# Effective Sampling Methods for Marketers

Companies want to know what all types of customers like so they can sell more products. But asking everybody is way too much work. This is where sampling comes in handy!

Sampling means only surveying a small group instead of the entire population. This makes research easier. The trick is picking a sample that represents all the different types of people.

There are a few main ways to sample:

1. Simple random: Pick people randomly from the whole group. Gives everyone an equal chance to be chosen.
2. Stratified: Split into subgroups first, then a random sample from each one. Captures key groups.
3. Cluster: Divide into clusters, and randomly select some clusters. Easier than reaching every person.
4. Systematic: Choose people at regular intervals, like every 5th one. Organized approach.
5. Convenience: Survey those easy to access. Fast but could miss some groups.
6. Judgement: Experts hand-pick based on knowledge. Uses insight but can be biased.

Sampling allows market researchers to estimate what products customers will buy without exhausting tons of time and money. We’ll learn when to use different sampling methods to best predict what people want!

## What is Sampling?

A sample refers to a select subset of a larger population, chosen for the purpose of research. Henceforth, sampling refers to selecting a subset of a population. Sampling is further divided into probability and non-probability sampling. Different types of random sampling are Simple Random sampling, stratified random sampling, cluster random sampling and systematic sampling.

Simple Random Sampling

In simple random sampling, every object has an equal chance of being selected. Suppose that there is a list of numbers from 1 to 10. We have to randomly select three numbers out of 10. Let’s observe how to do that using Python.

```import random

# Define a population
population = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]

# Sample size
sample_size = 4

# Simple random sampling using random.sample()
sample = random.sample(population, sample_size)

print("Population:", population)
print("Sample:", sample)
```

In the above code, we have defined a population. Using random. sample( ) we select a sample of three numbers from the population. The Python function random.sample( ) is an inbuilt function that returns a particular list of numbers.

The drawback of simple random sampling is that if the population is not inclusive enough, the sample can turn out to be skewed.

Recommended: Introduction to Bootstrap Sampling in Python.

Stratified Sampling

Stratified sampling divides the population into subgroups, or strata, to ensure representation across key characteristics. Each subgroup is referred to as a stratum.

```import random

# Define a population with groups (strata)
population = {
"Group A": [1, 2, 3, 4],
"Group B": [5, 6, 7, 8],
"Group C": [9, 10]
}

# Sample size per group
sample_size_per_group = 2

# Stratified sampling using dictionary comprehension
stratified_sample = {
group: random.sample(population[group], sample_size_per_group)
for group in population
}

print("Population:", population)
print("Stratified Sample:", stratified_sample)
```

From the above output, we can observe that the population was divided into three groups i.e. Group A, Group B, and Group C. From the individual groups, two elements are selected randomly.

Here you go, that’s your sample. One of the drawbacks of stratified random sampling is that each member of the population should be identified to experiment which can be impractical.

Cluster Sampling

Cluster sampling involves dividing the entire population into clusters and then randomly selecting clusters to represent the whole. We then randomly select the clusters which represent the entire population.

```import random

population = [
["Ramesh", "Sachin", "Anwesh"],  # Cluster 1
["Divyajeet", "Shresht", "Ram"],  # Cluster 2
["Umesh", "Rajesh", "Rashmi"],    # Cluster 3
["Rashi", "Rishabh", "Shreya"],    # Cluster 4
]

# Number of clusters to select
num_clusters = 2

# Sample size per cluster (optional, all selected in this example)
sample_size_per_cluster = None

# Randomly select clusters
selected_clusters = random.sample(population, num_clusters)

# Extract sample (single-stage)
if sample_size_per_cluster is None:
sample = [member for cluster in selected_clusters for member in cluster]
else:
# Double-stage: select members from each cluster
sample = []
for cluster in selected_clusters:
sample.extend(random.sample(cluster, sample_size_per_cluster))

print("Population:", population)
print("Selected Clusters:", selected_clusters)
print("Sample:", sample)
```

As we can observe from the above code, the whole population is divided into four clusters and two clusters have been selected randomly.

The drawback of cluster sampling is that this method can be heavily biased if the clusters formed are biased.

Systematic Sampling

In systematic sampling, the objects of the population are selected at fixed intervals to form the sample. Let’s look at its Python implementation to know further.

```import random

def systematic_sampling(population, sample_size):
"""
Performs systematic random sampling on a given population.

Args:
population: A list containing the population elements.
sample_size: The desired size of the sample.

Returns:
A list containing the sampled elements.
"""

population_size = len(population)
if sample_size > population_size:
raise ValueError("Sample size cannot be greater than population size.")

# Calculate sampling interval
sampling_interval = population_size // sample_size

# Choose a random starting point
random_start = random.randint(0, sampling_interval - 1)

# Sample elements with the systematic interval
sample = [population[i] for i in range(random_start, population_size, sampling_interval)]

return sample

# Example usage
population = ["a", "b", "c", "d", "e", "f", "g", "h", "i", "j"]
sample_size = 5
sample = systematic_sampling(population, sample_size)
print("Sample:", sample)
```

In the above code population and sample size are taken as arguments. After checking if the sample size is not greater than the population, the sampling size is calculated which is the population size divided by the sample size. Thereafter the program returns the sample.

## Non-Probability Sampling

Non-probability sampling involves selecting samples based on criteria other than random selection, such as convenience or judgment. In general non-probability sampling is broadly categorised into two methods i.e. convenience and judgemental sampling.

Convenience Sampling

Convenience sampling is a type of non-probability sampling in which the objects of the experiment are easily available. Though it is fast and inexpensive, it also has numerous drawbacks such as sampling bias which is non-quantifiable.

Judgemental Sampling

In judgemental sampling, samples rely on the researcher’s experience and judgment. Even though this method is fast and efficient, there are certain risks associated with this as well such as sampling bias based on the bias of the researcher. The study cannot be replicated as the bias of selection differs from researcher to researcher.

## Conclusion

With these sampling methods, marketers and researchers can navigate the complexities of data collection more efficiently, saving time and resources. It will help you save much of your budget, time, and other resources. Which sampling method will revolutionize your next market research project?

Recommended: 4 Ways to Perform Random Sampling in NumPy