A common mistake in analyzing marketing data is to immediately start your analysis after collecting your market research data.
Once your data has been collected, it needs to be "cleaned." Cleaning research data involves editing, coding and the tabulating results.
To make this step easier, start with a simply designed research instrument or questionnaire.
Some helpful tips for organizing and analyzing your data are listed below.
- Look for relevant data that focuses on your immediate market research needs and the marketing problems you are addressing
- Rely on subjective information only as support for more general findings of objective research.
- Analyze marketing data for consistency. You might want to compare the results of different methods of your data collection. For example, are the market demographics provided to you from the local media outlet consistent with your survey results?
- Quantify your results, even when you have qualitative market research data. For instance; look for common opinions in your interview results that can be counted together.
- Read between the lines by bridging disparate datasets. For example, combine U.S. Census Bureau statistics on median income levels for a given location and the number of homeowners vs. renters in the area.
Marketing data analytics is an advanced approach to analyzing marketing data that can get pretty sophisticated.
The sophistication of your marketing data analysis should match what the recipients of your market research outputs will understand and can comprehend.
For example, running a Monte Carlo analysis on your marketing data would be overkill for a small business run by an owner who never took statistics. That individual might prefer simpler analysis that relies on calculating average, median and min/max values.
In short, make sure your charts and figures will be easily understood by your audience. Otherwise, what's the point?
In closing, perhaps the best advice we can give to folks who are charged with analyzing marketing data is not to go it alone.
Talk through what you are seeing in the data with your colleagues and ask them to challenge you as best they can on the inferences you are drawing from your marketing data.
It's a final safeguard to make sure there isn't a flaw in your thinking, and it's good advice for anyone who is analyzing marketing data.
There's power in numbers, both in your marketing data and in your marketing team.