Finding Jeans That Fit

Shopping for women’s jeans online can be challenging due to difficulties in comparison between styles and uncertainty of fit, leading to a frustrating experience with the possibility of having to return items.

I wondered if there could be a solution to this problem that most women (including myself) face when shopping for jeans online. 

This is a design brief from UX challenge that I undertook as a personal project to document my end-to-end design process. All information in this case study is my own and is in no way affiliated with Amazon.



The market value of denim fabric was $21.8 billion in 2020 and is expected to reach over $26 billion by 2026. Although in-store sales still dominate, online sales are growing, with a 32% increase in the past 12 months. Amazon, with 50% share of the US e-commerce market, aims to improve customer experience to drive further growth. However, the current shopping experience for women’s jeans on Amazon’s iOS app is lacking accuracy and convenience, leading to the goal of redesigning the experience to help customers find jeans that fit.

My role

I worked on this personal project in 2021 to improve the experience of customers who shop for jeans on Amazon. I dedicated 10-12 hours per week over the course of 4 weeks to the project. As part of the research phase, I conducted primary user research to gather data about customers’ experiences and expectations. To supplement this data, I also collected secondary data about the demographic and psychological factors that influence customers’ decisions to shop for jeans both online and in-store.

Through my analysis of the data, I uncovered valuable insights that helped me translate concepts into features that addressed customers’ behaviors and motivations. I created frameworks and high-fidelity mock-ups to bring my vision and strategy to life. I carefully considered both customer goals and business goals as I defined the product and prioritized features.

Finally, I created journeys, wireframes, and high-fidelity mock-ups for the Amazon iOS app.


Collecting primary data

At the start of this project, I had no specific goal other than to make the experience better for customers who shopped for women’s jeans on Amazon. With no access to pre-existing insights for Amazon customers, I decided to explore why and how women were shopping for jeans online on Amazon. 

Early insights

I interviewed 7 women about their opinions towards and experience with shopping for jeans on Amazon and in-store. My goal was to understand the challenges customers faced while shopping for jeans both online and in-store. The women I interviewed resided in Canada, USA, UAE, Australia and India. Each interview lasted 15-20 mins and consisted of open ended questions. 

“If I could find a good pair of jeans with deep pockets on Amazon, I’d save that product and pass on the details to my daughters and granddaughters so they too can buy jeans with pockets.”

“Colour, style, fit and quality are equally important when I buy a pair of jeans.”

“High-waisted jeans in a darker colour are my go-to style!

“I usually try the first pair in-store so I know my size, and then I order the rest online.”

“I find sizing charts useful sometimes, but sometimes the fit doesn’t feel right even when the measurements are right.”

“If they had a model for every body type that would make it easier for people to match the fit to themselves.”

“I try to avoid shopping for clothes and jeans online because the size and fit are hard to get right!”

“I have a pear shaped body and it’s so hard to find comfortable jeans even when I go to the store. Online is just impossible.”

Quotes from the women I interviewed to collect data about opinions towards and experience with shopping for jeans

Deep in the literature

For a more holistic understanding of the wider challenges faced by women when shopping for jeans online , I spent some time buried in academic research and literature. My research supported a lot of the points that were brought up during the interviews. 

"Fit" matters

Online is the least favourite shopping place for young women to purchase a pair of jeans. In a survey study of 163 teens and college women, 64% cited “fit” as the most important factor in making decisions to select or reject purchasing denim jeans. Teenagers, especially, reported fit problems in the entire lower body and expressed frustration at the failure of finding the right size for a pair of jeans.

The sizing conundrum

The women interviewed had anxieties about choosing the right size for jeans online. They felt limited by the use of models with a single body type, leading to difficulty in visualizing the fit. Sizing charts with measurement units in inches or cm caused confusion, as well as different sizing numbers used between countries and vague measurement ranges.

How does it look? How does it feel?

Important factors that affect purchase decisions were colour, style and quality. Customers often preferred to buy brands whose jeans had a reputation for quality, or the customers had had the opportunity to inspect the quality themselves either by visiting the store or through previous purchase. 


Just the tip of the iceberg

I interviewed 7 women and found that 5 of them had body types that aligned with the existing societal beauty standards. Despite this, all 5 of these women reported having concerns when shopping for jeans online because of the lack of a system that would allow them to predict the fit accurately. This made me wonder about the difficulties faced by mid-sized and plus-sized women, and women from ethnic backgrounds with different body proportions from the popular beauty standard. 


Internet innovation vs clothing innovation

I conducted further research to define the success criteria for improving the shopping experience for women’s jeans on Amazon. I analyzed customer reviews on the platform to understand what factors contribute to a successful shopping experience. My analysis indicated that online apparel buying is driven more by the innovativeness of the internet than by clothing innovativeness.

Accurate sizing that minimized returns

“Very Very stretching. Couldn’t find the right size. Im 5’2″ and about 130 lbs. I ordered a 12. Way too big. Then I ordered an 8. Still too big. I gave up. Returned all”

Customers get frustrated and disappointed when they have to return an item because it did not fit as expected. Customer experience could be improved by designing a solution that could predict fit with more accuracy. One of the metrics that could point to an improved shopping experience is a decrease in the percentage of returns.

Finding jeans that catered to specific body types

“These jeans are nice looking but too big for me. Ordered a 2 (26) the smallest size and it was too baggy on the bum and thighs. I have narrow hips…i’m 5ft7 120 lbs. I’ll try another style.”

Apparel companies often provide a certain style of jeans ignoring the existence of various body dimensions in the population. An improved shopping experience can be achieved by guiding customers towards styles that would be best suited to their body shapes. 

Should “feel” right

“I bought these based on the reviews I read. They were fine.. nothing to get excited about. They fit, but their not high wasted so I fell like I’m constantly pulling them up. They are stretchy but.. meh… Not my favorite, I wouldn’t buy them again. I definitely recommend going to a store to try on jeans, unfortunately…”

The selection process for a pair of jeans in-store involves touching the fabric of the garment and trying on the pair. A complicated multi-sensory, emotional and cognitive experience takes place in this process. An improved online shopping experience should aim to recreate the experience of shopping in-store. 

Taking the guesswork out of the purchase decision

Digging into primary and secondary data revealed bigger insights into the shopping experience. Almost all instances involved additional research by the customer and meticulous skimming through product reviews to identify if the jeans would be a good fit.

“These are huge! I actually tossed them out. Donated. I thought by the reviews they ran small and ordered a small. The length was perfect! I’m 5’3 105 lbs. The model wearing them in the description looks super tiny and they fit her perfect. I honestly think the xsmall would’ve been too big too.”

“I order a 27 in the 711 fit and they are so stinkin tight I can barely get them on… I order a 27 in the 710 fit and they fit perfect…. I don’t get it.”

“Step 1 size down. After buying a pair in my usual size then buying another pair a size down I’m certain that if you have a pear or hourglass shape, buy a size down than your usual if you want a more flattering snug hip fit. The material is stretchy enough to accomodate this. The waist will probably be far too loose.”

Conflicting reviews for the same pair of jeans leaves shoppers confused

This data showed that the experience was hardly the fast and convenient shopping experience that one would expect while choosing to shop online. Additionally, the time and energy spent returning an item was having a material impact on the business bottom line for both Amazon and third party sellers on Amazon.

Conflicting information between product specification, Amazon’s insights and reviews

Shoppers experienced frustration when product specifications and Amazon’s insights (such as the “Fit” insight) directed them towards one decision, but reviews from other customers revealed conflicting information. There was no way for a shopper to verify the information before they made their purchase online. 

Review showing shopper's experience with using an inefficient sizing chart

Inefficient filters

Amazon’s filters for women’s jeans are leg style, size (regular, petite, plus, waist), colour, brands, price, reviews, deals, sellers and availability. My research showed that high waisted jeans were a top preference for shoppers. While the current filter system has an option for leg style, it leaves out waist style, which is an important consideration for the target market. Vanity sizing has rendered the sizing filters inefficient and does not take into account different body shapes that can wear the same size. 

Amazon’s filters for women’s jeans

Lack of fashion knowledge

Many shoppers were unaware of what styles would suit their body types best. This led to shoppers making guesses about whether a pair of jeans would fit well and disappointment when the item was received and it did not meet expectations. 

Shoppers are unaware of the best fit for their body type


An information-deprived guessing game results in unsatisfaction with the purchased item.

The current filter system and sizing options exacerbates the confusion customers have while shopping for jeans online. Customers are confused by inconsistent sizing charts, overwhelming styles and ranging reviews to name a few. 

“…how might I help customers be better informed about how a particular pair of jeans would fit their body?”

This begged the question, how might I help customers be better informed about how a particular pair of jeans would fit their body, so they can make a confident purchase decision? 


Introducing "Try It On"

An AI powered visualizer tool that customers could use to see what a pair of jeans would look like on their body, along with filtered reviews of customers who had the same body type as their own.

What’s my size?

Before: 23 year old Allie tries to buy jeans on Amazon but is confused by the sizing chart and becomes frustrated. She decides to come back later, but ends up forgetting about the purchase while watching her favorite show on Netflix.

Sizing chart with conversions and measurements that leaves shoppers overwhelmed

After: 23 year old Allie wants to buy a pair of jeans on Amazon. She finds a pair she likes and tries to determine the right size. She measures her waist, hips, inseam, and length, enters the measurements into the app, and receives a size recommendation. She orders the jeans and goes back to watching her favorite show on Netflix.

Shoppers enter their measurements or use their saved measurements from their profile to get their perfect size

How would this look on me?

Before: Madhu, a 26-year-old, wants to buy a pair of jeans on Amazon but has concerns about the fit due to her body type. She looks at product photos but doesn’t find any models similar to her shape. She decides to buy the jeans in-store instead of online to avoid the hassle of returns if they don’t fit.

Photos are often not enough for shoppers to determine fit

After: 26 year old Madhu found a pair of jeans she liked but was unsure of the fit due to her unique body shape. She used the app’s measurement feature and saw a rendering of how the jeans would fit, with information on snugness around the hips and potential looseness around the waist. With this information, she felt confident in her purchase and is prepared to customize the waistband if needed when she received her item.

Visualizer tool with AI powered insights to help shoppers find jeans that fit

That’s what she said!

Before: Sophia finds a pair of jeans on Amazon that she likes and reads the reviews before purchasing. She is disappointed when the jeans arrive and do not fit as she expected. While leaving a review, she sees another review mentioning the jeans are made for curvier women. Sophia wishes she had seen this review before purchasing to avoid her disappointment.

Conflicting reviews for the same product

After: 25 year old Sophia finds a pair of jeans that she really likes and wants to order. She uses the app to input her measurements and check the fit. The app shows her a filtered feed of reviews from women with similar body proportions who bought the same pair of jeans. The reviews indicate that the jeans are too loose and not suitable for her body type. She decides not to purchase the jeans and continues browsing for a better option.

Filtered reviews that are relevant to the shopper


The project focuses on improving the shopping experience for women’s jeans on Amazon’s iOS mobile app as app users make up approximately 42% of Amazon’s smartphone visits, with a majority of mobile time spent with Amazon coming from app users.


Deliver an enjoyable shopping experience for women of all shapes and sizes

Three primary questions informed my design strategy:

  1. How do I design for everyone, everywhere?
  2. What contexts need to be considered?
  3. What does an enjoyable shopping experience look like?

Early on, it was important to understand the different factors that may influence the customer experience. I mapped all the possible concepts and translated this into the spectrums and situations framework.

Different factors that may influence customer experience (Click to enlarge)

A more inclusive design

I wanted to design a solution for the poorly designed apparel shopping experience on Amazon’s iOS app that takes into account the challenges faced by diverse women. To achieve this, I considered temporary or permanent challenges (spectrums) and situational challenges (situations) that the target customers may face. The goal was to create a scalable solution that could accommodate any combination of these challenges.




Concepts that did not make the cut

Better sizing charts

I considered creating comprehensive sizing charts that displayed the conversions between different metrics and brands. However, this solution seemed to exacerbate the existing problem of shoppers being overwhelmed by too much information that they do not know what to do with. 

Wireframe for a comprehensive sizing chart

Search filters for reviews

The existing review filtering system on Amazon’s iOS app allows users to filter by pre-displayed keywords and by height or weight. However, height and weight are not accurate indicators of fit, and creating a filter by self-described body type keywords is difficult to implement due to the debates and variations in fashion terms.

Search filter within reviews

Fit Analytics

I researched intuitive size advisors such as fit analytics to understand if incorporating a similar tool would make an impact. One issue with such tools was that they provided fit advice based solely on height, weight and belly size. I found this to be a poor solution as height and weight are not the most accurate predictors of how an item would fit. I wondered if I could add some modifications to this concept to bring in a higher level of accuracy. 

Screenshot of the FitAnalytics website

Working backwards from perfect

I reversed the polarity of the poor shopping experience to jumpstart creativity. Three key design challenges emerged:

  1. How might I remove the need for reviewing sizing charts entirely?
  2. How might I predict fit for different body shapes, sizes and compositions?
  3. How might I better inform the customer of the experiences of other shoppers with a particular product in an efficient manner?

Inspiring confidence to buy jeans online

The current shopping experience for jeans on Amazon was difficult for customers due to overwhelming information and decision-making based on guesswork. Research insights revealed a need for improved sizing information and more relevant reviews. To address these issues, the author proposed three key features: Size Match, Fine Fit, and Smart Reviews to mimic an in-store shopping experience and make the shopping process easier for customers.


I used wireframes as a tool to help me illustrate user flow. While wireframes are great for visualizing individual screens, their static nature can’t effectively illustrate movement. Conversely, user flow charts are good at showing movement, but they aren’t complex enough to convey what each step will look like. So by using a wireflow, I was able to evangelise both the appearance and functionality of my ideas.

(Click to enlarge)

Step 1: The shopper lands on the page of a pair of jeans they like and now has to choose their size. They click on “What’s my size?” Alternatively, they can click on “Try it on” and skip to Step 4.

Step 2: Clicking on “What’s my size?” takes them to a page where they can enter their measurements to get their size for the pair they want to purchase. If they do know their measurements, they will need to measure their proportions to find out their size. From here the shopper can click on “Try it on” and move to Step 4.

Step 3: If they do not know what area of their body to measure, clicking on “How to measure” will take them to Step 3, where they will receive detailed information on how to get their measurements. From here the shopper can go back to “What’s my size?” and enter their measurements. 

Step 4: In the “Try it on” step, the shopper can view a rendering of what the selected pair of jeans would look like on their body measurements. In this step, the shopper can adjust measurements, view smart recommendations about the fit of the item and view a filtered list of reviews from other shoppers with the same or similar body measurements. 

Finding jeans that fit

Shoppers did not expect that every single pair of jeans then purchased online would be perfect. However, they did expect to have enough clear information to make an informed purchase to reduce the likelihood of receiving jeans that did not fit right.

Problem: Size charts suck

Even the most easy-to-use size charts that compare different standards (including measurements in inches and centimeters) can leave customers feeling overwhelmed and confused. Sizing charts that use diverse model photos and specify model measurements are still inadequate as they cannot possibly cover all body shapes and sizes. 

Solution: Size Match-Reverse sizing chart

Let the customer lead the process. By allowing the shopper to enter their measurements, and providing them with the size that would be ideal for them, I removed the frustration that they face while trying to figure out their sizes. 

Shoppers could also save their measurements to their profile so they do not have to re-add their measurements to every single search or product.

The Size Match feature comes with video and text instructions for shoppers who do need to take new measurements.

Problem: Recognizing diverse body shapes and sizes

A pair of jeans would fit differently on a pear shaped body and a straight body of the same height and weight. Model images often don’t display this diversity making it difficult for shoppers to visualize how a selected pair of jeans would fit their body. The current “fit” insight that is displayed in the app shows a breakdown of what other customers thought of the fit of their bodies. But, customers often left conflicting feedback, which added to the shoppers’ confusion. 

Solution: Fine Fit-Accurate and verifiable fit predictions

A body visualization tool that uses a statistical model of human body shape created from thousands of detailed laser range scans of human bodies. This tool takes into account how the human shape varies with age, weight, gender, etc. to create a mathematical model of body shape variability. Shoppers are able to adjust proportions to best match their body shape and view how the selected pair of jeans would fit their body. This tool also gives the shopper smart recommendations on what styles or sizes would suit their body better.

Problem: Unreliable Reviews

Shoppers often read through reviews before buying a pair of jeans. However, often the number of reviews was too high and it was a time-consuming ordeal for shoppers to go through reviews that applied to them. For example, a woman with a pear shaped body would be more interested in hearing from others with a pear shaped body who purchased the jeans. 

Solution: Smart Reviews-Show reviews that matter

Based on the measurements entered in Size Match and Fine Fit, users would be able to review a filtered list of reviews from people who have their measurements and purchased the jeans. This cuts down on the hours spent scouring through reviews to find ones that apply to the shopper. 


The project has limitations, including a small sample size for user research, focusing only on the shoppers’ experience and not considering the sellers on Amazon, and not conducting usability tests. The insights were based on the experiences of educated women in their twenties and do not provide insight into other age groups or demographics. A lack of resources limited the development of a working prototype for testing.

Personal note

I believe that the future of e-commerce will be influenced by immersive experiences. Although previous studies on the use of avatars in online shopping were found to be a turnoff to customers, I hypothesize that current body positivity movements may have changed customers’ perception towards avatars and immersive shopping.