For example, if you’re surveying your customer base, it can be helpful to know how much money they’ve spent with your company. If you’re surveying your employees, knowing their job title and level can give you more information that helps explain why they answered the way they did.
Most organizations have this kind of information about their survey respondents available. Now we’re making it a whole lot easier to put it to use in your surveys.
When you mark respondents in your SurveyMonkey contact list with custom data—like the types mentioned above—you can use filters in SurveyMonkey Analyze to compare the responses of different groups of respondent in your contact list.
That means you can use filters to see whether high-paying customers respond differently to your survey compared to low-paying customers, or how engaged the employees in one of your satellite offices are—and the best part is, you can do it all from within SurveyMonkey.
Here’s how it works
- Add custom data for each of your contacts in SurveyMonkey. It’s best to do this by creating a CSV spreadsheet of your respondents, adding the custom data as columns, and uploading it to your SurveyMonkey account.
- Send your survey using an email collector in SurveyMonkey
- Once you have responses, go to the Analyze section of your survey. Click on Filter and choose Filter by Respondent Metadata. Choose which custom data field you’d like to filter by and click “Apply.”
How filtering by custom data helps you
Being able to filter based on custom data fields can open a whole lot of opportunities for your surveys. For one, it means you’ll uncover trends that otherwise would be missed.
For example, let’s say you’re surveying your customer base about their experience with your company. You add how much each contact has spent on your products as a custom data field in SurveyMonkey.
When you get back your results, there isn’t much noteworthy on the surface. But when you compare customers who have spent a medium amount to those who have spent a large amount, you start to see some patterns emerge. You notice a big drop in Net Promoter Scores in customers with medium average order values.
From here, you can start to develop strategies to improve your medium spending customers’ experience, and keep them coming back for more.
That’s just one example. There are plenty of ways using custom data can help you survey your customers better. Here are a few examples of good ideas to filter by:
- The product they purchased
- Their account manager
- Their last purchase amount, and the date of their last purchase
- Their total purchase amount
Getting good at using custom data can also save you and your respondents a lot of time. There are some types of information you don’t want to ask for in every survey—especially if you’ve asked for it before. Once you know something about respondents in your contact list, you can use custom data to record it so you never have to ask them again.
For example, say you’re an HR manager who’s responsible for surveying employees in your company about their engagement. You don’t have to ask employees what office they work in, which department they’re in, or whether they’re an individual contributor or a manager. You can simply add those answers as custom data and then use filters to spot groups in your company that are happy, and those that might need more attention.
Using custom data, and filtering by it, can make you better at your job—no matter what role you’re in or how you apply your data.
It can bring you closer to your respondents and save them time, but most importantly it can help you uncover insights that you wouldn’t have been able to find otherwise. The possibilities for using it are practically endless, so get in there and start filtering!