5-Part Series: Marketing Research 101 – Part 4: Analyzing the Results



Our next stop in this 5-Part Series is analyzing the results. The results are the portion that you have been patiently waiting to see. What analysis came in from surveys, focus groups, or interviews. It is crunch time, and you get to explore and explain the results.


Marketing Research is broken down into five steps:


Each of these marketing research steps provides insight into how you can create your next research. Market Research is a Succession Must-Have, these will be a series of articles, to be able to provide a deep-dive about each step.


Let’s explore Step 4: Analyzing the Results


Step 4 of the Marketing Research is where you are going to analyze the data that you have received from your online surveys, interviews, or focus groups. You get to explore the feedback from your research. 


The primary purpose of this step is to take the data you collected and turn it into meaningful and actionable information that will be used to make decisions.


Ensuring that the previous steps were followed Identifying the Research (Links to an external site.)Designing the Research Study (Links to an external site.)and Conducting the Study (Links to an external site.)  (Links to an external site.)this step will allow you to get the data and analyze it to grab the most important information to give it to the decision-makers that requested the research in the first place. 


There is software out there that allows you to summarize the data received with a click of a button. Survey Monkey and Qualtrics are a great software to get the data analysis faster. You will be able to get averages and totals for results. If doing online surveys, these are the best tools to use.


Here are four essential actions for analyzing your data: 


Clean your Data First

When reviewing your data, you want to make sure you have clean data. Dirty data refers to data that is inaccurate, incomplete, inconsistent, or duplicated. These types of results can have a significant impact on an organization that has to make decisions based on the data. Having these results can also cause millions of dollars in wasted resources & expenses in terms of operational inefficiencies, missed series, and unrealized opportunities.


Having clean data can help with increasing efficiency, low cost, reduce risk, enhance customer relations, and make the most of all the information you have gathered.

  • Complete: Make sure you are not missing anything. All questions need to be answered and ensure that there are no partials, break-offs, or omissions.

  • Unique: Ensure that you do not have the same person taking the same survey. You want to strive to have unique responses from different individuals.

  • Timely: Ensure that the survey was taken in ample time. Ensure respondents read each question and not just answered the questions fast to be done with the survey.

  • Valid & Accurate: Make sure that the right amount of information is in the right place of the survey data set.

  • Consistent: Ensure that you create quality checks in the survey to ensure that they are still taking it.


Avoid Analysis Paralysis

Once you have all the data in front of you, it can be a bit overwhelming having to analyze everything that you have received back. Especially if the results are in the hundreds. To avoid stress, you want to make sure you are going back to review your research objectives to make sure you are on track. You also want to pull out your “Mock-up” that you created during Step 2: Design the Research Study (Links to an external site.). This will help in narrowing down all the data and only showing what you need to for the research.


Too Good to Be True

In reviewing your analysis, if you come across data that looks too good to be true, you may want to go back and see how your question was phrase before you share any data. Chances could be the data is skewed and will lead to inaccurate information.


Report Only Meaningful Difference

Once you have the data, you want to ask yourself if there is a real difference in data? The variations you do see you want to check that they are real differences, and it did not occur by chance. You want to make sure there is statistical significance in data and not just a glitch in the data. There need to be significant differences.


 You can now go and grab the data and see what results you got that will help make the decision-makers make a change in the organization.

1 view0 comments