Internal Customer
Listening
Product managers needed to collect and action feedback on their products efficiently. The solution was an easy to implement feedback form, with a hub powered by AI which pulled out themes and topics to empower managers to make decisions on product enhancements.
Background
The Ask
Product managers face challenges in efficiently receiving and managing internal customer feedback that can inform customer-centric decisions on investment planning, operational efficiencies, and value achievement.
This is due to fragmentation, access to users and inefficient feedback submission processes. Feedback is scattered across multiple channels which complicates context understanding and leads to duplication. The synthesis of unstructured feedback is time-consuming for analysis.
This impedes the extraction of actionable insights and, coupled with a lack of responsiveness, erodes user test and perception of the product’s commitment to improvement, leading to feedback apathy among users.
Methods
Over a 16-week discovery phase a feedback solution was designed and a prototype was built. This consisted of a simple feedback form, and a feedback hub. The form collected user feedback and could be implemented across different applications. The feedback could then be actioned in the hub, where product managers could close the feedback loop by launching a teams chat directly with the feedback submitter.
The feedback was analysed by AI, which generated global and product specific topics, along with sentiment scores. Business org data was captured through the Microsoft Graph API, which all fed into dashboards to give insights on the feedback data. We piloted the product on the People Portal with the People team and ran weekly user interviews with the stakeholders where they were able to give us feedback on the product and how they had found closing the feedback loop. This then allowed us to evolve the product as the phase progressed.
Next steps are to develop automated self-service onboarding for the product, so it is accessible to all teams within the company.
The Outcome
The product was received positively, product managers found the hub intuitive and the process of closing the feedback simple. The ability to gain insights from the feedback data powered by the AI has made it easier to see trends and inform customer-centric decisions for future product releases. Users submitting feedback felt heard, and more likely to engage in feedback submissions in the future.
The solution achieved the key aims and objectives outlined during the research phase. We created a quick and easy to implement feedback form which could be implemented across different types of applications. The product managers then can manage and close the feedback loop from the hub, and also gain insight on trends and key topics powered by the AI. Within the People Experience product group next steps are to extend the solution into the product for product space.
The Process
A discovery and prototyping phase, using agile methodologies to quickly build and deploy a solution for testing with a live digital product.
Define, Design and Build
Prior to the 16 week discovery and prototype phase research has been conducted to define the problem statement and key learning objectives. From user stories and user flows were created, to address the issues that both user types were currently experiencing.
Two goals were defined can we make feedback easy, and can we create a single view of all customer feedback?
Design and development worked closely to map out a solution that used Microsoft technology to access user data, to enable the closing of the feedback loop with users.
Using the companies design system I was able to rapidly design and prototype out the form and feedback hub for the solution. Running sessions with users for validation along the way, as-well as regular touch points with development as the solution needed to be built as quickly as possible to start the high fidelity prototyping.
Insights Powered by AI
To answer our second goal “Can we create a single view of all customer feedback?” we worked closely with a data and AI team to see how we could use an AI solution to pull insights from different feedback sources in a meaningful way.
While the looking at what kind of insights could be pulled from the feedback data, we found we could also use the AI functionality within the feedback hub to add more richness to how the feedback items can be managed and resolved.
As the feedback items come into the dashboard they at not only tagged with the meta data from the form, the AI would also scan the feedback item content and tag each item with a sentiment value, general topic, and a product specific topic. In the background the AI would also give each feedback item a quality score based on how much context was given in the feedback item description.
The AI tagging enhanced the data shown in the PowerBI dashboards, give the product manager the ability to see how different characteristics affect the overall feedback score.
Feedback Form
The final form used a star rating of the users experience of the application that day, this rating will become a measurable metric product managers can use to access generally how their products are doing.
Users next must choose what type of feedback they are going to submit from the options in the dropdown. This then means feedback is tagged when it arrives in the feedback hub, making it easier for product managers to manager and action.
Along with the text input area the users can also choose to upload an image with their submission. This feature would be help show visually what users are commenting on, whether it is a screenshot of the application with a query, or a suggestion of an enhancement.
Feedback Hub
The feedback hub design was developed while we worked closely with product managers on what features and functionality they would require to quickly and easily manage the feedback items, and close the feedback loop.
This also included looking at future functionality that we added to our backlog for consequence releases.
Clean UI: Minimal user interface, which when tested users found very easy and intuitive to use. Ability to sort and filter feedback items, to quickly address items that require attention.
Feedback items list: Feedback items that have been submitted, tagged with an ID, feedback type, status, global and product specific topics, and a sentiment value. Topics and sentiment value generated by AI.
Self-Service Onboarding
We designed a simple user flow for self-service onboarding hosted on the existing product. Those wanting to access the product visit the registration portal, where they will need to enter their product name with the option to also upload their product logo. This will then generate their test URLS, one for the feedback form and the other for the feedback hub. Here the will also be given the option to download one of the button assets already created to launch the form from.
Once the test form has been tested in a dev environment, and functionality tested in the test hub production URLs can be generated by the simple click of a button in hub header.
Step One
Register you product name with on the registration portal
Optional – upload your product logo
Step Two
We generate test URLs for you to use for the feedback form and hub
Step Three
Test the feedback launching for your dev environments and test out functionality in our test hub
Optional – use one of our feedback button graphics
Step Four
Generate production URLs by the simple click of a button within the test hub and deploy
Client Feedback
“Even though you were a contractor I felt like you were delivering as an employee. Specifically around using our standards and tools, seeking the branding compliance, and contributing back essentially to help us build out those bits. The people portal team still speak positively about your work and style whenever your name comes up in the weekly sessions with the team.”
Thank you for your interest in my work!