TrueData's

Audience Builder


A tool for finding your product-market fit

Hero Image For Audience Builder

NΒΊ 01Overview

Finding the man of your dreams

TrueData's core product is matching our customers with the perfect audiences for them. For a long time, this was a manual process, performed by our wonderful Customer Management team and Sales. Our team would process the results and email them to our customers. As our client list grew, the workload was not scalable for our Customer Management team and we decided to take our internal tool and make it customer facing.

Over the next year and a half we would take this tool from a bare form to a unique tool that solved pain points in ways none of our competitors were.

The tool I was updating looked like this:

NΒΊ 02Learning From the Best

The first iteration was an opportunity to build TrueData's first design system, implement new UI to our newly customer-facing product, and nail down the IA. We wanted to get the tooling in our customers hands as quickly as possible, and began testing with them immediately. Talking to 5 customers, I performed qualitative research to learn more about what was needed in the product, what was desired, what our competitors did right, and where our competitors had failed.

This is what we thought our customer's journey looked like:

NΒΊ 03Small Changes, Quickly

Our customers needed us to help fix how they select their ideal audience. This process was long and convulted on competitors platforms. This would improve our target metric of outperforming our competitors, and lower our customers cost per click. Additionally we had no way of visualizing for the users the total size of the Audience was. Finally, our aesthetics needed updating, so I built our companies first design system to apply to all designs moving forward.

I started by adding specificity to the Audience Builder. For each selection I included logistical operators such as "and"/"or". I knew not all marketers come from a tech background, and included tooltip hovers and contextual visuals.

NΒΊ 04Getting Feedback

Next, we added an "Audience Scale Meter" to not only show the size of the selected audience, but to Give context to our Marketing customers about what the size of the selected audience means. For example, if the audience is too large, the benefits of targeting becomes nearly useless. If the audience becomes too small, your campaign wont have enough reach (If it gets TOO small, you're invading privacy, and I set up safeguards to prevent individual privacy being invaded).

I presented these designs back to our engineers and our customers and wrote a research document to clarify my findings. The general feedback was:

The primary benefit of the logistical operators was simply revealing how the audience was being concatenated together.
For version one, remove ability to toggle between operators, our backend wasn't designed to support this functionality and would take up too many engineering resources early on. After we built the toggles, a Fullstory analysis proved this point by showing just how rarely people tried to toggle.
What our customers really loved about our platform was their interactions with he customer service team. Our Customer Service team was building our customers audiences; helping discover new audiences, and test out multiple audiences at once.
Make our audience builder more flexible, reimagine the way our customers interact with our platform. Allow customers to build and submit multiple audiences at once. Add audience plans to groups called "test plans"

NΒΊ 05Final State

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Design Metrics

Satisfaction

Goal: ​ ​ ​ 4.5/5

Actual : 4.7/5

Audience's Built / Month

Goal: ​ ​ ​ N/A β†’ 5

Actual : N/A β†’ 2

Rage Click Rate

Goal: ​ ​ ​ N/A β†’ < 1.00%

Actual : N/A β†’ <.0004%

Business Metrics

Click Through Rate

Goal: ​ ​ ​0.90% β†’ 1.00%

Actual: 0.90% β†’ 1.24%

Cost Per Click

Goal​: ​ ​ ​ $3.35 β†’ $2.50

Actual: $3.35 β†’ $1.47

New Customers

Goal: ​ ​ ​ 100% β†’ 120%

Actual: 100% β†’ 180%