Selected Sampling

Probability plays an important role in understanding customer behavior. In this dataset, approximately 51.25% of customers are female, showing a slight majority across the overall population.

However, when looking at specific segments, the distribution changes. For example, only about 30% of customers in the Tech Enthusiasts group are female. This shows that individual segments may not reflect the overall population.

This is important because relying on one segment alone can lead to inaccurate conclusions. By using proper sampling and probability, Amazon can ensure that its insights are more balanced and representative of the full customer base.

Assume that the 16,000 potential customers determined in the previous segmentation model are in the promotional data pool of Amazon. When a single customer is chosen at random in the entire database, then the likelihood that the customer is a woman is the number of women divided by the number of customers. Thus, the marginal probability is 8200/16000= 0.5125 and therefore, the probability of a random customer being a female is 51.25. Conversely, the probability of selecting a man is 7,800 / 16,000 = 0.4875, or 48.75%. On the portfolio level, Amazon has a slightly female majority audience. The commercial implications of this discovery are that female buyers are frequently the leaders in a household purchase sector like groceries, home goods, health products, and family subscriptions. It can be weighted in case the campaign goal is to maximize the order frequency. But random selection is not sufficient to provide the probability of response, profitability and category elasticity. This is why Amazon cannot presume that population share will automatically convert into campaign value. Marginal probabilities can be used to establish a fairness standard, and then conditional probabilities, conversion analysis, and lifetime value measurements are needed to allocate promotional rewards or impressions of an ad (Kotler & Keller, 2022).

Therefore, let’s think of the potential customer chosen randomly from the segment called Tech Enthusiasts. This segment has high margins on its electronics and smart devices and represents the most strategic target market. In the Tech Enthusiasts group, 1,200 people of all 4,000 customers are women. Thus, the probability of choosing a potential customer who will be a woman, given that the person is a customer of Tech Enthusiasts is 0.30:

P(F∣T)=1200/4000=0.30

This implies that at least 3 out of 10 randomly chosen Tech Enthusiasts will be female, which is significantly less than the total percentage of 51.25 of females. The difference between the two probabilities arises due to the difference in category participation based on interest structure, past purchase behavior, device ownership, advertising exposure, and income-related discretionary spending. Categories of electronics are generally male-skewed in most datasets, and home and value categories can be female-skewed. Thus, in case Amazon sampled only Tech Enthusiasts, the management might have made a wrong assumption that women are not represented in the whole campaign universe. This demonstrates the importance of sample frame design. The sampling population of a nonrepresentative segment can have legal, ethical and strategic errors. It would therefore be appropriate that Amazon apply stratified random sampling to all the four categories of customers such that gender representation is representative of the entire population but retains high value segment insight. This would enhance statistical validity and promotional fairness.