Unveiling the Specifics of ‘Unsubscribe’ for Email Marketers

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Email unsubscribe is one of the most dreadful things for any email marketer. After all the hard work you put into a campaign, it is particularly annoying to get your emails unsubscribed.

According to Mailjet, if your unsubscribe rate is below 1%, you are said to be within the industry norm. However, emails sent to new lists—to subscribers who have not received an email from you before—are not included in this calculation because they usually have more unsubscribes. Your industry also influences the number of unsubscribes you get. An agreeable unsubscribe rate is below 0.5%, and you should work on creating better emails if your unsubscribe rate exceeds that.

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Benchmark Your UX at All Costs

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In the user experience (UX) industry, benchmarking is a practice that measures the usability of a website. Benchmarking helps the UX researcher understand the current state of the site so the team can attack problem areas and improve them.

It is very difficult to fix or improve something when you don’t know what is wrong. Imagine taking your poorly-performing car to the mechanic and they ‘fix it’ without running any diagnostics or tests. They may change the spark plugs, rotate the tires, and add a few decals but will most likely miss identifying and addressing the root problem. This is similar to approaching a site redesign without first identifying its problems with real site users.

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10 Practical Tips for Increasing the Impact of Your Research Insights

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User experience (UX) researchers tasked with improving customer-facing products face many challenges on a daily basis—perhaps none more daunting than translating their research insights into positive change. This article presents 10 tips I have learned over the course of my career to help UX researchers increase the impact of their research insights in applied settings. These tips are intended primarily for in-house research teams, but they may apply to consultancies as well.

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When Words Are Not Enough

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The frequently-raised objection when it comes to quality research, UX research included, is that the conclusions are drawn based on the participants’ declarations. However, there exist some methods which allow one to grasp the real behaviors of participants, and they can be easily implemented into the research scenario.

During exploratory research, the respondents are often unable to articulate their needs or opinions. In turn, when it comes to usability tests or satisfaction surveys, it very often happens that the respondents’ answers are limited to vague opinions which, without being further explored by the moderator, don’t bring in much data.

Very often, they hide their opinions, because something is “not quite right” to say, something makes them feel ashamed, or their behaviors are controlled by mechanisms which they don’t even perceive—because who would admit to having certain prejudices or not fully socially-accepted desires?

Then how does one find out the real opinions of respondents?

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User Research With Small Business Owners: Best Practices and Considerations

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The majority of our work at Google has involved conducting user research with small business owners: the small guys that are typically defined by governmental organizations as having 100 or fewer employees, and that make up the majority of businesses worldwide.

Given the many hurdles small businesses face, designing tools and services to help them succeed has been an immensely rewarding experience. That said, the experience has brought a long list of challenges, including those that come with small business owners being constantly on-call and strapped for time; when it comes to user research, the common response from small business owners and employees is, “Ain’t nobody got time for that!”

To help you overcome common challenges we’ve faced, here are a few tips for conducting successful qualitative user research studies with small businesses.

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How to Determine When Customer Feedback Is Actionable

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One of the riskiest assumptions for any new product or feature is that customers actually want it.

Although product leaders can propose numerous ‘lean’ methodologies to experiment inexpensively with new concepts before fully engineering them, anything short of launching a product or feature and monitoring its performance over time in the market is, by definition, not 100% accurate. That leaves us with a dangerously wide spectrum of user research strategies, and an even wider range of opinions for determining when customer feedback is actionable.

To the dismay of product teams desiring to ‘move fast and break things,’ their counterparts in data science and research advocate a slower, more traditional approach. These proponents of caution often emphasize an evaluation of statistical signals before considering customer insights valid enough to act upon.

This dynamic has meaningful ramifications. For those who care about making data-driven business decisions, the challenge that presents itself is: How do we adhere to rigorous scientific standards in a world that demands adaptability and agility to survive? Having frequently witnessed the back-and-forth between product teams and research groups, it is clear that there is no shortage of misconceptions and miscommunication between the two. Only a thorough analysis of some critical nuances in statistics and product management can help us bridge the gap. Continue reading How to Determine When Customer Feedback Is Actionable

Your Guide to Online Research and Testing Tools

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The success of every business depends on how the business will meet their customers’ needs. To do that, it is important to optimize your offer, the website, and your selling methods so your customer is satisfied. The fields of online marketing, conversion rate optimization, and user experience design have a wide range of online tools that can guide you through this process smoothly. Many companies use only one or two tools that they are familiar with, but that might not be enough to gather important data necessary for improvement. To help you better understand when and which tool is valuable to use, I created a framework that can help in your assessment. Once you broaden your horizons, it will be easier to choose the set of tools aligned to your business’s needs. Continue reading Your Guide to Online Research and Testing Tools

Online Surveys On a Shoestring: Tips and Tricks

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Design research has always been about qualitative techniques. Increasingly, our clients ask us to add a “quant part” to projects, often without much or any additional budget. Luckily for us, there are plenty of tools available to conduct online surveys, from simple ones like Google Forms and SurveyMonkey to more elaborate ones like Qualtrics and Key Survey.

Whichever tool you choose, there are certain pitfalls in conducting quantitative research on a shoestring budget. Based on our own experience, we’ve compiled a set of tips and tricks to help avoid some common ones, as well as make your online survey more effective.

We’ve organized our thoughts around three survey phases: writing questions, finding respondents, and cleaning up data.

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Creativity Must Guide the Data-Driven Design Process

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Collecting data about design is easy in the digital world. We no longer have to conduct in-person experiments to track pedestrians’ behavior in an airport terminal or the movement of eyeballs across a page. New digital technologies allow us to easily measure almost anything, and apps, social media platforms, websites, and email programs come with built-in tools to track data.

And, as of late, data-driven design has become increasingly popular. As a designer, you no longer need to convince your clients of your design’s “elegance,” “simplicity,” or “beauty.” Instead of those subjective measures, you can give them data: click-through and abandonment rates, statistics on the number of installs, retention and referral counts, user paths, cohort analyses, A/B comparisons, and countless other analytical riches.

After you’ve mesmerized your clients with numbers, you can draw a few graphs on a whiteboard and begin claiming causalities. Those bad numbers? They’re showing up because of what you told the client was wrong with the old design. And the good numbers? They’re showing up because of the new and improved design.

But what if it’s not because of the design? What if it’s just a coincidence?

There are two problems with the present trend toward data-driven design: using the wrong data, and using data at the wrong time.

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