I do Social Media Marketing for SurveyMonkey, so I’m a big proponent of using online surveys to reach out to your customers in order to make better, more strategic business decisions. You can get a lot of insight out of a simple survey, especially when you know a thing or two about creating and analyzing it. Obviously writing and designing a good survey is an important part of this process, but all that front-end work means very little if you don’t know how to analyze your results.
At SurveyMonkey, we offer a number of tools to help you slice, dice, and make sense of your date. Two of the most useful are cross-tabs and filters. I’m going to give you a brief overview and use case for each, so you know how to implement them for your needs.
What are cross-tabs?
Cross-tabbing is a handy analysis tool that provides you with a side-by side comparison of two or more survey questions. When you apply the cross-tab filter, you can select the responses you’d like segmented out, and see how those segments responded to each question in your survey.
So if you’re curious how people of different genders responded to your various survey questions, for example, you would include a survey question asking about your respondents’ gender. Then, once you apply the cross-tab, you’ll be able to easily see how men responded, compared with women.
This can be really useful in your marketing strategy.The guidance of cross-tabs can tell you lot about those who may be interested in your idea or product — it can segment those who responded favorably to your proposal by age group, gender, color preference — any category that you include as a survey question can be used to further break down your responses using cross-tabs.
What is filtering?
Apply a filter to your results to see a segment of your respondents removed from the others. You can filter by response, by custom criteria, or by property (date, completed vs. partially completed responses, email address, name, IP address and custom values) to narrow down your results, so you’re just seeing responses from people who interest you.
So if you’re marketing a product to cat lovers, for example, and one of your survey questions asks if your respondents like cats, the responses of people who responded “no” to that question probably are not of much interest. Apply a filter which selects just for people who answered “yes,” or “maybe” (if that was an option), and you’ll be able to see just the results of potential customers.
Combine Filters and Cross-Tabs for Better Survey Analysis
So, you may be wondering, can you apply filters and cross-tabs at the same time? The answer is yes! It’s a useful strategy for cutting down the noise and making sense of your responses.
First apply your filter. So people who are potential customers, based on our previous example. Then apply your cross-tab to find out just how different groups of potential customers feel. So, going back to our cat lover example, you would first apply the filter so you’re just looking at responses from people who may be interested in your product.
Then apply your cross-tab so you know the ages (gender, income level, and location can also be interesting factors here), and voila. You are left with a comprehensive view of your potential customers which can be broken up by age, gender, or anything you like.
Just remember to think ahead about the factors that will be interesting in your analysis, so you can plan for them in your survey design. There will be no way to cross-tab for income level, if you don’t ask for it in your original survey.
We hope this cross-tab and filter analysis overview was useful for you! Still have more survey analysis questions? How about an example of an insight you’ve gained using the cross-tab or filter features? Tell us about it in the comment section below. Thanks!
Carroll leveraged Personify's Small World Community to build an online community with powerful gamification, personalization and reporting to support engagement and measured results, and this has translated to a 30% increase in yield rate for the 2018 semester.