Finding Jeans That Fit

Shopping for women’s jeans online could be time-consuming and frustrating. It’s hard to compare between different styles, and it’s hard to know for sure how they will fit. It’s a disappointing experience when you have to return an item that doesn’t fit quite right. 

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.

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Background

The market value for denim fabric was 21.8 billion U.S. dollars in 2020 and was expected to increase to over 26 billion U.S. dollars by 2026. While in-store sales still account for an overwhelming majority of women’s jeans sold – 80 percent in the last year – these purchases are on the decline. Online sales are driving growth in the market, with a 32 percent increase in the number of women’s jeans being purchased through e-commerce channels in the past 12 months.

Amazon enjoys nearly 50% share of the US e-commerce market and owns more than 90% of the global market share in 5 product categories, one of which is clothing. Amazon is constantly working on increasing its product and category coverage on the platform to attract new types of customers and various different niches. However, product coverage is not the only factor that defines Amazon’s success. Customers often cite experience based factors such as convenience, easy returns and speed of delivery as important reasons to use the platform. As online purchasing grows in importance, understanding and improving customer experience is an important criteria for further business growth. 

While the current shopping experience for women’s jeans on Amazon may have been revolutionary a decade ago, it currently struggles to keep up with the level of accuracy and convenience that customers expect from modern day technology. The goal of this project was to redesign the current customer experience on the Amazon iOS app to help customers find women’s jeans that fit. 

My role

I worked on this project in August 2021. I dedicated 10-12 hours per week for 4 weeks to this project.

User research

I conducted primary user research to collect data about the experience and expectations of customers who shop for jeans on Amazon. 

Customer insights & ideation

I supported my primary user research with secondary data that I collected about the demographic and psychological characteristics that impacted customers’ decisions to shop for jeans both online and in-store. I analysed the data to uncover insights and translate concepts into features that addressed customer behaviours and motivations.

Experience strategy & vision

I created frameworks and high-fidelity mock-ups to develop ideas and give life to my vision and strategy. 

Planning & scope definition

I defined the product keeping in mind customer goals and business goals. I prioritised features that will be executed in this project and banked valuable concepts that could be explored in the future.

Design execution & validation

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

KICKOFF

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 I interviewed reported getting anxious while choosing sizes online. They worried the item wouldn’t fit after all. They complained about seeing models who catered to only one body type and were unable to visualize what the jeans would look like on them in the right size. The women also complained about how sizing varied according to brand. “I’m a size 14 in H&M jeans and a size 12 in Zara, and my H&M jeans are still tighter than my Zara jeans.” There were multiple issues with sizing charts:

  1. Size charts often show measurements in inches or cms, while women often remember their size numbers (size 6, size 14 etc). 
  2. These sizing numbers differed between countries. A size 8 in women’s jeans in the US is a size 12 in the UK.
  3. Sizing often showed a range measurement (S=23-25cm), and this further confused customers on what size would fit them best.

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. 

THE DISCOVERY

Just the tip of the iceberg

Out of the 7 women I interviewed, 5 had body types that matched or closely aligned with the existing societal beauty standards. All 5 of these women reported having reservations when it comes to shopping for jeans online due to the lack of a system that would allow them to accurately predict the fit of the jeans they order. If women who fit into societal beauty standards were having trouble finding jeans that fit, what about mid-sized and plus sized women? What about women from ethnic backgrounds whose body proportions differed vastly from the popular beauty standard?

Curiosity revealed an opportunity to perfect the online jeans shopping experience for women of all sizes and body shapes. I had identified a direction for my project.

DEEPER INSIGHTS

Internet innovation vs clothing innovation

Before I could jump into designing, it was important to define success and understand the health of the shopping experience at scale. I wanted to understand what the metrics were to measure the quality of the current shopping experience on Amazon. As I did not have access to this data, I scoured through reviews on Amazon on the women’s jeans section and made some informed assumptions on what factors would define a successful shopping experience. My analysis pointed to the idea that online apparel buying is motivated more by internet innovativeness 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

REFRAMING THE PROBLEM

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? 

IMPROVING THE EXPERIENCE

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 wants to buy a pair of jeans on Amazon. She finds a pair that she really likes and wants to order it in the right size. She checks the sizing chart and is confused and overwhelmed by the information on the sizing chart. She tries to figure it out, but gets frustrated and decides to come back to it later. She resumes her favourite show on Netflix and later forgets about the jeans she wanted to buy.

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 that she really likes and wants to order it in the right size. She gets out her measuring tape and takes an accurate measurement of her waist, hip, inseam and length. She enters these measurements into the app and it tells her exactly which size she needs to order. She places her order and resumes her favourite 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: 26 year old Madhu has a thin frame with wide hips. She finds a pair of jeans that she really likes but wonders how it would fit her. She checks all the product photos to see if they have any models resembling her body type. She finds a photo of a plus size model wearing the pair, but the model is much bigger than she is and Sophia is still unsure if the selected jeans would fit her right. She is anxious about buying the pair online because she doesn’t want to deal with the headache of having to return the pair if it doesn’t fit right. She decides to drop by her favourite store over the weekend and buy the jeans in-store so she can try it on. 

Photos are often not enough for shoppers to determine fit

After: 26 year old Madhu has a small waist with wide hips. She finds a pair of jeans that she really likes but wonders how it would fit her. She enters her measurements into the app and views a rendering of how the jeans would fit her body. The app also informs her that the fit around her hips would be snug due to the stretchy material, however, the waist might be loose as her waist proportions are rather small. Sophia is pleased with this information and is prepared to customize the waistband to her size when she receives her item. She confidently places her order. 

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

That’s what she said!

Before: 25 year old Sophia finds a pair of jeans that she really likes and wants to order. She goes through the reviews to make sure that it’s a quality product and sees a couple of reviews about how the jeans fit so well. She is excited and orders a pair. When her jeans arrive, she tries them on and is disappointed because they’re rather baggy and that’s not what she expected. She returns the jeans and opens up Amazon to leave a review. As she’s about to leave her review she notices a review from another customer who said that the jeans are made for curvier women and that it feels like a diaper on the buttock area for less curvy women. Madhu wishes that she read this review before she placed her order, so she could have avoided some 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 enters her measurements to find her size and check fit. The app provides her with a filtered feed of reviews from women with similar measurements who purchased the same pair of jeans. Most of the reviews point out that the jeans are rather baggy and it seems like they were made for curvier women. Sophia realizes that this is not the right jeans for her and continues browsing through other pairs. 

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Filtered reviews that are relevant to the shopper

Specifications

App users account for approximately 42% of Amazon’s smartphone visits, while visits to Amazon.com make up the other 58%, but those app users take almost 85% of the mobile time spent with Amazon. This suggests Amazon performs better on mobile with app users, which should influence its strategy in the valuable and growing mobile commerce space.

For this project, I wanted to focus my time and efforts on designing my solution for Amazon’s iOS mobile app. 

HOW I GOT THERE

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

The existing apparel shopping experience was poorly designed for users who weren’t reflections of beauty standards in western society. To move beyond the existing biases, I tried to educate myself with an approach to designing for diverse women.

The spectrums attempt to highlight the range of temporary or permanent challenges to consider when a person is interacting with the Amazon iOS app.

The situations attempt to highlight situational challenges that all target customers experience. A situation is a temporary context that affects the way a customer interacts with the Amazon app for a short time.

The goal was to design a solution that could scale and extend to any combination of these contexts from the outset.

Spectrums

Situations

THE DRAWING BOARD

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

Currently, the reviews can be filtered by certain pre-displayed keywords and by height or weight. However, height and weight are not accurate predictors of fit. I considered creating a review filter where shoppers could type in keywords that they would use to describe their body. However, this solution seemed problematic as it would be difficult to predict what words shoppers would use to describe themselves. For example, a popular debate in fashion terms is the use of mid-size vs plus size. Some brands categorize size 12 as plus-size while others say plus-size is 14+. However, there is a rising view that size12 and 14 are mid-size and should have a separate category for themselves.  

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

A major reason customers were having an unpleasant experience while shopping for jeans on Amazon was that they were overwhelmed by the amount of information available in a product page yet at the same time they were making decisions based on guesswork. 

My research insights revealed that:

  1. Although some customers found sizing charts useful, a majority of them were overwhelmed by the amount of information or frustrated by the lack of information in sizing charts.
  2. While sizes may match the customers’ body measurements, the “fit” was often based on how comfortable the customer felt.
  3. Customers were overwhelmed and/or frustrated by the number of reviews they had to read to make an informed decision, especially if reviews were contrasting with the product description or with each other.

Based on these insights, I arrived at three key feature ideas: Size Match, Fine Fit and Smart Reviews to help mimic an in-store shopping experience and help customers focus their search. Central to the these features were these key ideas:

  1. Stop expecting customers to figure out their size from a table of measurements. Do the heavy lifting for them and give them their perfect size.
  2. Visualize the fit of a pair of jeans for the customer so they are able to make an informed purchase.
  3. Save customers’ time by showing them the exact information that is relevant to their purchase.

Wireflows

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 centimetres) 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. 

Limitations

This project has notable important limitations:

  1. I used a very small sample size for my user research and supported this data with secondary data. Most of my insights were based on the experiences shared by educated women in their twenties. My research does not provide any insight into other age groups or demographics.
  2. The solution I have designed accounts for the shoppers’ experience only. It does not take into account the sellers on Amazon. The changes I have proposed may cause disruptions in their workflows and account for a poor seller experience. 
  3. Lastly, I have not run any usability tests on this project. One important reason was that I did not have the resources to build a working prototype that I could have guerilla tested in the early stages. In a real case scenario, I would have had an early stage prototype built and guerilla tested it and reviewed the direction of the user experience based on the new insights. The prototype at the later stages would have undergone lab usability testing to  collect in-depth information on how real users interact with the new features and what issues they face.

Personal note

I believe that the future of e-commerce will be guided by immersive experiences. In 2018, I worked on a 5-day design sprint to design a UI prototype for a social/virtual fashion shopping app. The concept of immersive e-commerce experiences is something that continues to fascinate me. Fashion media and academics have predicted that Clueless-style avatars would help sell customers on style and fit when shopping online. However, researchers at the London College of Fashion found that using an online shopping avatar or a rendering of the customer’s own body would frequently discourage purchases and that an avatar “often acts as a turnoff to the user.” However, this study was conducted in 2014, and I hypothesise that new age body positivity movements that celebrate natural bellies and ethnic features may have changed shoppers’ perceptions towards avatars and immersive shopping.