Primary data vs. secondary data

3 min read

The basics of market research!

The sources of data that we use to make key decisions are more important than ever. 

Not only can you simply plug your search term into any web browser, but you will also get an almost innumerable number of results, many of which circularly source from one another. As such, how can you ensure that you’re relying on the best information to make strategic decisions?

At the highest level, market research data can be split into primary and secondary data sources, although from a best practices perspective, secondary research should always be performed first.

What is primary data?

Primary data in market research is the first-hand data that is closely related to the issue that needs to be addressed. They are consciously collected from the relevant respondents to generate original information that can be used directly to solve the marketing problem.

Primary data in market research may come from interviews, focus groups, and observations of strategically planned activities. Survey methods can collect data by asking questions from participants who might have the appropriate information. Personal interviews or online surveys are a great way to access information. Studying customer behavior on your website or observing their experience with your pilot product are examples. You may read our latest blog on market research applications to learn more about them in detail.

What is secondary data?

Secondary data in market research is an important tool to have in your arsenal. It is a means to collect information from existing sources. Secondary data is information that has already been collected by someone else through various research methods such as questionnaires, interviews, or surveys. Often it’s just a matter of going through it and extracting what you need for your project.

Secondary research replaces a primary research effort and reduces the time and resources required to collect and analyze information and improve usability and speed of delivery. Going through a secondary research project can be like a treasure hunt — you may not always find what you’re looking for, but along the way, you’ll certainly find some great and interesting stuff. It is an essential and non-negotiable part of any research project that aims at providing new knowledge on a specific problem. 

Mistakes to avoid while conducting primary data research

Among the many things that can go wrong during your primary data research are:

  • not knowing where to garner information from
  • getting them from feeble sources
  • researching the wrong group
  • relying on set data
  • not accounting for personal research biases, etc.

One can avoid these mistakes when one makes room for them and spots them before it’s too late. Here are a few most common mistakes to avoid and suggestions on how to go about avoiding them.

Population specification error

While conducting any field survey, one needs to be sure which type of audience they need to target. No matter how many responses you collect, if they aren’t targeting the right masses, it would not fulfill the need to conduct research. 

Selection error

Before heading towards any research, one needs to have a sample size ready and the type of respondents we are looking for. But there isn’t any surety; most of the time, all the responses are valid only. To avoid such an error, we must focus on quality rather than on quantity.

Non-response error

It doesn’t matter much how well one has designed their questionnaire. There will be some respondents who wouldn’t follow through with the research. Sending them reminders or having a backup is useful in such cases.

Mistakes to avoid during the secondary research

Selection errors

A selection error is a kind of sampling error that occurs because of an inherent bias on the part of the researcher. This may happen when the data selected for analysis is inaccurate or inadequate. All market research can be vulnerable to some degree to selection errors. Here’s where strategic measures to minimize impact and other ways to account for it.

Errors that can invalidate data

In this age of increasing data, there is a greater tendency toward data manipulation. The accuracy and validity of the obtained data need to be checked. In the case of secondary data, it might be contaminated due to some inappropriate or negligent actions of a crucial mass of leadership or the organization handling it.

Broadly such errors may be caused by: 

  • data alteration,
  • the ambiguity of concerned stakeholders, and 
  • conceptual errors.

Data reformulation errors 

Secondary data in market research is not always directly beneficial to the analyst because it does not appropriately measure the subject under consideration. Errors are frequently the outcome of one of the four conditions listed below:   

Circumstantial change

Such a type of error occurs due to sudden changes in conditions that somehow significantly affect the outcome of the research. It can be as big as a geographical change or as meager as the unit of measurement change. And there are at least two ways of altering the particulars.

Due to inappropriate transformations

Original data is frequently offered in secondary data sources in categories established to make the data more presentable in a tabular style, or the original categories do not represent the demands of the analyst to manage the work at hand.

Errors due to misrepresentation

No matter whether the whole research process being carried out was done perfectly, a glitch as small as a misplaced decimal or any grammatical error can manipulate the whole meaning or miscommunicate any information.

Errors due to collection procedures to

The collection methods in which the researcher might have collected data may not be the best suited to those conditions.

Final notes

Market research is time-consuming as it involves research related to a certain market, niche, or even product. Since there may be a multitude of competitors in the market, the competitiveness varies from one industry to another. As you go about with research, data collection may be too much to handle.

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