The importance of consumer insights in decision-making is well recognized in India. It was the British colonialists who changed the direction of the economy, unleashing the power and potential of markets to cater to consumers more efficiently. This made the masses consumers instead of just shopkeepers and entrepreneurs. Today, Indian markets are no longer dominated by the elite and top profiteers, but by the middle classes, as reflected in the extensive use of the term “Lake in India” to indicate products that have been made in India and imported into the country.

A market is only as good as its distribution which is a complex phenomenon. It is influenced by various forces such as demand, price, competition, and many others. All these forces have an impact on the distribution of goods and services in the market. One cannot ignore them and hope that things will remain as they are.

Consumers are the most important force in determining the quality and quantity of any product or service. With their insights into the market, they can make informed decisions as to where to spend their money. They will also have an idea as to the level of satisfaction they can expect from the service they receive. As far as distribution is concerned, the consumers can play a major role. They decide where and how they want to spend their money. That is the power of consumer insights in decision-making.

However, the power of consumers is not confined to distribution alone. It extends to pricing also. The consumers are the ones who decide which products and services are cheap or expensive. This is the power of market dynamics. When the consumers make their decisions based on economic factors, the prices of the products and services will follow.

The decision-makers, i.e., retailers and other big businesses, pay very close attention to the price of the product or service they offer. The consumers will be the ones to determine the level of affordability of the product or service.

The information available to them gives them the tools to bargain for lower prices with suppliers and make good deals. If they find out that the consumers are getting cheated by sellers of products and services, the power of consumer awareness will play a decisive role in their decision-making. The sellers will start to see that the competition is heating up and will find it increasingly difficult to maintain prices.

Consumer insights also influence the manner in which businesses operate. In industries where consumers have specific demands, they are the ones who ensure that the goods and services meet these demands. If consumers feel that the quality of the product is poor, they will not buy it. On the other hand, if they feel that the service they get is good, then they would not hesitate to patronize that business.

Consumer insights also give businesses important signals about the type of customer they should target. The analysis shows that consumers do not purchase the same type of product or service from different companies. Instead, they prefer brands that resonate with their values, lifestyle, and other characteristics. Thus, a business may choose to focus on the more lucrative segments of consumers. It may choose to specialize in providing goods and services that are in line with what consumers need. Alternatively, it may focus on services that consumers cannot live without.

By providing an analysis of the way in which consumers use – different marketing tools and businesses will be able to adapt quickly to consumer demands. Once the necessary changes are made, consumers will not only continue to patronize the business but also refer others to the business. This, in turn, will boost its market share and expand the market share of the business. This is how consumer insights play a crucial role in business. Without them, businesses would not be able to survive and grow.

If you found my post helpful, then do share it with your friends and colleagues. If you have any feedback/questions, you may leave a comment below.

Click here to know more about me.

The post Top 5 reasons why Consumer insights is important in decision making appeared first on PRASHANT AGGARWAL.

Quantitative Research Types

Quantitative Research – Experimental

• True experimental
  • • True experimental research aims to determine causal relationships among variables, and it relies on statistical analysis to approve or disprove a hypothesis. It is also worth noting that true experimental research is considered the most accurate type of experimental design.
    • It may be conducted with or without a pre-test, and it always involves a control group and a test group.
    • Other characteristics include variable manipulation, Random selection of participants. It is always conducted in a controlled setting, and intervention is always present.
    • For example, a researcher wants to do quantitative research and determine the effects of classical music on students’ academic performance. Since this is true experimental research, it must have at least two groups. One would be the control group, and the other one would be the test or experimental.
      • In the control group, everything is held constant or regular, which means that there would be no intervention, which means there is no music or no music played in their classes.
      • While in the test or experimental group, there is the presence of intervention which is classical music. This intervention can further be manipulated with variables such as frequency and music volume.
      • At the end of the study, the researchers assess the control without music. And test or experiment with music by looking at their academic performance.
  • Based on this example, we conclude that played classical music was studying as a positive effect on the student’s grades.
• Quasi-experimental
  • • Similar to true experimental research, this type of research aims to determine causal relationships among variables.
    • It bears a resemblance to true experimental research but different because participants are not randomly assigned in this type of research. It involves the conduct of the pre-test and post-test.
    • Furthermore, other characteristics of the Quasi-experimental research involved variable manipulation. It’s also conducted in a controlled setting, and that the intervention is also present.
    • For example, a teacher wants to do quantitative research and test a reading intervention program to aid students with a low level of comprehension. Since this is Quasi-experimental research, this study would usually start with the pre-test.
      • The pre-test would be a reading comprehension test that would help determine who among the students has passed and who has failed.
      • Recognizing the students to a field is essential as these students will be considered participants for the reading intervention program.
      • Once the participants have been identified, they shall undergo their reading intervention program, which can further manipulate them through the duration of the program, the schedule given to the students, the reading activities given, and the teaching strategy that is used.
      • After undergoing intervention, we now proceed with the post-test, which is another reading comprehension test. The students who spilled their reading comprehension tests given before would have experienced the reading intervention program and will then be given another reading comprehension test to determine whether they will pass or still fail.
      • If they have passed, we can conclude that the reading intervention program effectively improves the reading comprehension of students with low-level understanding.

Similarities between True and Quasi-experimental design of quantitative research

1. Both are used to determine causal relationships
2. Both have interventions
3. Involves manipulation of variables
4. both are conducted in a controlled setting

Differences between True and Quasi-experimental design of quantitative research

1. There is a random selection of participants in True experimental, but in Quasi, the selection is not random.
2. True experimental may be conducted with or without a pre-test, while Quasi-experimental research involves pre-test and post-test.
3. True experimental research involves a test group and a control group, while Quasi-experimental research does not have any control group.

Quantitative Research – Not experimental

• According to Purpose
  • Surveys
    • • • Survey research gathers evidence on people’s knowledge, opinions, attitudes, and values on various issues and concerns.
        • The survey research makes use of questionnaires, interviews, and surveys. Also, in survey research, the variables are not controlled or manipulated, and no intervention is applied.
        • For example – A researcher wants to survey the preference of individuals towards mass testing. What he does is identify the participants and give them the questionnaires that they will answer.
        • Again, there is no intervention in survey research because the researcher would only wait for the respondents to give out their answers. Also, in survey research, researchers focus on recording and tallying the responses, analyzing and interpreting that data.
        • From these detailed data, he comes up with conclusions. The data that the researcher was able to identify is that those who answered yes are 75 percent, while those who answered No are 25 percent. From this detailed data, the researcher then concludes that the majority, or 75 percent or therefore Group A, favors mass testing.
  • Correlational Research
    • • • The correlational research seeks to interpret the degree of relationship between 2 or more variables using statistical data. Similar to the survey research, the variables are not controlled or manipulated, and no intervention is applied here.
        • It is also worth noting that while correlational research aims to interpret the degree of relationship between variables, it does not seek to determine cause and effect relationships.
      • For example, the researcher wants to do quantitative research to determine the influence of online gaming on students’ critical thinking.
        • • In the study, he hypothesized that there is no significant relationship between online gaming and critical thinking.
          • To conduct this detailed research, the researcher needs to find online gamers who will agree to become participants in our study. Again in the correlational analysis, there is no intervention because the responsibilities of researchers focused primarily on gathering the needed data, applying the correct statistical treatment, analyzing and interpreting the treated data, and rejecting or accepting the hypothesis.
          • Now going back to our online universe, these participants are then given instruments that would assess their online gaming habits and their critical thinking level.
        • From there, we will give the appropriate statistical treatment to determine whether there’s a relationship between online gaming and critical thinking and whether an increase in online gaming would increase critical thinking or vice versa.
• According to time
  • Cross-sectional research
    • • • Cross-sectional research involves gathering data at a single point in time. It focuses on the same set of variables, after which comparisons are made across variables of interest.
        • For example – A researcher once identified the spending trends between men and women in their thirties. From this detailed research, the findings came that women tend to spend more money than men.
      • The researchers then take this particular data in cross-sectional research and compare it with other variables like age brackets. He will then compare whether there are similarities or differences in terms of the trends and results.
  • Longitudinal
    • • • In longitudinal research, the data is collected at multiple points in time. A researcher collects data from the present and again in the future to compare datasets.
        • For example – A study on the effects of antiretroviral medicines as a maintenance drug for HIV-positive patients needs to be done. The participants for this particular study are individuals who tested positive for HIV.
        • At the start of the longitudinal research, their HIV viral load is taken and reported before they are given the medicines. After which their viral load is monitored on a specific schedule to determine how the drugs are working.
    • Based on this particular example, you could say that it takes quite a long time before this specific study is finished, hence longitudinal.

As researchers, it is essential to understand the Quantitative research types. This knowledge will help us to determine the best research design for the research objectives.

In this post, I’ve covered the basics of Quantitative research types. If you found my post helpful, then do share it with your friends and colleagues. If you have any feedback/questions, you may leave a comment below.

Click here to know more about me.

The post Quantitative Research – All types explained in 1 post appeared first on PRASHANT AGGARWAL.

• Someone who is facing difficulties in interpreting the data.

• Someone who wants to improve the data interpretation/reading skills

In this post, I will give you 2 important tips which will change the way you approach any type of data. This will definitely help you in interpreting data like a pro.

“Our problems are not with the data itself, but arise from our interpretation of the data” – Bruce. H. Lipton

1. Perspective/Hypothesis

The importance of having a perspective before looking at the data is a bit under-rated in the real world. When I started my career in market research back in 2011, not having a perspective before looking at the data was one of my biggest mistakes. I used to get so lost in the data that I would forget what I was really looking for. I see this challenge with many new professionals in the industry which is why I am sharing my learnings over a period of time.

Now, as a first step before jumping into a data ask yourself what you are really looking for or what you are expecting the data to tell you?

For Example – You are about to look at the monthly car sales data and see if the trend is increasing/decreasing. Now, before jumping into the data you should have a perspective/view on the trend beforehand as well. Suppose, you have a view that the car sales would be declining because the economy is in bad shape.

Now with this perspective when you look at the data, you would immediately get your answer.

• If the data shows that the car sales are declining – you’ve successfully understood that data.
• But, supposing the data shows that car sales are increasing against your perspective. You will find a feeling on inquisitiveness inside you to find the underlying reason for this.

As a result, you will automatically start looking for answers and deep dive further.

This is just a simple example to explain the power of perspective before reading the data. This is applicable to any data you want to read.

2. Data Type

Many times we do the mistake of reading qualitative data quantitatively and vice versa. It is important to have clarity in the back of your mind whether you are looking at quantitative data or qualitative data. For the purpose of this post, I’m not going into details about these data types as these are wide topics to cover.

Knowing the data types reduces the chances of misinterpretation of the data.

For Example – Qualitative data can tell you exhaustively what are the various car segments available in the market. But, only Quantitative data will be able to tell you the size and growth of each of these segments.

When you are analyzing the Quantitative data, the concept of the Significance test plays a very important role. For example, the size of the hatchback segment in two independent populations comes out to be 25% to 26% respectively. Now, this may look like 1% more in 1 data set than other but it is not statistically significant to be able to confidently say that.

Similarly, in Qualitative data, if a particular segment size of the car seems bigger than others it only gives a hypothesis and not a conclusive picture.

These are small examples to explain how these data sets can be misinterpreted.

Following the above 2 points will definitely improve your data analyzing skills. these are also the 2 big mistakes that I did when I started my career and hence, wanted to share my learnings for the benefit of everyone.

If you found my post useful then do share it with your friends and colleagues. If you have any feedback/questions, you may leave a comment below. I will definitely reply.

Click here to know more about me

The post Interpreting Data like a Pro in 10 mins appeared first on PRASHANT AGGARWAL.