Market research has become an integral part of a business and a tool that corporate entities can use to their advantage to influence the performance of their products in the market, learn customer pain points, consumer buying patterns, latest market trends, economic shifts, and consumer preferences. With the ever-increasing competition in the business world, market research has become search a critical asset in the business world such that business organizations that rarely utilize it face the risk of extinction. Large corporate organizations have gone to the extent of dedicating whole departments for market research purposes. Luckily, medium and small-sized businesses that do not have the financial muscle to dedicate a whole department for marketing research purposes can as well enjoy the proceeds of market research through hiring experts to conduct market research regularly.
Generally, market research should be carried out throughout the year, however, there are critical moments that any business entity, irrespective of its size should utilize market research. Such moments include when launching a new product. Normally, many business entities would launch a product and wait to hear about how the newly launched product is performing in the market. Rather than using this approach, market research should be conducted before launching a new product. This approach gives the business a fair current market overview which may assist the business predict how the product is likely to perform in the market, how much the business is going to charge for it, who are the target customer, where the product will be sold and how it will get there. Also, market research gives business-critical knowledge about customer preferences which may come at a critical time for the business to adjust the product before launching it to ensure it fits the consumer preferences. Apart from a new product launch, market research is essential when the business is intending to start a new business. Market research digs into the current situation in the market and how the new business is likely to fair in terms of customer availability and the amount of competition.
There is a myriad of reasons why any business organization should conduct market research. One is to understand the customers. Be it when launching a new product, when opening a new branch, starting a new business, investigating an issue with a product, or expanding a product, reading from the same page as the customers are very critical. Through market research, a business gets to know what are the customer’s preferences, their ability to buy the product, how often they will buy the product, and how they would want to access the product. Customer preferences are known to be extremely versatile and therefore market research should be conducted regularly to ensure the business is always informed of the current customer preferences. Other reasons that may call for market research include accelerating business growth and development.
Conducting market research requires the application of various scientific research methods called sampling techniques. These techniques are classified into two broad categories namely probability sampling and nonprobability sampling technique. Under these two broad categories, there are subcategories each with its advantages and disadvantages.
SAMPLING TECHNIQUES
The essence of having various sampling techniques is to help researchers determine the most appropriate technique to use in his/her research study. Researchers are required to evaluate the nature of research data that they are intending to take and determine the most appropriate technique to apply. Generally, there are various criteria that a researcher can use to determine the best sampling technique to apply. Such criteria include the nature of research and the expected outcome. In some instances, the target population may be too big and the researcher’s ability to include every member of the population may be limited due to factors such as lack of enough manpower, time, and finance. This will prompt the researcher to opt for sampling techniques that would fit the available budget and that can be carried out with the available manpower in the time limit allowed. The number of variables being studied also influences the type of sampling technique used. The available sampling techniques allow a researcher with both complex and simple data to be collected and analyzed depending on the type of outcome that is expected from the research activity. Finally, the importance of the research data to an organization in terms of its usage in decision making. If for instance, the research being conducted is to introduce a new product in the market. The organization may require a detailed research study to be carried out 0to determine how the new product is likely to fair on in the market. Consequently, such a research study may include several variables to increase the level of accuracy and usefulness of the data in producing the intended result.
PROBABILITY SAMPLING
This is a sampling technique that allows researchers to take a sample from a larger population using the dictates of probability. For a sampling method to qualify as probability sampling, it must apply the selection of the sample must use random criteria. This means all the units in the sample population have the probability of picked. For instance, if the researcher’s sample contains 100 competitor businesses that the researcher wants to undertake his research on, then each of the competitor business should have a probability odd of 1 for being selected to participate in the research. Using this criterion, researchers can assemble a sample that is a true representation of the real population itself. Data obtained from such a sample will be analyzed and the results obtained will represent the true general population. The various techniques under probability sampling include the following.
Simple random sampling
As the name suggests, simple random sampling is considered to be the easiest among the probability sampling techniques. The chance of an item being selected from the sample population completely depends on chance and every single item possesses an equal chance of being selected. Although there are various approaches that researchers can decide to use when conducting simple random sampling, however, all the approaches involve including all the units of the sample in a master list or a general list. Thereafter, units are picked randomly from the general list. This method is usually favored by market researchers today especially when the population under research is enormously large and the number of units involved is indefinite. Given such circumstances, market researchers opt for a simple random sampling technique.
Apart from being easy to conduct, a simple random sampling technique has various advantages. For instance, it’s a cost-effective sampling technique that favors small and medium enterprises that cannot dedicate a huge amount of funds for market research purposes. Apart from cost-effectiveness, the method is fairer compared to the others in terms of eliminating any biasness that is commonly associated with probability sampling techniques. Interestingly, given the basic nature of the method in data collection, one does not need to possess particular technical knowledge. Rather, basic skills such as listening and recording skills are enough for data collection. There is no limitation as to how large the sample size should be because the population might be too huge.
Although the message has very lucrative advantages, the method has various disadvantages accrued to it. One is that taking a large sample size might be inconvenient because every member of the population holds an equal probability of being selected. Managing a large sample size might present problems to the researcher which increases the probability of biasness. In case the research requires the use of face to face data collection methods, this method may not be appropriate given the constraints related to covering large geographical areas in terms of time and cost. Finally, simple random sampling may inadvertently turn out to be costly because a list of all the potential participants should be availed before undertaking the exercise.
Systematic sampling
Participants are nominated at intervals regularly from a given sampling frame. These intervals are determined in such a way they are in an adequate sampling size. For instance, given a demographic of size b, and required to demographic of sample size, a researcher should select each b/ath participant for the study. This method is simpler and more direct than random sampling. It is suitable for covering a wide area of research. This methodology, however, inserts other subjective factors in the study. This increases the chances of oversight or under-representation of study patterns. In the first step, the researcher nominates a starting number which is relatively smaller than the demographic under study. Afterward, an interval is taken in between the samples of the demographic.
Some of the advantages of this methodology include its simplicity. This method is relatively easier to accomplish and understand. Constructing this systematic samples is cheap and moderately easy in comparison and analyzing. This makes it easier for surveys that run under a limited budget. It also provides statisticians with a high level of control and sensibility of the study process. This gives an advantage to surveys with tight measures made a hypothesis if the study is developed to meet given parameters. Also, this methodology offers very low risk. There is a very limited chance of the data being contaminated.
However, this methodology suffers from several drawbacks. For instance, it assumes that the demographic size can be estimated. In constructing the systematic samples, this method assumes the population size. If the researcher wants to survey the size of a mouse in a region, determining the number of the mouse in that region is not possible. This will pose a problem in determining a point to start or the space size. Additionally, there is a low level of randomness. For a reasonable survey, randomness is key in providing all-round information. If the demographic is of standardized pattern, incurring similar cases is very apparent.
Stratified Random Sampling
Also referred to as proportional random sampling, the method involves first grouping the subjects to be sampled into various classification groups such as age, gender, social-economic status, level of education, or religious affiliation. Point to note is that classification must be done systematically to avoid overlapping, a subject has characteristics that can fit into more than one group of classification. Each member of theses distinct groups stands a chance of being selected to represent the sample. The general classifications that we use in our daily lives fall under this method sampling method. These classifications include nationality, socioeconomic divisions, religion, age, and educational achievements. to conduct market research using this technic. Researchers need to identify the target population. Then, a researcher would need to identify the stratification variables and determine the number of strata that the population would be divided into. Ideally, al the stratification variables should be guided by the objective of the research study. For instance, if the objective of the research is to identify the best location to start a business, the variables should revolve around this objective. Preferably, these variables should not be more than six and the stratification should range from four to six. This will minimize the probability of having a scenario where the impact of one variable is being canceled by another. Once the stratification is done, a researcher would need to create a sampling frame that will accommodate all the variables. The next step is assigning a random number to each unique unit. Then finally the researcher can select units from the strata randomly.
Various advantages are accrued to this sampling method. For instance, the accuracy level of the results obtained is way better than what the other probability sampling techniques. Also, due to the application of statistical knowledge in this method, smaller samples can as well give highly informative data that is a true representation of the real population. In any case, this is among the few sampling techniques that cover the whole population given that the strata divisions cover every aspect of the population.
One of the disadvantages related to this sampling technique is that it can be costly for small business enterprises without huge financial capability. Also, due to the complex classification applied, the method may require lots of time which may inconvenience businesses. However, stratified sampling is among the most appraised sampling technique due to its high level of accuracy.
Clustered sampling
In this category, subsets of a population are taken as a primary sampling unit instead of using individuals. The population is categorized into subsets referred to as clusters randomly picked from a population to be included in the study. These subsets, the clusters, are normally predefined defined. For instance, specific GP activities or an urban center can be used as a cluster. In a one-stage sampling cluster, every participant of a given cluster is then encompassed in the sampling study. On the other hand, a two-stage sampling cluster selected participants from every cluster are randomly picked to be included in the study. Clustering, however, must be considered in the analysis. In England, a General Household survey is conducted every year. This is the best instance where a single-stage cluster sampling is put to work. All household members selected for the survey are encompassed in the study.
This sampling method is more efficient in comparison to other methods like simple random sampling. Cluster sampling is well suited where a large geographical area is to be covered. For example, reaching a large number of participants in small GP activities is relatively cheaper and easier rather than just contacting a few participants in a lot of distinguished GP activities. This method also makes it possible for the survey to be conducted with small capital. The cost of the cross-examination of every member of a community can be high. Using clusters however it is cheaper to compile data of targeted population providing accurate information needed. Clustering minimizes the chances of variability. It provides accurate estimates than other methods.
However, this method has its fair share of limitations of utilizing this method in sampling. This limitation may include a high risk of biased information as a participant may not be fully cooperating. Also if the chosen cluster does not represent the population under study the resulting analysis cannot be fully beneficial. Errors are eminent in clustering. This is brought by highly depending on the researcher’s methods and skills. A lot of clusters are grouped based on self-identifying data. This may pose a limitation as individuals may influence the importance of the information by misrepresenting oneself in a given way.
NON-PROBABILITY SAMPLING TECHNIQUES
Non-probability sampling is defined as a sampling technique that is executed by selecting samples based on rather different criteria compared to what is used in probability sampling. Here, researchers use their subjective judgment. This method is less strict compared to the probability sampling method and relies heavily on the expertise of the researcher. The observation method is prominently used in the non-probability technique. One distinct feature that differentiates this method from the probability technique is that all subjects in a population do not possess an equal chance of being selected to represent the sample. Researchers prefer this technique when the possibility of drawing random probability is not possible due to constraining factors such as cost and time required to obtain the results. The various types of non-probability sampling include the following;
Convenient sampling
In this method, the researcher selects samples depending on the convenience of their availability. The researcher does not consider a sample that would represent the whole population but rather considers the ease of access to the available samples. Ideally, credible research requires that the sample be able to represent the true population. However, sometimes this is not possible given the size of the population. For instance, an individual intending to open a clothes store in a busy location that has other cloth stores may consider using this method. Since taking samples countries may be too expensive and may even present irrelevant data that may not apply in the investor’s location, a convenient sampling method may be appropriate. He/she may consider observing the buying behavior of people around or location conduct interviews to gather the information that may be used to make an investment decision. It is one of the most popular non-probability sampling techniques due to its cost-effectiveness, speed, and ease of acquiring a sample.
However, research experts argue that this method may present half baked results that may lie out critical data that may help market researchers make market-related decisions that may impact the business both in the short run and the long run as well. Additionally, the fact that the sample is obtained depending on availability, there is a high probability of missing out on critical information from a population that may not be available during the taking of the sample. This means such mistakes may impact the business negatively in the future due to inadequate data that consequently leads to misinformed decisions.
Consecutive sampling
Consecutive sampling is almost similar to convenience sampling with a slight difference in that the researcher picks one unit, person, or group from the population and conducts in-depth research for a given time, analyzes the results obtained, and then proceeds to the second unit, individual or group. This method of research enables the researcher to conduct in-depth research on one unit and derive variable information that can help the researcher make a conclusive decision.
Sampling techniques require that a researcher come to a definite conclusion that either agrees with the null hypothesis or disapprove it and accept the alternative hypothesis. However, consecutive sampling slightly differs from the other sampling techniques by presenting a third option whereby the researcher can either accept the null hypothesis, the alternative hypothesis and if none of the two is applicable, then the researcher can go back, select another sample and conduct the research or experiment again. This sampling method is commonly used by malls that issue promotional leaflets to customers and a couple of them agree to answer a few questions asked by market researchers. The main advantage is the availability of a variety of options when determining the sample size. However, the sample size cannot be considered as a representative of the population since no systematic method is used to collect the sample.
Quota sampling
In this form of non-probability sampling method, the researcher forms a sample from units that represent a population. The criteria used by the researcher to create the sample is base on particular qualities or traits. The quotas created using these differential traits or qualities can be generalized to represent the whole population. There are various subcategories of quota sampling such as controlled quota sampling and uncontrolled quota sampling. The decision on which among the two to use depends on the expertise of the researcher about the population or the nature of the population involved.
The main method of using this technique is that apart from being the most efficient technique in time-saving, data obtained from this method presents an accurate representation of the population of interest due to the use of specific quotas. Also, the method is convenient for both the respondents and the researcher due to the use of quota samples. Market researcher prefers this technique when the time limit allowed is very little. Also, it is convenient when the budget constraint is very prominent.
Snowball sampling
This method is very common in social sciences, especially when targeting groups that are normally difficult to reach. Target groups that are already there are obligated to select the further subject they know. The researcher observes the new subject and asks them to nominate identify other subjects with similar traits, and the chain continues. This method exists in three types. These types are; exponential non-discriminative snowball, linear snowball, and exponential discriminative snowball.
This method allows the study of groups that a rare to reach. This is one of the major strengths of this method. This method is very cheap, easier, simple, and moderately cost-efficient. This method of sampling requires minimal planning and just a few researchers in comparison to other sampling methods.
This technique suffers from the researcher not being in control of the method. The target within reach by the researcher depends majorly on the last subject observed. Also, representativeness by the technique is not certain. During the study, the researcher does not know the actual distribution of the targeted demographic or that of the study sample. Biased research is also an inherent threat in using this method. The observed subject will tend to pick a subject they are familiar with. As a result, there is a possibility of researches getting results of a small section of the population with common characteristics.
Clustered sampling
In this category, subsets of a population are taken as a primary sampling unit instead of using individuals. The population is categorized into subsets referred to as clusters randomly picked from a population to be included in the study. These subsets, the clusters, are normally predefined. For instance, specific GP activities or an urban center can be used as a cluster. In a one-stage sampling cluster, every participant of a given cluster is then encompassed in the sampling study. On the other hand, a two-stage sampling cluster selected participants from every cluster are randomly picked to be included in the study. Clustering, however, must be considered in the analysis. In England, a General Household survey is conducted every year. This is the best instance where a single-stage cluster sampling is put to work. All household members selected for the survey are encompassed in the study.
This sampling method is more efficient in comparison to other methods like simple random sampling. Cluster sampling is well suited where a large geographical area is to be covered. For example, reaching a large number of participants in small GP activities is relatively cheaper and easier rather than just contacting a few participants in a lot of distinguished GP activities. This method also makes it possible for a survey to be conducted with small capital. The cost of the cross-examination of every member of a community can be high. Using clusters however it is cheaper to compile data of targeted population providing accurate information needed. Clustering minimizes the chances of variability. It provides accurate estimates than other methods.
However, this method has its fair share of limitations of utilizing this method in sampling. This limitation may include a high risk of biased information as a participant may not be fully cooperating. Also if the chosen cluster does not represent the population under study the resulting analysis cannot be fully beneficial. Errors are eminent in clustering. This is brought by highly depending on the researcher’s methods and skills. A lot of clusters are grouped based on self-identifying data. This may pose a limitation as individuals may influence the importance of the information by misrepresenting oneself in a given way. Systematic sampling
Participants are nominated at intervals regularly from a given sampling frame. These intervals are determined in such a way they are in an adequate sampling size. For instance, given a demographic of size b, and required to demographic of sample size, a researcher should select each b/ath participant for the study. This method is simpler and more direct than random sampling. It is suitable for covering a wide area of research. This methodology, however, inserts other subjective factors in the study. This increases the chances of oversight or under-representation of study patterns. In the first step, the researcher nominates a starting number which is relatively smaller than the demographic under study. Afterward, an interval is taken in between the samples of the demographic.
Some of the advantages of this methodology include its simplicity. This method is relatively easier to accomplish and understand. Constructing this systematic samples is cheap and moderately easy in comparison and analyzing. This makes it easier for surveys that run under a limited budget. It also provides statisticians with a high level of control and sensibility of the study process. This gives an advantage to surveys with tight measures made a hypothesis if the study is developed to meet given parameters. Also, this methodology offers very low risk. There are very limited chances of the data being contaminated.
However, this methodology suffers from several drawbacks. For instance, it assumes that the demographic size can be estimated. In constructing the systematic samples, this method assumes the population size. If the researcher wants to survey the size of a mouse in a region, determining the number of the mouse in that region is not possible. This will pose a problem in determining a point to start or the space size. Additionally, there is a low level of randomness. For a reasonable survey, randomness is key in providing all-round information. If the demographic is of standardized pattern, incurring similar cases is very apparent.
CONCLUSION
The fact that business organizations can’t reach out to all of their customers individually. there