5 Logic Features to Take Your Surveys to the Next Level

Ready for the lowdown on survey logic?

When you include logic features in your survey design, it gives you greater control over the behaviour of your survey and the experience that respondents have. This not only creates a smoother, more personalised survey journey but also paves the way for higher quality data. The more relevant and seamless you can make your survey, the more likely it is that your respondents will answer every question and provide the exact insights you need. 

Whether you’re totally new to survey logic or could just do with a refresher, we’re spotlighting five popular logic features that are included in our paid plans. Read on to learn why they’re so powerful and how you can use them to fine-tune your surveys. 

Imagine that you’re introduced to someone at a party and they ask what you do for a living. You tell them that you’re currently unemployed and they immediately ask lots of questions that only apply to someone who is working full-time. You’d probably feel rather ignored and annoyed and want to terminate the conversation as soon as possible.

Well, surveys can spark the same negative feelings and quick exits if they are not well designed. When respondents encounter irrelevant questions – especially after they’ve already made it clear who they are or how they feel – they’ll often quit the survey. Even if they do persist with your survey, you probably won’t obtain any useful insights, because those respondents will no longer feel engaged or that they are providing relevant information.

With question skip logic, you can skip respondents ahead to a specific page or to a particular question on another page based on how they answer a closed-ended question. Let’s suppose, for instance, that a study on the employee experience across industries asked a question similar to the one from our clueless party guest above. With question skip logic, the survey could skip respondents who are currently working full-time to follow-up questions about their current experience. For respondents who are currently unemployed, skip logic could send them to questions about their past work experiences or questions that ask about the employee experience in a broader sense. 

There’s proof that skip logic improves survey responses, and the same applies to both question skip logic and page skip logic. That’s because you’re building personalised survey paths where each respondent only sees questions that are relevant to them. Some respondents might need a shortcut that will skip them towards the end of your survey. Others will take a longer (but still rewarding) route. In either case, you’ll be ensuring a journey that makes sense and makes the most of their time. 

Want to learn more about question skip logic? If so, watch this video.

You know those custom survey paths that question skip logic creates? Well, advanced branching helps you to tailor them even more.

Whereas the paths you create with question skip logic are solely dependent on how a respondent answers a closed-ended question, advanced branching allows you to create custom survey paths by applying logic based on a variety of conditions. These conditions can include respondents’ answers to questions, as well as custom data from contacts and custom variables. 

Let’s suppose, for example, that a hair salon owner has created a customer survey asking whether the respondent is a regular customer, whether they visit occasionally or whether they’re a first-time client. With advanced branching, the salon owner could show or hide certain survey questions (or pages) depending on the answer given to this question. A regular would see different questions (or pages) to a brand-new customer and everyone would have a more tailored experience. 

Advanced branching allows you to direct respondents to specific questions on the same page or on different pages, depending on how you choose to structure your survey. If you keep related questions together on one page, no page break is needed for the logic to work. However, if you prefer to use page breaks, branching will route respondents to the right page.

If the owner wanted to find out whether their customer experience improved after implementing a new booking system, they could either disqualify respondents who answer a series of questions a certain way or use customers’ email addresses to skip recent first-time customers past questions that ask how the new system compares to the old one.

Advanced branching can take a bit of planning and it’s best to apply it after you’ve finalised your survey design, but it can do wonders for your data and your respondent experience.

Want to learn more about advanced branching? If so, watch this video.

We’ve talked a lot about the importance of only showing relevant survey questions to your respondents. Well, disqualification logic is a great, and necessary, foundation for that because it helps you to weed out anyone who isn’t part of your target audience.

With disqualification logic, you can set up a question that will disqualify respondents who choose a particular answer option. That way, you won’t have to sift through results that include responses from people who didn’t meet your targeting criteria or consent to your terms. (Please note that if you are buying responses via SurveyMonkey Audience, disqualification logic works in a slightly different way.)

Think of disqualification logic as a specific way to use question skip logic: instead of sending a respondent to another question or page based on their answer to a question, you’ll exit them from your survey. For example, let’s suppose that you’re a product marketer for a pet supply company and you want to send a survey to dog owners. You could add a yes or no question to the beginning of your survey asking respondents whether they currently own a dog and add question skip logic to send respondents who answer “No” to a disqualification page. (If you don’t want to miss out on dog-loving respondents who simply don’t own a dog at the moment, you could always choose to skip respondents who answer “No” to a separate page with a question asking whether they have ever owned a dog.)

You can show disqualified respondents the standard survey end page that you have set up, or you could show them a custom disqualification message or send them to a URL of your choice. The latter options are good ways to add a touch more personalisation and give the respondent a way to learn more about your company, products or services.

Want to learn more about disqualification logic? If so, take a look at our instructions and expert tips.

Custom variables are parameters (sometimes called query strings) that can be added to the end of your survey’s web link collector URL and pass key data into your survey results. They are rather like a hidden survey question that will help you out later when you’re segmenting, filtering and analysing your results.  

Custom variables are useful because they’re so flexible. They’re great for campaign tracking and web analytics and can help you to make the most of your customer relationship management (CRM) data. For example, a company that’s looking to improve its post-purchase consumer surveys might create custom variables for product ID and purchase date. When analysing results, the company could then create filters for those variables and learn more about specific purchasing experiences. With those variables in place, the company could also use the same survey over time to collect unique data about how its purchasing experience is improving (or not).  

Custom variables can also save your respondents from having to fill in information that you already have and simplify analysis. Let’s suppose that an internal task force of eight employees recently responded to a website issue that affected your customers and you now want to send that team a short survey. You could create custom variables based on information such as the employee’s name, department and job title. 

On the respondent side, this will save each person from having to fill in that information themselves. And when it comes to analysis, you’ll have the option to download an all responses data XLS or SPSS export to see the custom variables paired with each response. In the spreadsheet, the results for this single-question survey will look like this:

Want to learn more about custom variables? If so, take a look at our instructions and expert tips.

Want to improve how you ask follow-up questions? If so, carry forward responses help your survey to ‘remember’ respondents’ answers to previous questions so that you can ask follow-up questions that are relevant to those answers. 

For example, let’s suppose that you’re a ride-share company conducting a market research survey. Repeatedly asking questions about ride-share companies that a respondent has never heard of won’t do your survey experience, or your data, any favours. Instead, you could ask respondents which ride-share companies that they’re familiar with (using a multiple choice question that allows multiple answers) and then use carry forward responses to ask them more specific questions about only those companies that they selected. This will increase the likelihood of you gaining the insights you need into respondents’ ride-share preferences and finding out how they perceive your competitors’ brands.

Whether you use carry forward responses to filter out answer choices that your respondents didn’t select or carry forward the answers they did select, this logic feature allows you to go from broader questions to specific questions that are more useful to your research and show respondents that you’re paying attention to their answers.

Want to learn more about carry forward responses? If so, take a look at our instructions and expert tips. As these features show, logic is a great way to improve your survey design, create more inclusive survey experiences and get more nuanced data. Try them out in your next survey project and see the difference in your results.