Access this on-demand session to learn how to extract far deeper value from the survey data you already collect. Whether you run formal surveys or gather ongoing customer or employee feedback, you may be missing important patterns, relationships and opportunities for insight. This session shows how to move beyond basic reporting and unlock a richer understanding of what respondents are really telling you.

You will learn how to identify meaningful relationships in your data, such as differences in views across age groups, genders, regions or customer segments. The session explains how to distinguish genuine patterns from random variation so that decisions are based on statistically reliable findings. You will also learn practical techniques for working with rating scales, analysing multiple response questions and creating new variables that make your data easier to interpret.

The session introduces approaches for analysing open-text comments, including identifying themes, automating coding and assessing sentiment. It also demonstrates how to use key driver analysis to reveal which factors most strongly influence overall satisfaction or other outcomes you want to predict.

Designed for survey researchers, HR teams, customer insight professionals, market researchers and anyone involved in analysing attitudinal data, this on-demand session is accessible whether or not you currently use SPSS Statistics.

• How to find and describe important relationships in your data. Do different age groups respond differently? Does your respondents’ gender, or where they live, or how frequently they’ve bought your product make a difference to their views?
• How to distinguish between significant patterns in your data and random variations. When you’re making important business decisions on the basis of your survey results you need to be sure that the patterns you observe are statistically significant rather than random fluctuations. We’ll show you how.  
• Tips for effectively working with rating scales. Rating scales can be a great way of asking a lot of questions in a small space but what do you do with the results? We’ll show you how to get the most out of your rating scale data by calculating average rating scores, finding correlations, creating new fields and comparing respondents’ answers to a number of different questions.
• The best ways to analyse multiple response data. Multiple response questions can enable you to collect a lot of information but the results can be hard to analyse. We’ll show you the most common pitfalls and simple techniques to avoid them.
• How to analyse open-ended and text-based responses. It’s great when customers spend time giving you detailed written feedback but how can you confidently analyse text data and make sense of what it’s telling you? We’ll show you how you can automate much of your text data analysis, identify common themes across many respondents and even find out whether the comments are positive or negative.
• Creating new fields from existing data. It’s often useful to be able to take the data you already have and use it to create some new fields. For example, perhaps you asked respondents to tell you their age but now you want to categorise them according to age range. Or maybe you’d like to combine responses from a number of different questions into one aggregate score. We’ll show you how you can do this quickly and easily.
• Predicting satisfaction and performing key driver analysis. You’re asking customers to tell you how satisfied they are, but wouldn’t it also be useful to know what drives their satisfaction? Key driver analysis lets you see which fields in your data have the strongest relationship with overall satisfaction (or whatever else you want to be able to predict).