Audience Selector

What is Fanbank

Pumpjack Dataworks’ FanBank is a cloud-based software service designed to help Sports organisations know their customer base and increase their value. It’s designed to help teams, leagues, and federations unify their fan data, understand its financial value, and enable the secure exchange of data assets with their business partners.

Rightsholders typically have small databases of fans, yet the actual number is multiples larger. Pumpjack collects, connects, and protects all of your fan interests together through a unified solution, and provides you with the tools to know each fan and how to engage with them intimately.

Teams, Leagues, and Federations worldwide use Pumpjack to know their community, frame their knowledge, and unlock new value.”

My Role

I worked as a generalist UI/UX designer with a Lead Product Designer. I was responsible for the product design, visual design, UX design.

Discovering the Problem

When I joined our organization, the Audience Selector tool was a basic filter and search feature. While it served its purpose to some extent, it had limitations that were frustrating our users. As our product grew and the complexity of the data increased, users found themselves repeatedly recreating their filters, which was both time-consuming and inefficient. Additionally, there was no way for users to share their selected traits and values with others, hindering collaboration and data sharing within our platform.

Research

To test this and to discover problems and opportunities I created a research plan. Being a B2B platform we had access to the real users of our product and we we’re able to conduct interviews to discover their pain points and frustrations and also opportunities to make the product better aligned with our future goals (Data Trading).

Since we know who our users are I wanted to take the JTBD approach here because I wanted to understand what the users were trying to accomplish here.

Interviews

I interviewed five individuals from three different clients that included people who belonged to different teams like marketing, data analysis and sales. During the interviews, I asked them to share their thoughts on their current filter system and also asked them to take me through the usual process of filtering data. I also asked them what frustrations they face at every step.

Defining the Problems

User Journey Map

A synthesized version of the user journey map for the users I interviewed looked something like this. I discovered some interesting information.

The findings from the User Journey Maps feedback highlight a mixed bag of user experiences. While the system excels in terms of discoverability and intuitive elements such as the plus button for adding traits, it faces several challenges. Users feel overwhelmed by the multitude of filters and seek simplification, and there's a clear need for more informative trait information. Additionally, improving query creation and filter activation are essential. Users express a desire for real-time feedback, clearer guidance, and more efficient report generation processes. Addressing these findings will lead to a more user-friendly and satisfying system

JTBD - Functional Needs

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Efficient Filtering
Users want to quickly and efficiently filter data to access the specific audience segments they require for their tasks.

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Query Creation
‍Users need the ability to create complex queries with multiple criteria.

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Real-time Feedback
‍Users require real-time feedback and a summary of the data as they apply filters, enabling them to make decisions faster.

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Sharing
‍Users want to easily save and share their filtered audience segments with colleagues, enhancing collaboration and data sharing.

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Filter History
‍Users desire access to a history of their last used filters, so they can easily revisit previous configurations.

Competitive Analysis

I also compared and researched the competitors. Some of the users we interviewed mentioned some tools and examples. We did not have direct competitors or had access to all of the competitors’ tools so I focused my research on the segment filter design patterns that these tools like Facebook Business, Twitter Business, Hootsuite, etc have to offer.

This helped us pick out the best parts of the design patterns and create a powerful design for our use case.

Ideating

Brainstorming

Using the information collected I started with brainstorming solutions and ideas for each problem I identified and selected them based on Feasibility, Desirability and Viability.

User Flows

For each functional need and the idea that I selected while brainstorming, I worked on the user flow to establish how a user would achieve the goals. They started with steps and then evolved into flow charts.

Design and Prototype

After a series of Design pattern explorations, Wireframes, Low Fidelity, High Fidelity Prototypes, and a number of Iterations I came up with the final prototype. It solved some of the biggest problems our users had.

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Save and Share Filtered Audience Segments
Users can save specific audience filters for future use. Sharing functionality allows users to collaborate and exchange audience segments.

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Enable Complex Query Creation
‍Users can create complex queries using logical operators (AND, OR, NOT).Flexibility in forming advanced filter criteria for precise segmentation.

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Use Saved Audiences as Variables
‍Users can use their saved audience segments as variables for new queries. This simplifies the process of creating refined audience filters.

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Real-time Updates and Feedback
‍Users receive real-time updates while making filter selections. Immediate feedback on audience size and characteristics..

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Offer Contextual Information and Labels
‍Real-time summary of selected audience segments for quick insights. Users receive real-time updates while making filter selections. Immediate feedback on audience size and characteristics.

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