being active customers.
MY CONTRIBUTIONS
Fully responsible for multiple features across the full product lifecycle.
Worked across 2 'squads' of 4 engineers, 1 QA and 1 PM each and Data Scientists
​Worked in a design team with 2 UX designers and 1 UX researcher.
Created, managed and delivered a UX improvements from post release research.
Built a design system using atomic design methodology to ensure consistency.
Hired, onboarded and mentored designers. I worked with design agencies.
MY PROJECTS
I worked on a range of projects at Adthena, below illustrates my process for our insights feature. Adthena insights are notifications of shifts in the market, whether a competitor has entered their space or they've started using a new offering. Using Ai and machine learning Adthena can identify these changes and alert out customers so they can take action straight away.
V1 of this feature has been released Q2 2022.
1.
This project was prioritised based on current feedback we had received for an existing feature. We learnt from our users that they wanted to receive more recommendations on what they should use in their adverts.
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Working with our data science department we started building a new model to help do exactly this. To ensure this model was accurate and valuable we tested it out as an alpha version with simple chart visualisations in our 'labs' area of the tool, where end users can play with potential new features and give us feedback.
2.
We conducted value interviews with customers who started to use this data. Using Pendo we were able to track usage of this feature to gather a list of customers to talk to.
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We understood how they were using this data and what needs we should address from our MVP. We created a user journey and started ideating.
3.
We understood that our customers wanted to receive timely insights on what they should include in their ad copy.
Using our internal customer facing team we ran brainstorming sessions to come up with ideas.
As the application has a format for existing market insights, we built upon this to ensure consistency in our designs.
4.
This led us to iterate designs quickly with the engineering team on the UI.
A script and prototype were developed for a round of usability interviews to mitigate any risks. Feasibility risk round was also conducted with engineers.
As we were designing an MVP, we took this opportunity to ask what users might want to see as a development of this feature as well.
5.
Lastly, we created user specification flows and responsive designs with updated style guides. These are reviewed with engineers.
Working with a PM, we started writing user stories in Jira and worked with a squad of engineers to start developing the feature. I contributed and ran various engineering ceremonies e.g. stand-ups, refinements, scoring and planning.
6.
The MVP of the feature was delivered to customers in Q2 2022. Based on success metrics we have set, there will be a wait of 3 months to see how this feature is used in practice before conducting post release interviews.
