IKH

Uses of Sampling in Marketing Campaigns

(Note: In this segment, a basic understanding of A/B testing is required, for which you can use the following link.  You’ll learn about A/B testing in particular and hypothesis testing in general in the upcoming module.)

Now, let’s go through the second use case, i.e., the use of sampling in marketing campaigns for measuring campaign efficacy.

Say you are an e-commerce company that operates through an app/site. Broadly, there are six common types of problems that you could face.

As you saw in the video, each of these six situations needs to be handled differently.

Let’s pick one of these cases where the user opened the app and browsed but didn’t shop. How would you coax such a user to use the app again and actually shop? Let’s see.

So basically, there are four different courses of action that you could take in order to get the user to use your app/site.

Which one of these should you select? Is the strategy of giving a 20% discount more effective than the one in which you give only a 10% discount? Do you even need to give a discount? Is a reminder enough? Will the reminder even work, or will people just return to the app/site themselves after some time?

To be able to answer all these questions, you would need to perform A/B testing. Basically, you would divide your current customer base into four groups, say, group A, group B, group C, and group D. Then, each of the groups would be subjected to one of the above strategies. For example, group A would get a 20% discount coupon, group C would get an app reminder, etc. Then, when you got the data for these sample groups, you could use the concepts of hypothesis testing and sampling to answer the questions asked above.

Again, you could use stratified sampling here. But how would you do that? Well, first of all, you’d need to break up your population into various small segments on the basis of factors such as the acquisition channel, the frequency of shopping, the payment mode generally used, etc.

Once you break your population into microsegments this way, you can then get sample information for each segment. Remember that the reason for making these divisions would be to ensure that the sample represents the population as closely as possible.

Finally, once you make these probably unbiased divisions, you would have your sample. And, once you get the sample, you can perform A/B testing.

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