Blog
The latest news from Google AI
Simple is better - Making your web forms easy to use pays off
Monday, July 14, 2014
Posted by Javier Bargas-Avila and Mirjam Seckler, User Experience Research at Google
Imagine yourself filling out a long and cumbersome form on a website to register for a service. After several minutes of filling out fields, coming up with a password, and handling captchas, you click the submit button to encounter your form filled with red error messages. Suddenly the “close tab” button seems much more tempting than before.
Despite the rapid evolution of the Internet, web forms, with their limited and unilateral way of interaction, remain one of the core barriers between users and website owners. Any kind of obstacle or difficulty in filling in online forms can lead to increased frustration by the user, resulting in drop-outs and information loss.
In 2010, a set of
20 guidelines to optimize web forms
was published by researchers from the University of Basel in Switzerland, including best practices aimed to improve web forms and reduce frustration, errors and drop-outs. For instance, guideline no. 13 states that if answers are required in a specific format, the imposed rule should communicated in advance; or no. 15 that states that forms should never clear already completed fields after an error occurs.
To investigate the impact of applying these rules, we conducted a study and presented our results at
CHI 2014
:
Designing usable web forms: empirical evaluation of web form improvement guidelines
. In the study, we examined a sample of high traffic online forms and rated them based on how well they followed the form guidelines outlined by the 2010 publication. We then selected three different online forms of varying qualities (low, medium and high), and improved them by applying the guidelines, with the high quality form needing less modification than the medium and low quality forms. We then tested both the original and improved forms extensively with 65 participants in a controlled lab environment.
In our study, the modified forms showed significant improvements over the original forms in the time users needed to complete a form, an increase in successful first-trial submissions and higher user satisfaction. As expected, the impact was highest when the original form was of low quality, but even high quality forms showed improved metrics.
Furthermore, user interviews with participants in the study revealed which guidelines were most impactful in improving the forms:
Format specifications (e.g., requiring a minimum password length) should be stated in the form, prior to submission. The application of this guideline had a large positive impact on user performance, subjective user ratings and was also mentioned frequently in user interviews.
Error messages must be placed next to the erroneous field and designed in a way users are easily able to fix the problem. Doing this reduced form-filling time and increased subjective ratings.
Most frequently users mentioned that it was key to be able to tell apart optional and mandatory fields.
Example Guideline: State format specification in advance
Example Guideline :Place error message next to erroneous fields
Example Guideline: Distinguish optional and mandatory fields
Putting field labels above rather than adjacent to the fields in the form led also to improvements in the way users scanned the form. Using
eye-tracking technology
, our study shows that users needed less
number of fixations
, less fixation time and fewer
saccades
before submitting the form for the first time.
Scan path for an original and improved form
From our study, we conclude that optimizing online forms is well worth the resource investment. With easy to implement actions, you can improve your forms, increase the number of successful transactions, and end up with more satisfied users. Google is currently working on implementing these findings on our own forms.
We wish to thank our co-authors at the University of Basel, Switzerland for their collaboration in this work: Silvia Heinz, Klaus Opwis and Alexandre Tuch.
Labels
accessibility
ACL
ACM
Acoustic Modeling
Adaptive Data Analysis
ads
adsense
adwords
Africa
AI
AI for Social Good
Algorithms
Android
Android Wear
API
App Engine
App Inventor
April Fools
Art
Audio
Augmented Reality
Australia
Automatic Speech Recognition
AutoML
Awards
BigQuery
Cantonese
Chemistry
China
Chrome
Cloud Computing
Collaboration
Compression
Computational Imaging
Computational Photography
Computer Science
Computer Vision
conference
conferences
Conservation
correlate
Course Builder
crowd-sourcing
CVPR
Data Center
Data Discovery
data science
datasets
Deep Learning
DeepDream
DeepMind
distributed systems
Diversity
Earth Engine
economics
Education
Electronic Commerce and Algorithms
electronics
EMEA
EMNLP
Encryption
entities
Entity Salience
Environment
Europe
Exacycle
Expander
Faculty Institute
Faculty Summit
Flu Trends
Fusion Tables
gamification
Gboard
Gmail
Google Accelerated Science
Google Books
Google Brain
Google Cloud Platform
Google Docs
Google Drive
Google Genomics
Google Maps
Google Photos
Google Play Apps
Google Science Fair
Google Sheets
Google Translate
Google Trips
Google Voice Search
Google+
Government
grants
Graph
Graph Mining
Hardware
HCI
Health
High Dynamic Range Imaging
ICCV
ICLR
ICML
ICSE
Image Annotation
Image Classification
Image Processing
Inbox
India
Information Retrieval
internationalization
Internet of Things
Interspeech
IPython
Journalism
jsm
jsm2011
K-12
Kaggle
KDD
Keyboard Input
Klingon
Korean
Labs
Linear Optimization
localization
Low-Light Photography
Machine Hearing
Machine Intelligence
Machine Learning
Machine Perception
Machine Translation
Magenta
MapReduce
market algorithms
Market Research
Mixed Reality
ML
ML Fairness
MOOC
Moore's Law
Multimodal Learning
NAACL
Natural Language Processing
Natural Language Understanding
Network Management
Networks
Neural Networks
NeurIPS
Nexus
Ngram
NIPS
NLP
On-device Learning
open source
operating systems
Optical Character Recognition
optimization
osdi
osdi10
patents
Peer Review
ph.d. fellowship
PhD Fellowship
PhotoScan
Physics
PiLab
Pixel
Policy
Professional Development
Proposals
Public Data Explorer
publication
Publications
Quantum AI
Quantum Computing
Recommender Systems
Reinforcement Learning
renewable energy
Research
Research Awards
resource optimization
Robotics
schema.org
Search
search ads
Security and Privacy
Self-Supervised Learning
Semantic Models
Semi-supervised Learning
SIGCOMM
SIGMOD
Site Reliability Engineering
Social Networks
Software
Sound Search
Speech
Speech Recognition
statistics
Structured Data
Style Transfer
Supervised Learning
Systems
TensorBoard
TensorFlow
TPU
Translate
trends
TTS
TV
UI
University Relations
UNIX
Unsupervised Learning
User Experience
video
Video Analysis
Virtual Reality
Vision Research
Visiting Faculty
Visualization
VLDB
Voice Search
Wiki
wikipedia
WWW
Year in Review
YouTube
Archive
2020
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2019
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2018
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2017
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2016
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2015
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2014
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2013
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2012
Dec
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2011
Dec
Nov
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2010
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2009
Dec
Nov
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2008
Dec
Nov
Oct
Sep
Jul
May
Apr
Mar
Feb
2007
Oct
Sep
Aug
Jul
Jun
Feb
2006
Dec
Nov
Sep
Aug
Jul
Jun
Apr
Mar
Feb
Feed
Follow @googleai
Give us feedback in our
Product Forums
.
