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A Look Back at 30+ Years of Website Design

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A Look Back at 30+ Years of Website Design

Web design has come a long way since 1991, when the first ever website was published. Exclusively text-based, this site marked the beginning of what would become a digital revolution.

And while recollections of “under construction” GIFs and blinding background colors make me thankful for just how far the web has come, there are some historical web design choices that actually demand a nod of respect.

Websites like this one haven’t been lost to time, either. If you want to see what a website looked like at any period since its launch, enter its domain name into the Wayback Machine and choose a date. In this post, let’s take a look at how web design has evolved, from text-only interfaces up through the sleek, modern designs we see today.

Early 1990s: Antiquity

The early 90s marks the start of our website design timeline. At this point, there was no such thing as a high-speed internet connection. It was dial-up modems, or it was nothing. Therefore, websites needed to be built for less-than-stellar connection speeds. They mostly looked like walls of text — what we now take for granted as “design layout” did not exist.

history of web design: an examle of an early html website

While later versions of HTML allowed for more complex designs, they were still very basic compared to today, consisting mainly of tags for headers, paragraphs, and links. Visual elements and styling like typography, imagery, and navigation were things of the not-too distant future.

Takeaways for Today’s Websites:

While the function of these early sites was purely informational, we can see some design elements that apply today. These old web pages were very lightweight and optimized for a slow internet connection we all still experience from time to time. These design considerations took the user experience into account, something today’s websites don’t always do, even with faster speeds.

Yes, today’s internet can handle media-rich websites … but it still has some limits. Large media files, heavy graphic design, and excessive animations can all contribute to higher bounce rates when load speeds aren’t as fast as we want. Keep your user in mind when considering complicated design, and remember to K.I.S.S. (Keep It Simple, Superhero).

Mid-1990s: The Middle Ages

The middle ages of web design were plagued by on-site page builders and spacer GIFs. (Better than an actual plague though, right?) By the mid-90s, web design had evolved both in terms of structure and appearance. Designers began using table-based layouts to organize content, allowing for greater flexibility and creativity. Sites were still quite text heavy, but text could now be divided into columns, rows, and other navigational elements for better readability.

Graphical design elements also quickly grew in popularity. Page hit counters, animated text, and dancing GIFs are just a few of the graphical elements that mark this period in web design.

history of web design: the early version of apple's website

Takeaways for Today’s Websites:

Today, there are plenty of reasons why table-based design is not the best choice for your website — the extensive markup, slow load times, and visual inconsistency are just a few of the pitfalls.

Regardless, this development was key in the evolution of web design: It was the first move toward non-linear page structure. Different elements could now be positioned in different sections of a web page, and designers had to consider the best way to present information to the user.

Page structure remains critical when thinking about navigation and content. It largely determines how the user experiences and interacts with your site. While these considerations might not have been at the forefront during the middle ages of web design, they are certainly at the forefront today.

Late 1990s: The Renaissance

Renaissance. Rebirth. Web design has had its fair share of reimaginings, but one of the first occurred with the introduction of Flash. Introduced in 1996, Flash opened up a world of design possibilities that weren’t possible with basic HTML. It was the marriage of virtual graphics and interaction.

While many of the same design elements from previous periods were still present, they were enhanced with animations, tiled background images, neon colors, 3D buttons, splash pages, and other multimedia.

Flash marked the beginning of visitor-focused design — structure and navigation became important considerations and designers began to hone in on appearance and usability over pure content.

history of web design: a website with flash elements

Takeaways for Today’s Websites:

Flash was a game-changer, but it wouldn’t stick around forever. Flash is hardly ever used today and is deemed one of the biggest SEO sins of all time. Today, it’s the norm to opt for alternative methods such as CSS and JavaScript animations to get similar effects, or to embed videos from video hosting sites.

Early 2000s: The Enlightenment

The early 2000s were a period when usability and flexibility really came to the forefront of web design.

Leading the charge was CSS, a coding language that allowed developers to store visual rules in files separate from HTML, effectively separating content and style. This gave greater creative freedom to both web designers and content developers — content could now be developed exclusively from design, and vice versa. CSS made websites easier to maintain (less code and complexity), more flexible (div tags are independent of one another), and quicker to load (smaller files).

Better understanding of color psychology also led to increased use of whitespace and the decrease of garish colors, like neons. Links started being added to icons rather than just text, resolution and pixelation became more important concerns, and strategic placement of content also gained traction.

history of web design: an early website for the company polaroid

Takeaways for Today’s Websites:

People typically scan websites looking for the information they need, so any site that makes this job easier gets a giant check-mark. Savvy web designers know that most users don’t read everything on a website, and understand how readers take in information.

Therefore, intuitively placed information, visually accentuated links, and straightforward navigation are just a few best practices today’s websites should adhere to. Always design with usability in mind!

Mid- to Late-2000s: The Industrial Revolution

The Industrial Revolution of web design begins with the birth of Web 2.0. It’s at this time that things really began to move toward the modern web. The growth of multimedia applications, the rise of interactive content, and the advent of social media are a few definitive features of this period.

Moreover, these changes largely dictated the way web design was … well, done. Aesthetic changes included better color distribution, increased use of icons, and greater attention to typography.

Most importantly, however, design became about content, and content became about search engine optimization. With the user now firmly at the center of design, selling products (at least explicitly) became the secondary function of websites — now it was all about getting found.

history of web design: a mid-2000s website for the company lulu lemon

Takeaways for Today’s Websites:

As mentioned, the evolution of Web 2.0 saw the growth of SEO as a consideration. While these techniques have been adapted over the years, thinking about your website in terms of SEO is still a top priority for most thriving business websites.

SEO demands content, and content largely became the focus of web design during this era. Keyword optimization, inbound and outbound linking, authoring, tagging, and syndication technology such as RSS became natural design elements. While link spamming and keyword jamming soon exploited these techniques, these methods are no longer effective and (I hope) have largely fizzled out.

2010 to Now: The Modern Era

Today, over two decades after the publication of the first website, web design has firmly established itself as an irreplaceable component of every good marketing strategy. Recent research found that 50% of today’s consumers think website design is crucial to a business’s brand.

In terms of modern aesthetics, we have seen the proliferation of minimalism: sparse content, flat graphics (so long, 3D buttons!), simpler color palettes, and big and bold visuals. In addition, UX has taken center stage, giving way to such design features as infinite scrolling and single-page design.

You may have noticed that our website has embraced all these features with its latest design:

history of web design: a modern website for the company hubspot

One more key step in the evolution of web design is the mobile web. Since the launch of the iPhone in 2007, there has been a re-evaluation of the way websites are structured to accommodate for the growing number of mobile web users. This includes several mobile frameworks that take a “mobile-first” approach, and an even greater focus on mobile speed optimization, since phones usually lack the processing speed or connection strength of your typical desktop.

This digital revolution has also given rise to responsive design, in which page elements automatically adjust to the width of the browsing window, allowing websites to look good on any device or screen. Today, responsive design is necessary to ensure a pleasing mobile user experience, given over half of global website traffic comes from mobile devices.

Where will websites go next?

If there’s one factor that has informed every single one of these developments, it’s content. Every design element here has been adapted in such a way to bring the most relevant content to the user in the most efficient and effective way. Notions of accessibility, adaptability, and usability truly define this era of web design.

Though there’s much more we can do with web design today, it’s fun to take a look back at where we came from. Looking at how web design has progressed thus far, it’s exciting to think about where it will be in the next 20 years.

Editor’s note: This post was originally published in July 2013 and has been updated for comprehensiveness.

examples of brilliant homepage, blog, and landing page design

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OpenAI’s Drama Should Teach Marketers These 2 Lessons

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OpenAI’s Drama Should Teach Marketers These 2 Lessons

A week or so ago, the extraordinary drama happening at OpenAI filled news feeds.

No need to get into all the saga’s details, as every publication seems to have covered it. We’re just waiting for someone to put together a video montage scored to the Game of Thrones music.

But as Sam Altman takes back the reigns of the company he helped to found, the existing board begins to disintegrate before your very eyes, and everyone agrees something spooked everybody, a question arises: Should you care?

Does OpenAI’s drama have any demonstrable implications for marketers integrating generative AI into their marketing strategies?

Watch CMI’s chief strategy advisor Robert Rose explain (and give a shoutout to Sutton’s pants rage on The Real Housewives of Beverly Hills), or keep reading his thoughts:

For those who spent last week figuring out what to put on your holiday table and missed every AI headline, here’s a brief version of what happened. OpenAI – the huge startup and creator of ChatGPT – went through dramatic events. Its board fired the mercurial CEO Sam Altman. Then, the 38-year-old entrepreneur accepted a job at Microsoft but returned to OpenAI a day later.

We won’t give a hot take on what it means for the startup world, board governance, or the tension between AI safety and Silicon Valley capitalism. Rather, we see some interesting things for marketers to put into perspective about how AI should fit into your overall content and marketing plans in the new year.

Robert highlights two takeaways from the OpenAI debacle – a drama that has yet to reach its final chapter: 1. The right structure and governance matters, and 2. Big platforms don’t become antifragile just because they’re big.

Let’s have Robert explain.

The right structure and governance matters

OpenAI’s structure may be key to the drama. OpenAI has a bizarre corporate governance framework. The board of directors controls a nonprofit called OpenAI. That nonprofit created a capped for-profit subsidiary – OpenAI GP LLC. The majority owner of that for-profit is OpenAI Global LLC, another for-profit company. The nonprofit works for the benefit of the world with a for-profit arm.

That seems like an earnest approach, given AI tech’s big and disruptive power. But it provides so many weird governance issues, including that the nonprofit board, which controls everything, has no duty to maximize profit. What could go wrong?

That’s why marketers should know more about the organizations behind the generative AI tools they use or are considering.

First, know your providers of generative AI software and services are all exploring the topics of governance and safety. Microsoft, Google, Anthropic, and others won’t have their internal debates erupt in public fireworks. Still, governance and management of safety over profits remains a big topic for them. You should be aware of how they approach those topics as you license solutions from them.

Second, recognize the productive use of generative AI is a content strategy and governance challenge, not a technology challenge. If you don’t solve the governance and cross-functional uses of the generative AI platforms you buy, you will run into big problems with its cross-functional, cross-siloed use. 

Big platforms do not become antifragile just because they’re big

Nicholas Taleb wrote a wonderful book, Antifragile: Things That Gain From Disorder. It explores how an antifragile structure doesn’t just withstand a shock; it actually improves because of a disruption or shock. It doesn’t just survive a big disruptive event; it gets stronger because of it.

It’s hard to imagine a company the size and scale of OpenAI could self-correct or even disappear tomorrow. But it can and does happen. And unfortunately, too many businesses build their strategies on that rented land.

In OpenAI’s recent case, the for-profit software won the day. But make no bones about that victory; the event wasn’t good for the company. If it bounces back, it won’t be stronger because of the debacle.

With that win on the for-profit side, hundreds, if not thousands, of generative AI startups breathed an audible sigh of relief. But a few moments later, they screamed “pivot” (in their best imitation of Ross from Friends instructing Chandler and Rachel to move a couch.)

They now realize the fragility of their software because it relies on OpenAI’s existence or willingness to provide the software. Imagine what could have happened if the OpenAI board had won their fight and, in the name of safety, simply killed any paid access to the API or the ability to build business models on top of it.

The last two weeks have done nothing to clear the already muddy waters encountered by companies and their plans to integrate generative AI solutions. Going forward, though, think about the issues when acquiring new generative AI software. Ask about how the vendor’s infrastructure is housed and identify the risks involved. And, if OpenAI expands its enterprise capabilities, consider the implications. What extra features will the off-the-shelf solutions provide? Do you need them? Will OpenAI become the Microsoft Office of your AI infrastructure?

Why you should care

With the voluminous media coverage of Open AI’s drama, you likely will see pushback on generative AI. In my social feeds, many marketers say they’re tired of the corporate soap opera that is irrelevant to their work.

They are half right. What Sam said and how Ilya responded, heart emojis, and how much the Twitch guy got for three days of work are fodder for the Netflix series sure to emerge. (Robert’s money is on Michael Cera starring.)

They’re wrong about its relevance to marketing. They must be experiencing attentional bias – paying more attention to some elements of the big event and ignoring others. OpenAI’s struggle is entertaining, no doubt. You’re glued to the drama. But understanding what happened with the events directly relates to your ability to manage similar ones successfully. That’s the part you need to get right.

Want more content marketing tips, insights, and examples? Subscribe to workday or weekly emails from CMI.

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Cover image by Joseph Kalinowski/Content Marketing Institute

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The Complete Guide to Becoming an Authentic Thought Leader

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The Complete Guide to Becoming an Authentic Thought Leader

Introduce your processes: If you’ve streamlined a particular process, share it. It could be the solution someone else is looking for.

Jump on trends and news: If there’s a hot topic or emerging trend, offer your unique perspective.

Share industry insights: Attended a webinar or podcast that offered valuable insights. Summarize the key takeaways and how they can be applied.

Share your successes: Write about strategies that have worked exceptionally well for you. Your audience will appreciate the proven advice. For example, I shared the process I used to help a former client rank for a keyword with over 2.2 million monthly searches.

Question outdated strategies: If you see a strategy that’s losing steam, suggest alternatives based on your experience and data.

5. Establish communication channels (How)

Once you know who your audience is and what they want to hear, the next step is figuring out how to reach them. Here’s how:

Choose the right platforms: You don’t need to have a presence on every social media platform. Pick two platforms where your audience hangs out and create content for that platform. For example, I’m active on LinkedIn and X because my target audience (SEOs, B2B SaaS, and marketers) is active on these platforms.

Repurpose content: Don’t limit yourself to just one type of content. Consider repurposing your content on Quora, Reddit, or even in webinars and podcasts. This increases your reach and reinforces your message.

Follow Your audience: Go where your audience goes. If they’re active on X, that’s where you should be posting. If they frequent industry webinars, consider becoming a guest on these webinars.

Daily vs. In-depth content: Balance is key. Use social media for daily tips and insights, and reserve your blog for more comprehensive guides and articles.

Network with influencers: Your audience is likely following other experts in the field. Engaging with these influencers puts your content in front of a like-minded audience. I try to spend 30 minutes to an hour daily engaging with content on X and LinkedIn. This is the best way to build a relationship so you’re not a complete stranger when you DM privately.

6. Think of thought leadership as part of your content marketing efforts

As with other content efforts, thought leadership doesn’t exist in a vacuum. It thrives when woven into a cohesive content marketing strategy. By aligning individual authority with your brand, you amplify the credibility of both.

Think of it as top-of-the-funnel content to:

  • Build awareness about your brand

  • Highlight the problems you solve

  • Demonstrate expertise by platforming experts within the company who deliver solutions

Consider the user journey. An individual enters at the top through a social media post, podcast, or blog post. Intrigued, they want to learn more about you and either search your name on Google or social media. If they like what they see, they might visit your website, and if the information fits their needs, they move from passive readers to active prospects in your sales pipeline.

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How to Increase Survey Completion Rate With 5 Top Tips

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How to Increase Survey Completion Rate With 5 Top Tips

Collecting high-quality data is crucial to making strategic observations about your customers. Researchers have to consider the best ways to design their surveys and then how to increase survey completion, because it makes the data more reliable.

→ Free Download: 5 Customer Survey Templates [Access Now]

I’m going to explain how survey completion plays into the reliability of data. Then, we’ll get into how to calculate your survey completion rate versus the number of questions you ask. Finally, I’ll offer some tips to help you increase survey completion rates.

My goal is to make your data-driven decisions more accurate and effective. And just for fun, I’ll use cats in the examples because mine won’t stop walking across my keyboard.

Why Measure Survey Completion

Let’s set the scene: We’re inside a laboratory with a group of cat researchers. They’re wearing little white coats and goggles — and they desperately want to know what other cats think of various fish.

They’ve written up a 10-question survey and invited 100 cats from all socioeconomic rungs — rough and hungry alley cats all the way up to the ones that thrice daily enjoy their Fancy Feast from a crystal dish.

Now, survey completion rates are measured with two metrics: response rate and completion rate. Combining those metrics determines what percentage, out of all 100 cats, finished the entire survey. If all 100 give their full report on how delicious fish is, you’d achieve 100% survey completion and know that your information is as accurate as possible.

But the truth is, nobody achieves 100% survey completion, not even golden retrievers.

With this in mind, here’s how it plays out:

  • Let’s say 10 cats never show up for the survey because they were sleeping.
  • Of the 90 cats that started the survey, only 25 got through a few questions. Then, they wandered off to knock over drinks.
  • Thus, 90 cats gave some level of response, and 65 completed the survey (90 – 25 = 65).
  • Unfortunately, those 25 cats who only partially completed the survey had important opinions — they like salmon way more than any other fish.

The cat researchers achieved 72% survey completion (65 divided by 90), but their survey will not reflect the 25% of cats — a full quarter! — that vastly prefer salmon. (The other 65 cats had no statistically significant preference, by the way. They just wanted to eat whatever fish they saw.)

Now, the Kitty Committee reviews the research and decides, well, if they like any old fish they see, then offer the least expensive ones so they get the highest profit margin.

CatCorp, their competitors, ran the same survey; however, they offered all 100 participants their own glass of water to knock over — with a fish inside, even!

Only 10 of their 100 cats started, but did not finish the survey. And the same 10 lazy cats from the other survey didn’t show up to this one, either.

So, there were 90 respondents and 80 completed surveys. CatCorp achieved an 88% completion rate (80 divided by 90), which recorded that most cats don’t care, but some really want salmon. CatCorp made salmon available and enjoyed higher profits than the Kitty Committee.

So you see, the higher your survey completion rates, the more reliable your data is. From there, you can make solid, data-driven decisions that are more accurate and effective. That’s the goal.

We measure the completion rates to be able to say, “Here’s how sure we can feel that this information is accurate.”

And if there’s a Maine Coon tycoon looking to invest, will they be more likely to do business with a cat food company whose decision-making metrics are 72% accurate or 88%? I suppose it could depend on who’s serving salmon.

While math was not my strongest subject in school, I had the great opportunity to take several college-level research and statistics classes, and the software we used did the math for us. That’s why I used 100 cats — to keep the math easy so we could focus on the importance of building reliable data.

Now, we’re going to talk equations and use more realistic numbers. Here’s the formula:

Completion rate equals the # of completed surveys divided by the # of survey respondents.

So, we need to take the number of completed surveys and divide that by the number of people who responded to at least one of your survey questions. Even just one question answered qualifies them as a respondent (versus nonrespondent, i.e., the 10 lazy cats who never show up).

Now, you’re running an email survey for, let’s say, Patton Avenue Pet Company. We’ll guess that the email list has 5,000 unique addresses to contact. You send out your survey to all of them.

Your analytics data reports that 3,000 people responded to one or more of your survey questions. Then, 1,200 of those respondents actually completed the entire survey.

3,000/5000 = 0.6 = 60% — that’s your pool of survey respondents who answered at least one question. That sounds pretty good! But some of them didn’t finish the survey. You need to know the percentage of people who completed the entire survey. So here we go:

Completion rate equals the # of completed surveys divided by the # of survey respondents.

Completion rate = (1,200/3,000) = 0.40 = 40%

Voila, 40% of your respondents did the entire survey.

Response Rate vs. Completion Rate

Okay, so we know why the completion rate matters and how we find the right number. But did you also hear the term response rate? They are completely different figures based on separate equations, and I’ll show them side by side to highlight the differences.

  • Completion Rate = # of Completed Surveys divided by # of Respondents
  • Response Rate = # of Respondents divided by Total # of surveys sent out

Here are examples using the same numbers from above:

Completion Rate = (1200/3,000) = 0.40 = 40%

Response Rate = (3,000/5000) = 0.60 = 60%

So, they are different figures that describe different things:

  • Completion rate: The percentage of your respondents that completed the entire survey. As a result, it indicates how sure we are that the information we have is accurate.
  • Response rate: The percentage of people who responded in any way to our survey questions.

The follow-up question is: How can we make this number as high as possible in order to be closer to a truer and more complete data set from the population we surveyed?

There’s more to learn about response rates and how to bump them up as high as you can, but we’re going to keep trucking with completion rates!

What’s a good survey completion rate?

That is a heavily loaded question. People in our industry have to say, “It depends,” far more than anybody wants to hear it, but it depends. Sorry about that.

There are lots of factors at play, such as what kind of survey you’re doing, what industry you’re doing it in, if it’s an internal or external survey, the population or sample size, the confidence level you’d like to hit, the margin of error you’re willing to accept, etc.

But you can’t really get a high completion rate unless you increase response rates first.

So instead of focusing on what’s a good completion rate, I think it’s more important to understand what makes a good response rate. Aim high enough, and survey completions should follow.

I checked in with the Qualtrics community and found this discussion about survey response rates:

“Just wondering what are the average response rates we see for online B2B CX surveys? […]

Current response rates: 6%–8%… We are looking at boosting the response rates but would first like to understand what is the average.”

The best answer came from a government service provider that works with businesses. The poster notes that their service is free to use, so they get very high response rates.

“I would say around 30–40% response rates to transactional surveys,” they write. “Our annual pulse survey usually sits closer to 12%. I think the type of survey and how long it has been since you rendered services is a huge factor.”

Since this conversation, “Delighted” (the Qualtrics blog) reported some fresher data:

survey completion rate vs number of questions new data, qualtrics data

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The takeaway here is that response rates vary widely depending on the channel you use to reach respondents. On the upper end, the Qualtrics blog reports that customers had 85% response rates for employee email NPS surveys and 33% for email NPS surveys.

A good response rate, the blog writes, “ranges between 5% and 30%. An excellent response rate is 50% or higher.”

This echoes reports from Customer Thermometer, which marks a response rate of 50% or higher as excellent. Response rates between 5%-30% are much more typical, the report notes. High response rates are driven by a strong motivation to complete the survey or a personal relationship between the brand and the customer.

If your business does little person-to-person contact, you’re out of luck. Customer Thermometer says you should expect responses on the lower end of the scale. The same goes for surveys distributed from unknown senders, which typically yield the lowest level of responses.

According to SurveyMonkey, surveys where the sender has no prior relationship have response rates of 20% to 30% on the high end.

Whatever numbers you do get, keep making those efforts to bring response rates up. That way, you have a better chance of increasing your survey completion rate. How, you ask?

Tips to Increase Survey Completion

If you want to boost survey completions among your customers, try the following tips.

1. Keep your survey brief.

We shouldn’t cram lots of questions into one survey, even if it’s tempting. Sure, it’d be nice to have more data points, but random people will probably not hunker down for 100 questions when we catch them during their half-hour lunch break.

Keep it short. Pare it down in any way you can.

Survey completion rate versus number of questions is a correlative relationship — the more questions you ask, the fewer people will answer them all. If you have the budget to pay the respondents, it’s a different story — to a degree.

“If you’re paying for survey responses, you’re more likely to get completions of a decently-sized survey. You’ll just want to avoid survey lengths that might tire, confuse, or frustrate the user. You’ll want to aim for quality over quantity,” says Pamela Bump, Head of Content Growth at HubSpot.

2. Give your customers an incentive.

For instance, if they’re cats, you could give them a glass of water with a fish inside.

Offer incentives that make sense for your target audience. If they feel like they are being rewarded for giving their time, they will have more motivation to complete the survey.

This can even accomplish two things at once — if you offer promo codes, discounts on products, or free shipping, it encourages them to shop with you again.

3. Keep it smooth and easy.

Keep your survey easy to read. Simplifying your questions has at least two benefits: People will understand the question better and give you the information you need, and people won’t get confused or frustrated and just leave the survey.

4. Know your customers and how to meet them where they are.

Here’s an anecdote about understanding your customers and learning how best to meet them where they are.

Early on in her role, Pamela Bump, HubSpot’s Head of Content Growth, conducted a survey of HubSpot Blog readers to learn more about their expertise levels, interests, challenges, and opportunities. Once published, she shared the survey with the blog’s email subscribers and a top reader list she had developed, aiming to receive 150+ responses.

“When the 20-question survey was getting a low response rate, I realized that blog readers were on the blog to read — not to give feedback. I removed questions that wouldn’t serve actionable insights. When I reshared a shorter, 10-question survey, it passed 200 responses in one week,” Bump shares.

Tip 5. Gamify your survey.

Make it fun! Brands have started turning surveys into eye candy with entertaining interfaces so they’re enjoyable to interact with.

Your respondents could unlock micro incentives as they answer more questions. You can word your questions in a fun and exciting way so it feels more like a BuzzFeed quiz. Someone saw the opportunity to make surveys into entertainment, and your imagination — well, and your budget — is the limit!

Your Turn to Boost Survey Completion Rates

Now, it’s time to start surveying. Remember to keep your user at the heart of the experience. Value your respondents’ time, and they’re more likely to give you compelling information. Creating short, fun-to-take surveys can also boost your completion rates.

Editor’s note: This post was originally published in December 2010 and has been updated for comprehensiveness.

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