TURF Output I

Project Overview

This feature enabled researchers to optimize data for Total Unduplicated Reach and Frequency (TURF).

Role

UX Research

Competitive Research
Test Script & Recruitment
Usability Testing
Analysis

UX Design

Wireframes
Prototypes

Final Design

Key Impact and Results

This update allowed the center of excellence team (COE) and clients conduct TURF surveys and analyisis on one platform.

An average of 30 TURF studies are launched monthly since release in March 2023.

165 unique brands use TURF monthly, such as: Pepsi, Talking Rain, 1440 Foods, Brilliant Food and Beverage, and Coca-Cola for B2B and B2C SaaS insights platform driving over $60M in ARR.

Constraints

In order to ensure TURF specific questions could utilize this analysis tool I needed to design a new question type to funnel results to the right place. This would impact the survey setup and results flows.

TURF analysis would be launched as a minimal viable product (MVP), it was up to me to uncover which feature would be included, with the PM and Engineers, and determine which were critical in the first phase.

These new features would apply to TURF results for now but some would eventually be scaled to other question types and I needed to plan ahead for that.

This was my first time working with a new Project Manager, communication styles were being navigated.

The Challenge

What would an MVP look like for a feature that doesn't exist on the website, but for which clients have a mental model?

What is TURF?

TURF is a market research methodology that identifies the optimal combination of items (e.g., ice cream flavors) that will appeal to the largest number of consumers. This methodology quantifies how to achieve maximum customer reach with the fewest number of items.

Unlike other research methodologies, TURF analysis requires additional manipulation when reviewing and analyzing results. This outcome is accomplished through the “TURF Simulator.”

Competitive Analysis

I began looking for platforms with TURF Simulators to better understand our clients’ expectations and mental model.

I looked at competitors such as: Conjointly, Zappi, Quantilope, Alchemer, Qualtrics, etc.

Conjointly 1

Conjointly 2

Internal Reports

Quantilope

I uncovered different chart types and orientations, chart legends, and features for TURF analysis, such as:

Number of options on display
Visualization of reach and frequency
Breakdown of audience makeup
Customization of results by reordering and removing options.

As a result, I explored 3 chart orientations to optimize the data output.

Explorations

One focus for this MVP was data output orientation and the ability to Reorder results, Exclude options, and Filter based off data output.

Sketches

I explored three orientations to display TURF results. I mocked up horizontal and vertical bar charts, as well as a table to optimize data output visualization.

Vertical bar chart - Most common orientation among competitors.
Table - Clearest breakdown of data.
Horizontal bar chart - Most consistent with the hosting platform.

Low-fidelity wireframes exploring output orientations.

Mid-fidelity wireframes

Mocked up in Figma

Horizontal

Vertical

Table

Horizontal

Pros

Consistent with how data is displayed on the platform, this version allowed for the highest number of items to be seen at a glance.
Vertical scroll allows users to see results at a glance and maintains consistency with current user behavior.
This option scales easily.

Cons

Responses are cut off after the first 13, depending on screen size. While scrolling can help mitigate this problem, TURF questions can have anywhere from 25-40 response items.

Vertical

Pros

This version clearly showed the incremental reach but did not scale well.
Most common format revealed during competitive analysis.
Meets mental model of clients who have conducted TURF analysis on other platforms.

Cons

Responses are cut off after the first 10 items.
Introduces a new chart orientation for results on the site.
Introduces horizontal scrolling, which is inconsistent with current scrolling pattern and may cause challenges for clients.
The text was hard to read and could not be angled due to technical limitation of Engineering.

Table

Pros

Clear breakdown of data at a glance.
This version, displayed the results, reach, and frequency in a column layout.

Cons

Introduces a new orientation for result charts.
Is incompatible with other features on the site.
Could not be built due to technical limitations.

Check-In with Engineering

Next, I reviewed these orientations with the Project Manager, Product Manager, Business Analysis, and Engineering.

During the call it was confirmed that we would only continue with the Horizontal orientation for data output since it was the most possible to develop from an Engineering perspective within the projected timeframe.

It was most consistent with the display for other question results on the website as well.

Informational Interviews

I spoke with three market researchers to uncover their primary needs for the analysis tool.

Features they requested:

Reorder
Exclude
Merge
Frequency
Reach
Automatic Rebasing
Filter by demographics
Number of items on display

Discover

The most essential TURF features for clients were the ability to reposition, exclude, and filter results by demographics.

I designed the Simulator Panel to include these functionalities.

Reposition Explorations

The ability to reorder helps clients analyze different combinations of items that identify the ideal set in order to reach the maximum number of customers without unnecessary overlap.

Popover/modal
Drag and Drop
Side Panel

Popover/modal

Drag and Drop

Side Panel

Reposition Direction

During testing, when asked to reorder options, participants expected the capacity to drag and drop response items directly on the chart.

All five successfully located and used the dropdowns in the side panel.

Three rejected the popover, as it blocked access to the chart results.

During a cross-department feasibility call, Engineering determined that drag and drop was not possible to implement.

Side panel dropdowns were a feasible alternative.

“I want to see the first 5-10 results at a glance. The rest doesn't teach me much.”
- Allie, Test Participant

“The first three positions are the most important to me.”
- Rachel, Test Participant

Exclude Explorations

Here I mocked up a feature that enabled clients to remove answer items from the chart, which helped them focus on relevant results.

Side Panel Search

Checkboxes

Exclude Direction

Testing disclosed participants’ desire to exclude row items in bulk.

Testers instinctively tried to click on the item name to remove items, expecting the entire row to be selected.

A meeting with Engineers confirmed that a ‘select all’ checkbox interaction was feasible.

Checkboxes

Selections

“I want to click on the item label, not just the checkbox to make a selection.”
- Max, Test Participant

Excluded

Demographic Filters

I explored designs that allowed clients to filter results by gender, age, income, and ethnicity. Businesses use demographic filters to target the ideal consumers of products.

Within TURF Simulator
Placed within Chart Settings

Demographic Filters' Direction

I spoke to the design team about informational hierarchy and determined that the demographic filters would live within the TURF Simulator.

Chart Settings would remain a separate tab within the settings panel.

Chart Settings function manipulates the chart, while demographic filters and simulations impact data output.

Usability Testing II

I tested these features with five employees who have extensive market research knowledge to ensure that clients would be able to easily locate the new features and use them with the least amount of friction.

Participants requested that an indicator be added to the chart once data has been manipulated for reference purposes. They otherwise reported the new features as being "easy to use" and "meeting expectations."

Next Steps

Feasibility Calls with Engineering
Updates and Annotations
Sync with BA and PM
Handoff Final Flows for UI Treatment
QA Feature and Flag Bugs before Launch

Delivery

I handed off the wireframes to the UI Designer post testing, then handed off the final design to the Engineering team for development.

An average of 30 TURF studies have been launched monthly since release in March 2023, with 165 unique brands using TURF, such as: Pepsi, Talking Rain, 1440 Foods, Brilliant Food and Beverage, and Coca-Cola for a B2B and B2C SaaS insights platform driving over $60M in ARR.