To grow in today’s tough economic market, every hour and dollar spent needs to yield positive results that can be used to facilitate growth. Collecting survey data and analyzing the data are two different, but very important steps you must take to help identify crucial areas of growth.
Data collection comes from a variety of sources and interactions, such as face to face interviews, mail surveys, web survey, and offers, and the amount of information that can be collected and compiled can be staggering. However, simply collecting this data is not enough. While it is a crucial step, it is what you do with the data that is key to growing your business.
Analyzing your Collected Data
In order to utilize this information to target areas of growth; you will need to have a strategic survey analysis plan in place. Having a wealth of information on hand is one thing, but being able to effectively use the information is another.
Compile & Validate your Information
Before you do anything, compile the data, including any questions, answers, and profiles of participants. Make sure it is fairly easy to move and group your results. Check to see that all of the questions were understood. See if participants made comments about particular questions or left specific questions unanswered, and then consider what you will do with this data.
For example, if your survey question asked the participant to provide a rating from 1-5 and you find the question was answered with something other than <blank>, 1, 2, 3, 4, or 5, how you will interpret the findings?
Once you have compiled and validated your results, break the data down by grouping it into classes and recording how many data points fall into each class. This is where you check for patterns based on gender, race, age, religion or any other information you collected from participants, and where your raw data is transformed into valuable information.
Establish Simple Frequency Distributions for Each Survey Question – Frequency distributions demonstrate how many observations on a given variable have a particular attribute. You may choose to look at gender, income levels, age ranges, and etc. For example, a survey is taken of 50 people. The frequency distribution might indicate 25 people selected (a); 5 were female and 20 were male. The other 25 people selected (b); 12 were female and 13 were male. Distributions may be displayed using percentages, or in terms of a bar chart.
Ultimately, the more detailed your analysis, the more valuable the data becomes.