Apparel Shopping Frustrations

VERIFYT: Apparel Shopping Frustrations

My Role
User Research:
Qualitative & Quantitative

Company

Team
1 researcher (me)
2 UX/UI designers
App + web developers

Date
June, 2022

Client + Product Summary
NetVirta is a B2B2C SaaS company who has created several smartphone-based, 3D body scanning technologies that serve an array of industries. Examples include CurveCapture®, a mobile 3D scanning app used to scan patients heads, aiding in the creation of custom cranial orthotics, and Verifyt®, a full-body and foot scanning app used to suggest best fittings shoes and apparel. These scanning apps are complimented with: (1) an end-consumer facing eCommerce plug-in that connects with the scanning app to suggest best sizes and styles directly on a partnering brands' eCommerce site, and (2) a business-facing customer intelligence portal used as a 3D model database and analytical tool.

Project Goals + Research Questions

The goal of this project was to understand the frustrations felt by participants when online shopping (if any), the level of that frustration, and possible remedies to lessen said frustration. To do so, we set out to answer the following research questions:

What fit specific issues do shoppers currently face?
- For tops?
- For pants?
- For dresses?
- For formal wear?

Secondary questions to be answered:

What hinders consumers from buying something online?
Would anything help you move forward with a purchase given any hesitations?

Tools Used

Userinterviews.com
Participant recruitment

Microsoft Teams
Conduct and record virtual interviews

Qualtrics
Create survey

Pollfish
Collect additional survey responses

Microsoft Excel
Create notes sheet, compile notes from all user interviews and survey

Mural.co
Note themes, analyze data, present findings

Microsoft Powerpoint
Present my findings

Discovery Phase

1. Identified First Step
Decided as a team that we needed to identify the exact fit problems online shoppers are facing and how we can help them with these problems

2. Competitive Analysis
Conducted an analysis of other solutions that aim to solve the fit problems faced by online shoppers

3. Participant Criteria
Established the criteria participants must meet to take part in our study

4. Data Collection Methods
Decided which data collection methods to use based on the problem we are trying to solve and the questions we are trying to answer

The Process

1 Recruitment
Used a screening survey to:

capture demographics of users
identify participants who shop online, in-store, or both

Chose 12 participants of ranging ages, genders, races/ethnicity, and household incomes

2 Scheduling + Introduction
Sent an introductory email with a calendar invite for date and time of interview

Field Study
Conducted observational interviews with all participants using the following layout:

3 Part 1: Introduction

Welcomed participant
Gained consent to record the interview + set up recording
Introduced session details

Part 2: Pre-Test Questions

Identified participants apparel shopping habits (in-store vs. online)
Identified participants' top-of-mind positives and negatives of shopping for clothing

Part 3: Observation

Shopping task #1: shop for a top
Shopping task #2: shop for bottoms
Shopping task #3 (if permitted): shop for formal wear

Part 4: Post-Test Questions

Identified any other frustrations not observed during the study and what they believe could help lessen said frustrations
Identified any additional tools not used during study to help during the shopping experience
Concluded the study + distributed incentive

Survey
Collected 413 total responses to gain further insights into the specific fit problems faced by apparel shoppers

Given the large population size (anyone who shops for clothes), I aimed to collect >400 survey responses to be statistically valid within a +/- 5% margin of error (Source)

Collected 113 responses through free distribution channels, including:

Facebook + Instagram
Multiple Facebook groups
Multiple Reddit Threads

Collected the remaining 300 responses using Pollfish

4 Findings Summary

Field Study Findings

eCommerce Shopping Habits & Processes

Average shopping flow:
- Chooses gender and clothing category
- Searches for an article of clothing they like
- Chooses size/color (interchangeable)
Available sizing usually affects color bought
Top and bottom shopping is usually the same per shopper
If filtering by size, usually by those in a specialty sizing category (i.e., plus size, petite, short, tall)

eCommerce Shopping Frustrations

Finding the right size and fit
Find products they like but their size is unavailable/out of stock
Plus size and short models wearing products don't represent how products actually look on a consumer
Finding products they like but retailer/brand not offering the product in their size
Figuring out different types of sizing (i.e., US vs. European)
Can't feel material of things they are shopping for
Lack of confidence in what will fit them at new brands
Sizing or coloring in person is off

eCommerce Shopping Tools Used

Reviews - Used by most
- Used to get a sense of size/fit (mainly) and quality of clothing
- Directly affects if a purchase is made or not
- Users love the ability to filter reviews by height/weight/size
Pictures/Videos of Products - Used by most
- Used to get a sense of quality and more detailed view of a product
- Many don’t trust that what they are seeing is how it will fit on themselves
- Very helpful when height/weight and size worn for the model is available
Size Chart - Used by 2/10
- IF used (which is not often), used to convert US to other sizes (i.e., European, etc.)
- Users do not know their measurements, proving useless to many – just want to see size information
"More to Consider" - Used by 2/10
- If size is unavailable for a product they like, will refer to this section to find something similar in style
Cart - Used by 3/10
- Users use their cart as a saving space and will revisit to go through/delete some items before checking out officially

Survey Findings

Frustrations

Finding the right fit (i.e., clothing tends to not fit my body shape well) - 44.3% of respondents
Finding the right size (i.e., my size falls in-between or outside of the offered sizes) - 39.2% of respondents
Return process - 39.5% of respondents

Possible Remedies (According to Respondents)

Reviewers providing height, weight, size ordered, and pictures of them wearing product
Seeing pictures of real people wearing the product and their measurements
Showing how clothes look on different body types
Showing models in their size
Mandatory clothing dimensions and useful size charts

Fit Issues
The following graphs represent which articles of clothing respondents experience issues with fit. For more details on which aspects of these clothing articles give respondents the most trouble, visit this Mural Board.

Design Opportunities

"We recommend you filter by..."
- For 2nd time + users
- Biggest frustration is finding products that don't come in their size
- Could be used for filtering by size category (plus, petite, tall, short) or by size itself
Suggest other styles that we know are available in that user's size
Ability to filter/show user reviews left by those with similar measurements to theirs
Show how products would fit in certain areas, based on the percentage of users who said they struggle with certain areas (i.e., length is a must, neck circumference is not, etc.)

CHELSEA MORGAN

VERIFYT: Apparel Shopping Frustrations

My Role User Research: Qualitative & Quantitative

Company

Team 1 researcher (me) 2 UX/UI designers App + web developers

Date June, 2022

Project Goals + Research Questions

The goal of this project was to understand the frustrations felt by participants when online shopping (if any), the level of that frustration, and possible remedies to lessen said frustration. To do so, we set out to answer the following research questions:

​

What fit specific issues do shoppers currently face?

For tops?

For pants?

For dresses?

For formal wear?

Secondary questions to be answered:

What hinders consumers from buying something online?

Would anything help you move forward with a purchase given any hesitations?

Tools Used

Userinterviews.com Participant recruitment

Microsoft Teams Conduct and record virtual interviews

Qualtrics Create survey

Pollfish Collect additional survey responses

Microsoft Excel Create notes sheet, compile notes from all user interviews and survey

Mural.co Note themes, analyze data, present findings

Microsoft Powerpoint Present my findings

Discovery Phase

1. Identified First Step Decided as a team that we needed to identify the exact fit problems online shoppers are facing and how we can help them with these problems

2. Competitive Analysis Conducted an analysis of other solutions that aim to solve the fit problems faced by online shoppers

3. Participant Criteria Established the criteria participants must meet to take part in our study

4. Data Collection Methods Decided which data collection methods to use based on the problem we are trying to solve and the questions we are trying to answer

The Process

1

Recruitment Used a screening survey to:

capture demographics of users

identify participants who shop online, in-store, or both

Chose 12 participants of ranging ages, genders, races/ethnicity, and household incomes

2

Scheduling + Introduction Sent an introductory email with a calendar invite for date and time of interview

Field Study Conducted observational interviews with all participants using the following layout:

3

Part 1: Introduction

Welcomed participant

Gained consent to record the interview + set up recording

Introduced session details

Part 2: Pre-Test Questions

Identified participants apparel shopping habits (in-store vs. online)

Identified participants' top-of-mind positives and negatives of shopping for clothing

Part 3: Observation

Shopping task #1: shop for a top

Shopping task #2: shop for bottoms

Shopping task #3 (if permitted): shop for formal wear

Part 4: Post-Test Questions

Identified any other frustrations not observed during the study and what they believe could help lessen said frustrations

Identified any additional tools not used during study to help during the shopping experience

Concluded the study + distributed incentive

Survey Collected 413 total responses to gain further insights into the specific fit problems faced by apparel shoppers

Given the large population size (anyone who shops for clothes), I aimed to collect >400 survey responses to be statistically valid within a +/- 5% margin of error (Source)

​

Collected 113 responses through free distribution channels, including:

Facebook + Instagram

Multiple Facebook groups

Multiple Reddit Threads

​

Collected the remaining 300 responses using Pollfish

4

Findings Summary

Field Study Findings

eCommerce Shopping Habits & Processes

Average shopping flow:

Chooses gender and clothing category

Searches for an article of clothing they like

Chooses size/color (interchangeable)

Available sizing usually affects color bought

Top and bottom shopping is usually the same per shopper

If filtering by size, usually by those in a specialty sizing category (i.e., plus size, petite, short, tall)

eCommerce Shopping Frustrations

Finding the right size and fit

Find products they like but their size is unavailable/out of stock

Plus size and short models wearing products don't represent how products actually look on a consumer

Finding products they like but retailer/brand not offering the product in their size

My Role
User Research:
Qualitative & Quantitative

Team
1 researcher (me)
2 UX/UI designers
App + web developers

Date
June, 2022

Userinterviews.com
Participant recruitment

Microsoft Teams
Conduct and record virtual interviews

Qualtrics
Create survey

Pollfish
Collect additional survey responses

Microsoft Excel
Create notes sheet, compile notes from all user interviews and survey

Mural.co
Note themes, analyze data, present findings

Microsoft Powerpoint
Present my findings

1. Identified First Step
Decided as a team that we needed to identify the exact fit problems online shoppers are facing and how we can help them with these problems

2. Competitive Analysis
Conducted an analysis of other solutions that aim to solve the fit problems faced by online shoppers

3. Participant Criteria
Established the criteria participants must meet to take part in our study

4. Data Collection Methods
Decided which data collection methods to use based on the problem we are trying to solve and the questions we are trying to answer

Recruitment
Used a screening survey to:

Scheduling + Introduction
Sent an introductory email with a calendar invite for date and time of interview

Field Study
Conducted observational interviews with all participants using the following layout:

Survey
Collected 413 total responses to gain further insights into the specific fit problems faced by apparel shoppers

Fit Issues
The following graphs represent which articles of clothing respondents experience issues with fit. For more details on which aspects of these clothing articles give respondents the most trouble, visit this Mural Board.