Building a high-performance website starts with site planning and architecture. Unfortunately, for many development projects, site speed is overlooked because the responsibility spans across different roles and page speed wasnât always a Google quality standard.
As of July 2018, Google announced a new ranking algorithm known as âThe Speed Updateâ where Google will look at how fast your mobile pages are and use that as a ranking factor. Â Even though this update focused on mobile experiences, if history is any indicator, the expectation will extend to desktop experiences as well.
While site speed should be baked into an entire development project, you may need to find ways to retroactively fix it.
Why do you need to fix this immediately?
As Ricky Bobby says, âif you ainât first youâre last.â
Would you rather be proactive or reactive? The former gives you a competitive advantage, and itâs one we recommend. Google rewards sites that apply best practices when it comes to site development.
Ignoring site speed is becoming more hurtful than ever. First of all, your users expect your site to load quickly. Site performance plays a major role in engaging and retaining users. If you run your business on the web, performance is crucial. If you canât retain your users then they wonât convert, which ultimately has a negative impact on your revenue.
Donât take our word for it. Take a look at what Google has to say about site speed and how poor site performance impacts paid and organic visibility.
Fast websites generate more revenue.
Google Ads Impact
According to Google studies, sites that load within 5 seconds vs 19 seconds:
Drive double the mobile revenue. Mobile users are impatient! They are much more likely to convert on a fast page
Gain 25% in ad viewability. Google takes your landing pages into consideration; they are more likely to serve your ads if your page speed is fast.
Have 60% more page views, increasing retargeting opportunities. If your website loads quickly, people are more likely to stay on your website and browse around.
Google Organic Impact
1 second delay in load time loses 11% pageviews. Fast websites get more organic entrances and keep users on the site longer.
July 2018, Google announced page speed will be taken into account for their ranking algorithm. And page speed is only gaining importance as user expectations increase.
3 Marketing insight leaders emphasized the importance of page speed.Hubspot, Backlinko, and Optinmonster, listed site speed as a top-ranking factor for Google.
UX & Conversion Rate Impact
As organic rankings and engaging ads bring traffic to your website, page speed will facilitate conversions by enhancing UX.
1 second delay in load times loses 7% of conversions.
As page speed increases from 1 to 5 seconds, the probability of bounce increases by 90%.
Atlantic BTâs Proven Quick Wins
Reduce the number of third-party scripts
Caching and minification
Optimize images
Lazy load images
Serve content from a Content Delivery Network (CDN)
Remedy redirect chains
Need help with page speed?
If you’re interested in learning more about the impact of page speed and see how you can fix it, we’re here to help. We’ve brought many websites up to speed, including our own! Learn more about our Site Speed Quickstart, or reach out for a free consultation.
As I was watching the AWS re:Invent 2019 keynote addresses and product releases, I was struck by a realization, namely, that machine learning isnât some science fiction future yet to come – itâs already here, if you know where to look and how to use it.
Machine Learning is increasingly available, but some approaches are easier than others.
Our clients are starting to ask more and more about implementing machine learning into the solutions we provide for them. They do this because they have heard of the increasing ability of machine learning to enable automation of tasks that, until recently, could only be performed by human intelligence. The cost and time required for humans to perform these tasks meant they were often too expensive or couldnât be offered in real-time – for example, document translation services.
What you may not know is that many services leveraging machine learning (ML for short) are already available. For example, Amazon Web Services (AWS) is continually developing and expanding a broad range of technology services – we watch their annual re:Invent conferences very carefully to learn more about their new offerings. In fact, AWS re:Invent 2019 introduced or expanded twenty ML based services!
We categorize ML solutions into two models.
I like to think of these services in two broad categories: âReady-to-Useâ and âBuild-Your-Ownâ models. Why do I make this distinction? It comes down to what machine learning involves.
Think about what âlearningâ entails for a human: years of experience, from crawling to graduate school; feedback in forms ranging from trial-and-error to peer review; and the sheer repetition involved to internalize what we learn.
The process with machines is fundamentally the same. It takes large amounts of raw data, intense processing, and guidance to develop the algorithms. For humans, this takes years of full-time processing by the human brain. For machines, the effort required is comparable – developing effective machine learning is no small task!
For this reason, the ready-to-use models are the ones that excite me the most. In these cases, the data gathering, algorithm development, and validation have all been done for you.
Think of all the login captcha images youâve identified over the years. You were âtrainingâ a machine learning algorithm.
Which Machine Learning services are easy to implement?
Being an AWS Certified Partner, we use many of the ML enabled services from Amazon Web Services. These are just a few:
Comprehend – topic, sentiment, and relationship analysis of text.
Transcribe – automatically convert speech to text.
Translate – natural and accurate language translation.
Polly – turn text into lifelike speech.
As you can see from these examples, these are broadly applicable services that could be developed from widely available data sources and input for training the models. Being broadly applicable, there’s a good chance one of these could be useful for your business. Fortunately, these services are ready to use and integrate in your applications.
If you have a very specific task for a limited use case, you will likely need to use the Build-Your-Own model. As with building anything, you need the appropriate tools and techniques. Amazon Sagemaker is a tool designed for just that purpose. Frankly, building your own ML model is a complex topic beyond the scope of this post.
If you would like to learn more about how to leverage the Ready-to-Use services, watch for my next two posts in this series revolving around these topics:
When working towards winning enterprise, government, and higher ed projects; smart organizations will only consider your services after reviewing past results. Itâs in their best interest to make sure you have a proven process that serves the requirements of an organization their size.
But sometimes, the most relevant examples of your work will be with an anonymous partner.
You could simply stick to writing about businesses that allow you to use their name. Or, you could learn how to impactfully write anonymous case studies. Your portfolio will ideally have a healthy mix of both.
Our 5 tips for writing anonymous case studies are:
Lead with results.
Come up with a name for reference.
Use this name as little as possible.
Take advantage of anonymity.
Give very specific, tangible details.
When writing anonymous case studies, you have three goals.
Case studies are ultimately about building trust. But it can be challenging to build trust when you are hiding details. Your goals are to:
Reduce suspicion that you are making this case study up.
Prove that you have a game plan by showcasing your processes.
Demonstrate that your processes get results for large brands.
Achieve these goals by following our 5 writing tips.
1) Lead with results. In order to distract from the fact you are not giving a name, focus on the results at the very beginning of the case study. You can do this in a summary format early on. Then, go into careful detail of the results at the end of the case study, where they naturally belong.
2) Come up with a name for reference. You will have to refer to the business as something. You may choose to call it âthis city,â âthis South Carolina university,â or âthis vehicle brand.â Whatever you choose, you want it to be as specific as possible without giving away the brand (or over-hinting in a way that could annoy your client).
3) Use the reference name as little as possible. You do want to use a descriptive name to give readers a frame of reference in the beginning. However, overusing these pronouns can be awkward and remind the reader that you are hiding an identity. To reduce use of these names, structure sentences to refer to the project and work itself rather than the business:
The goal of this redesign project was to cater to predefined personas.
A new website would grow the brand and unlock new B2B opportunities.
A new system would allow for more administrative efficiency.
4) Take advantage of anonymity. A common reason for clients to disprove case studies is they paint the company in a bad light.
Since thereâs no name tied to this content, you can get into all the nitty gritty details without embarrassing anyone. Was the project a total mess when you first came in? You can unveil all the business issues the company was facing, which can provide context that will enhance end results.
5) Give very specific, tangible details. Detail your process as much as possible. Talk carefully about each step you took to solve the problem, what methods or technologies were involved, and what specific client challenges you tackled.
Images are another way to tangibly show credibility. Best case scenario, use any visuals or pictures of your team working on the project that you can. Be careful to remove identifying logos, names, images, and data.
An example of showing a project deliverable without revealing the company’s identity.
Instead of rounding when presenting final results, use exact numbers for percentages increased, ROI, and any other metrics.
An example of one of Atlantic BTâs anonymous case studies.
In Atlantic BTâs case study about a redesign project (no longer available), we direct readersâ focus away from anonymity by highlighting the results right after the intro. We refer to the project as âa pharmacy schoolâ early on, later using language that speaks directly to the project itself rather than calling out a name. For example:
The new website included enhanced features and a scalable content system that supported growth.
We then clearly define the work implemented, getting into details like card sorts, 3 methods for testing, and direct quotes from survey takers. These specific quotes act as metrics of success:
âItâs pretty clean. I like that there is a lot of space. Itâs breathable.”
âIt looks cleaner and a lot less chaotic.â
We go into further detail by mentioning the original technologies used by this school and how we transformed the website with Advanced Custom Fields in WordPress.
Anonymous case studies donât always work.
Many readers are here to skim, or just review a logo and images. In these cases, your hard work will do little to build authority with these readers. While you should never rely on anonymous case studies to build full confidence in a reader, they can enhance your existing portfolio of success stories.
You may also consider using them as a sales tool to send to prospects you are already conversing with. In that situation, you already have some buy-in.
Ready to grow your business strategy?
Writing compelling case studies is a small piece of your content strategy. If you need support with market research, business process analysis, or digital strategy; our team is happy to help you get started. Contact us for a free consultation.
How often have you found yourself frustrated when answering a survey? Perhaps you were not presented with an option that covered your case or enabled you to raise your concern. Maybe you wished for a place to provide more detailed information.
In either of these situations, that firm could not get useful information to improve your experience with them.
Why Should I Include More Open-Ended Survey Questions?
While multiple choice responses are straight-forward to analyze with clear trends in responses, it only leaves room for answers to questions that the survey writer anticipated. This is okay for some questions, such as yes or no, how many times, Likert ratings, or questions with only a few possible responses.
For other questions, like âhow do you feel about our product?â, it’s nearly impossible to anticipate any adjective a person would want to use.
Furthermore, with multiple choice for such a question you are limiting responses in a way that manipulates data. You could lead the survey taker into submitting a misleading response by forcing their selection into predetermined categories.
Multiple choice questions can help you identify a problem, but they rarely provide enough insight to help you solve the problem.
Open-ended questions allow respondents to provide answers in their own words, focusing on what is important to them. With no restrictions on their response, you can identify new issues that you would not have thought to include in your questions.
In addition, this kind of open text feedback will often contain information about context (in which circumstances an event occurred) and additional detail (exactly what happened).
The Challenge With Open-Ended Question Analysis
While open-ended questions can provide a wealth of meaningful information, it takes a great deal of time to analyze them properly. In fact, User Researcher and founding partner of Adaptive Path Indi Young, plans for 8 to 10 hours of analysis time for every hour of recorded interviews or text read at natural speed. We have found this estimate to be realistic.
Why does it take so long? It takes time because you donât know what you are looking for – you will know the valuable nuggets when you see them, but only analyzing all the data will provide the patterns to reveal them. To do this, you have to:
Go through every word in the responses
Identify the topics that are mentioned
Identify the labels people are using to distinguish those topics
Map different labels people use for the same things
Repeat the process for adjectives and modifiers
Identify how they feel about these topics, positive, negative, or neutral
Discern contexts that clarify the meanings
Extract relevant details that can be used in developing solutions
This process may seem like overkill – if you have a dozen or two short responses most people can read through them and take away one or two key points. However, if you have hundreds of responses, or the respondent can go into detail and provide longer answers, then you rapidly obtain more information than can be usefully processed merely by reading through them.
A structured analysis, aggregating the detailed responses from many participants, can reveal insights that might easily be missed in small samples. However, few firms have the resources to provide that kind of analysis on hundreds or even thousands of responses.
When to Incorporate Natural Language Processing for Surveys
Fortunately, machine learning-enabled algorithms have developed to the point where much of this analysis can be automated. The process is called Natural Language Processing, or NLP for short. While it canât do everything listed above, NLP can be of great assistance in two major areas: 1) Topic Analysis (what people are talking about), and 2) Sentiment Analysis (how they feel about those topics).
Using NLP to perform that preliminary work of topic and sentiment analysis can give the research team a great head start and allow them to instead focus on what human experts do best – assimilate those results and then look at the contextual information and details to glean valuable insights. Furthermore, it reduces human error and bias.
A Real-World Example With Amazon Comprehend
During the Discovery phase of projects, Atlantic BT frequently uses surveys to conduct user research. Recently, we needed to analyze responses in a survey performed as a part of brand research for a pharmacy school.
In this instance, Atlantic BT was working with 800 responses from hundreds of participants. At an average of one minute per response, simply reading through all these would take 13.5 hours, or two full days. And thatâs before performing any analysis – remember the point above about proper analysis taking 8 to 10 times longer? That would mean that a fully manual analysis of that content would take three weeks!
Instead, we chose to use Natural Language Processing to perform the basic topic and sentiment analysis, which allowed our research team to rapidly identify key areas to focus on and research more fully. We chose Amazon Comprehend as the NLP tool to use.
Why We Chose Amazon Comprehend
Amazon Comprehend is a service that uses machine learning to draw insights from text. You could use this tool to identify positive or negative connotation or to pick out specific phrases within responses. According to Amazon, full capabilities include:
Identifying the language of text
Extracting key phrases, places, people, brands, or events
Understanding how positive or negative text is
Analyzes text using tokenization and parts of speech
Automatically organizes a collection of text files by topic
Building custom sets of entities or text classification models that are unique to your organization
As Atlantic BT is an Amazon partner, we find that Amazon Comprehend is compatible with our other toolsets, is continually being improved, and is very cost effective.
What We Learned Through Natural Language Processing Analysis
Once the full analysis was complete, Atlantic BTâs user research team was able to draw conclusions that helped drive a website redesign and content strategy.
Eight major topics were identified as reasons for wanting to attend this pharmacy school. Further research, such as cross-validating these insights with other sources such as search terms, Reddit and other methods, enabled us to refine our insights around these topics. Understanding the motivation behind prospective students in selecting a school and program is critical to boosting the conversion rate of these low-volume, high-value transactions of both applying to a school and finally selecting that school from those that approved their application.
Just a few examples of the insights gained include:
Deep Motivations: While things such as national rankings are of obvious importance, we learned more about how motivations and decisions were shaped by a key influencer in the applicantâs life; the stories related in the responses were extremely helpful in identifying content topics which would resonate with and reinforce those motivations. These factors often influence decisions around programs and schools to which they will apply.
Natural Environment: While not necessarily something one would think about in selecting a pharmacy school, the comments made it clear that proximity to a lake and other outdoor activities was a differentiator for many applicants. Factors like this can make a large difference in turning an offer into an acceptance – which is very important when most applicants have been accepted by multiple schools.
Multiple Value Propositions: Students must now make a complex return on investment calculation when considering their career options against student debt. Things such as dual-degree programs could save a year of education, a variety of programs can offer opportunities to improve specialization in the field of pharmacy and thus expand career opportunities. Responses identified these and more as important decision points.
These types of themes were leveraged to create engaging content, matching the needs and motivations of prospective students towards the end goal of increasing quality applications and acceptance into the pharmacy school.
Need Help Conducting User Research?
Atlantic BT is well-versed in user research; conducting user and stakeholder surveys is just one phase of our Discovery process. Contact us to learn more about our UX Research and Design services.
Color â it’s one of the most expressive, subjective elements in life. Color surrounds us everywhere we go and shares a powerful connection with our emotions. And yet ironically â most of us are too blind to see just how lucky we are to have the ability to see in color. A lot of people today cannot see as many â or in some cases, any â colors like the rest of us.
It’s no surprise that our world is changing rapidly and becoming more dependent on technology. Digital experiences, such as browsing websites or applications, has become critical to our daily lives. When designing these experiences, it can be easy to overlook color accessibility.
Recently, I was asked about my design process and how I go about inclusivity â in particular with color accessibility. I realized how many people were not aware of accessible design.
So, let’s talk about color accessibility and how to go about tackling these challenges in your own digital experiences.
Why is accessibility so important?
Digital experiences can be expressive to everyone, regardless of color deficiencies. As creative professionals, we have the power to make the lives of those affected better â to have a sense of belonging. It starts with planning and designing for accessibility. It involves crafting experiences for all people, including those of us with visual, speech, auditory, physical, or cognitive disabilities. Letâs create a web weâre all proud of: an inclusive web made for and consumable by all people.
Color accessibility is important because it enables people with visual impairments or color vision deficiencies to interact with digital experiences in the same way as their non-visually-impaired counterparts.
[pull_quote]If you have to squint at any point in a website or web app to read or articulate something, thereâs an accessibility problem.[/pull_quote]
While we often think of visual impairments as long-term or permanent, many of us may experience short-term visual impairments. Have you ever had the sun glare into your eyes or your monitor when trying to browse the web or use an app? Ever forget your glasses or contacts? What about trying to read those digital billboards from a distance? Even those with the sharpest vision â corrected or not â will have trouble reading or comprehending your brand at some point.
Still not convinced? In 2017, The World Health Organization estimated that roughly 217 million people live with some form of moderate to severe vision impairment. Ouch. That statistic alone is reason enough to not only consider â but mandate â design for accessibility.
Apart from being an ethical best practice, there are also potential legal implications for not complying with regulatory requirements around accessibility. Did you know: In 2017, plaintiffs filed at least 814 federal lawsuits about allegedly inaccessible websites, including several class actions.
Designing digital experiences with color accessibility in mind can also have a positive economic impact on a brand by increasing its user base and conversion rate. Similar to poor usability, poor accessibility can drive up abandonment rates, which can lead to lost revenue and ultimately lost brand value. Making sure a brand uses colors that are strong in contrast will only help improve on this economic impact.
What makes a color palette accessible?
Digital experiences should follow the guidelines outlined in the Web Content Accessibility Guidelines (WCAG) to be accessible. Color accessibility is required for Level AA and Level AAA.
Level AA
For digital experiences that must comply with WCAG 2.1 Level AA, the following are the bare minimum requirements for color contrast:
Minimum 4.5:1 for normal text
Minimum 3:1 for large text, graphics, and UI components (e.g. input borders)
Level AAA
For digital experiences that must comply with WCAG 2.1 Level AAA, the following are the bare minimum requirements for color contrast:
Minimum 7:1 for normal text
Minimum 4.5:1 for large text, graphics, and UI components (e.g. input borders)
Note â Large text refers to a minimum of 24px or 19px bold.
Ensuring your designs are color accessible doesn’t have to be difficult.
There are two types of testing for color accessibility: quantitative and qualitative. The best way to ensure your designs are accessible is to test with actual people! If someone cannot use or read your product, then there’s likely a contrast issue. Qualitative testing can be time-consuming and costly.
For inexpensive testing, there are color contrast tools you can use online. These tools measure the contrast ratio between a foreground color and background color. The higher the ratio, the more likely a person can distinguish it.
Hereâs a typical scenario I go through all the time when designing digital experiences, such as websites or web applications:
The customer provides me with their branding colors.
I take these colors and run them through a color contrast tool to see what combinations I can use (according to the WCAG). I’ll also check brand colors against commonly-used black and white.
When colors fail to meet the requirements, I start nudging the colorâs lightness to the closest value that passes.
Rinse and repeat
Does this sound familiar? The color palette I am given may not be the same palette I recommend. As you can imagine, itâs a difficult conversation to have with a customer that they cannot use their colors the way they want.
I’ll admit that finding elegant color combinations for Level AAA is pretty tough, but for standard body text, I almost always try to get a combination that works for that level of compliance. Itâs just a better experience to have a strong contrast ratio for dense content.
ColorShark has a beautiful, intuitive interface for finding accessible colors.
Single color contrast tool to rule them all.
To help creative professionals be better equipped, there are a lot of tools out there, such as Colorable and ColorSafe. While these tools are great at doing a simple comparison between colors, I have to manually tweak combinations that do not pass compliance. To help automate this, I designed and developed a color contrast tool, called ColorShark. I wanted ColorShark to provide people with the ability to not only provide real-time visual indicators of color combinations, their respective contrast ratios, and adjustment sliders to hue, saturation, and lightness, but also automatically detect and suggest the closest compliant colors â if your combination is not accessible.
Going back to that typical scenario, using a tool like ColorShark saves me time and budget in getting an accessible color palette for a customer and their brand. Currently, I haven’t found another tool that can provide that sense of speed and exploration.
Let’s build an accessible web.
Don’t take the ability to see color for granted. As part of inclusive design, creative professionals must promote best practices to make sure people â regardless of color deficiencies â can use websites and web applications.
Tools like ColorShark can drastically improve your exposure to color accessibility and give you the means of expanding your audience. You’ll also feel better that you’re being more inclusive!
Information Architecture (IA) is the structural design of organizing information and its findability. For sites with large amounts of diverse content, a properly developed Information Architecture is critical to optimize both organic discovery through search engines, as well as ease of navigation once a user is on the site.
Read on to learn more about the thorough planning process involved in developing IA, gain insight into best practices, and see real-world examples.
Useful Exercises for Information Architecture
In order to build an information architecture that facilitates user experience, you will have to go through some planning exercises. The activities most commonly undertaken in defining an information architecture involve:
Content inventory: Examination of a website to locate and identify existing site content.
Content audit: Evaluation of content usefulness, accuracy, tone of voice, and overall effectiveness.
Information grouping: Definition of user-centered topics and relationships between content.
Card Sorts: A method, live workshop or online, which identifies the way users understand and group the content being presented to them.
Tree Testing: A method of testing user navigation through a proposed navigation in response to task scenarios, used to check effectiveness of various navigation paths and labelling.
Taxonomy Development: Definition of a standardized naming convention (controlled vocabulary) to apply to site content.
Descriptive Information Creation: Definition of useful metadata that can be utilized to generate âRelated Linkâ lists or other navigation components that aid discovery.
Steps for Determining Information Architecture
Developing Site Structure & Navigation
Site structure is the most basic component of information architecture. Itâs an important first step for planning navigation and content. The ultimate goal of a well-planned site structure is understandability; the structure should be based on the ways that user conceptualize the information provided by your website.
Structuring Taxonomies
Search capability is tremendously enhanced by a well-structured taxonomy. Proper development of taxonomies requires user research to identify and validate how your users need to group, sort, and identify the information on your website. After all, a search engine is only as good as the information structure it has available to search through!
In addition to search, taxonomies also support dynamic content feeds, empowering development teams to build sites customized towards user preferences. Feeds such as these rely on taxonomy terms and proper governance of taxonomies.
For example, for the North Carolina Judicial Branch, Atlantic BT built a taxonomy containing 17 Vocabularies, each of which was populated with the terms appropriate for that grouping, as well as their synonyms. When built into the content and database structure, these vocabularies allow for highly effective search:
Developing Data Structure
The final component of creating information architecture is ensuring that different content types are structured properly. Our designers and architects work together to identify required fields and metadata for all content types. This standardization allows designers to visually arrange content on pages in the most understandable way. Additionally, content creators can use these predefined content types to easily create new content that complies with site standards.
How Will this Information be Found? Building a Search Validation Plan
After planning and mapping your website structure, itâs important to make sure content is findable. For any search, native or third party, a well-designed and implemented Taxonomy is extremely useful in facilitating search results.
This test plan aims to evaluate the individual structures that make up a search system:
WHAT
WHY
HOW
OUTPUT
Search Analysis Review
Evaluating search data to identify and prioritize research scope
Analysis of search data reports. When relevant and necessary new data can be acquired
Clear indication of primary website goals – See âCritical Use Case Scenariosâ document
Taxonomy – Revise Topics
Create a topics hierarchy to return better results
Review Internal Card Sorts.
Review research data to decide frequency and priority.
Clearly define benefit and risk of topics taxonomy
In-person testing of content and term selection.
Refined taxonomy
See âTaxonomy Validationâ document
See âTaxonomy Card Sort Resultsâ document.
Taxonomy Synonym Identification
Identification of how actual users respond to navigation prompts and structures.
Review of Google Trends, search data, stakeholder interviews, label testing, and content to identify relationships.
Dendrograms and cluster analysis.
Navigation testing
Identification of how actual users respond to navigation prompts and structures.
Tree Test to identify findability from navigation menu
Dendrograms and cluster analysis.
Field Prioritization / Index Refinement
Optimization of relevant search result
Expert review / User Testing/ Google analytics – ongoing, new terms
To make sure we understand how users approach the application and match the interface accordingly.
Live user testing with scripted scenarios on clickable mockups.
Data on success/fail rates for scenarios, where errors were made, open-ended user feedback.
Validation of Search- QA
Top queries
Test plan for QA
Live testing by QA team members not involved in Design or Development
Feedback to Dev team on failures through bug reports and backlog.
Will This Information be Useful? Developing a Content Strategy
Now that youâve planned, mapped, and organized your content for search; itâs time to create a plan for types of valuable content and a publishing schedule.
The goal of developing a content strategy is to build an achievable roadmap to create and maintain content that audiences will actually care about.
Identifying structural elements early on will facilitate your content planning. Structural elements include:
Content Elements: Stand-alone or sub-pieces, such as alert banners.
Content Types: Most people think of these as pages.
Page Description Diagrams: Identifying and prioritizing the elements on each content type.
Style Elements: A basic plan and design of how to present the Page Description Diagrams.
From this point, you should be able to categorize and repurpose existing content. You may also choose to incorporate your persona research into writing new content or adding elements to existing pages.
What Comes After Information Architecture?
With a well-mapped Information Architecture and Content Strategy in place, you are ready to enter the design and testing phase of your website.
Defining a complex websiteâs IA can be a daunting task. If youâre still unsure where to start structuring your website, contact Atlantic BT for help. Weâve worked to help many large organizations reorganize their content to better serve users and become more efficient.
