Market research: Suppose your company wants to launch a product whose usage depends on people having a decent internet connection, such as Hotstar, Netflix, etc. Before launching such a product, you need to understand the potential market size. For this, you need to conduct a survey with some people and based on their data, infer parameters such as the average data usage, the willingness to adopt new technologies, etc. for the entire population.
Marketing campaign efficacy: Suppose you work for a company such as Hotstar or Netflix. You want more and more people to move from your competitors’ platforms to your platform. You plan to do this through a marketing campaign. But how should this you structure this marketing campaign? What should be its budget? Which strategy should be used (free membership for a week, lower membership fees for a few weeks, etc.)? You can use the date from your past marketing campaign and your knowledge of sampling techniques to make these decisions.
Pilot testing: Again, let’s take the Hotstar and Netflix example. Suppose you’ve done all the market research required and developed a product. Now, before putting your product out in the market, you want to give it a trial run. For this, you can perform what is called a pilot test. It means that instead of giving your product a full-fledged launch, you can just launch it partially to a few people, who can test your product and help you decide whether it is good enough for a full launch.
Quality control: This is more of a manufacturing-centred application. Let’s say your company produces 10 million smartphones annually. This means that around 30,000 phones are manufactured every day. In such a situation, quality assurance (QA) becomes a function of utmost importance. Since it is difficult to check all 30,000 phones every day, your company would just “sample” a few and then make decisions based on those samples.

So, first, let’s see how sampling can be used for market research.

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So, now you know how stratified sampling can be used to improve your inferences. Let’s go through the case again:

You want to conduct a brand equity survey for e-commerce brands. In other words, you want to find out how much of the e-commerce market is controlled by Flipkart, Amazon, and Snapdeal, respectively.
An important part of this process would be to conduct a survey, the results of which would tell you the proportion of Indian e-commerce buyers that uses each of these websites.
However, in order to do this, you would need to perform stratified sampling on the basis of gender (male/female), age, and location (metro/tier 2/other urban/rural). Not doing this would mean that you run the risk of erroneous selection, for example, selecting too many people from metro areas or too few women, etc. Hence, by not using stratified sampling you might end up with an unrepresentative sample.
So, you give the questionnaire prepared by your team to the general public. Once you’ve acquired sufficient sample data, you can make estimations for the general population and estimate the brand equity of major Indian e-commerce brands.
However, you must not accept every entry you get. You can run some checks to screen out fraudulent entries. For example, if a person takes only one minute to fill a survey that usually takes 10 minutes, he/she is probably committing fraud.

However, as much as you would like to believe that you have used stratified random sampling, there actually is a big chance that the sampling done here is closer to stratified volunteer sampling or to stratified opportunity sampling than to stratified random sampling. Let’s understand why this is the case.

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So, let’s say you used email as the medium for your survey. Once you decided on your quota guide, etc. and sent the emails, you probably used the survey results to estimate population parameters. That’s the entire process. But where exactly did you make it a volunteer/opportunity sampling exercise?

For many people, the email could have ended up in the spam folder. If this happened, you would probably not get a response from them. Now, if all these people happened to fall in a specific general category (such as old people who don’t understand how to filter spam), then your survey would have ended up being biased.

Another potential source of bias is non-response. Let’s say that out of 80 people, only 40 chose to respond to the email. In that case, the 40 who did not respond would not be represented in your survey results. Hence, again, if these 40 people happened to disproportionately represent a particular segment (such as people who are digitally less savvy), the survey results would be biased.