Content Marketing

A Marketer’s Guide to Conducting Data Analysis

Team Pepper
Posted on 4/05/227 min read
A Marketer’s Guide to Conducting Data Analysis
All growing businesses rely on data analysis today. Here’s your guide to conducting it effectively.


More and more businesses are investing time and money in data analysis to scale their operations. The process of data analysis is now a core component of any growing business. It is no more confined to just research in academics. More and more businesses are resorting to the analysis of different types of data gathered from the market, stakeholders, and other important sources. A lot of the techniques, of course, have been borrowed from data analysis in research, which saves time and effort on the part of the researcher. This is true specially when marketers are trying to roll out innovative strategies almost every day to keep the business relevant and thriving.

Before understanding how to conduct data analysis, let’s first get the basics out of the way. How does data analysis help in marketing?

What Is Data Analysis, and Why Is It Important?

The process of analyzing data in order to inform future plans and forecast roadmaps is what constitutes data analysis. It involves different formats to conduct secondary analysis, crunch numbers, and then compile the information in the chosen structure to present relevant findings to inform the way forward, as well as to gain insights from past performances. There are so many different types of data that are available that it may get difficult to standardize them. That is where data analysis helps: it helps in the cleaning and transforming of data into a standardized set of information.


Data analysis can be quantitative or qualitative. Either way, it enhances productivity and helps the business to scale. Through the process of analyzing data, researchers and marketers extract useful information on consumer behaviors, and patterns that are dynamic, and vary over time. This is a critical component, as it informs product development and innovation too.

The Significance of Data Analysis in the Growth of a Business

Almost all businesses today rely on the power of data analysis to scale and reach new audiences. So, how does it really help?

1. Understanding business value chain

There are experts who can help you dig out data and insights from your business value chains. This type of information basically helps you decipher the foundational growth strategies of your company.

2. Knowledge of the industry

Through secondary analysis, you can gain an in-depth understanding of the industry you are part of. This is helpful to assess your position in the market and figure out your competition. It also helps build a broader understanding of how the economy is performing, vis-à-vis

the particular sector you are in.

3. Forecasting new opportunities

As a business, if you are constantly engaging in data analysis, there is a good chance that you will be able to cull out relevant opportunities that will be presented to you in the form of insights and numbers. This will again shape your strategies in the future.

4. Data analysis is in demand

The demand for data analysis and analysts is on the rise. As a business, if you are not tapping into this growing domain that is informing the future of businesses, you stand to lose a great deal with respect to your competitors.

5. Informing technology adoption

Data analysis in research is quite similar to its counterpart in marketing and business. Just like in research, the findings inform policy changes, etc., so also in businesses, the information gathered through data analysis is used to address the kind of technology and other resources that need to be adopted for the business to innovate and stay relevant.

6. Data analysis

Marketing is one domain that needs a lot of data and descriptive analysis in order to catch the pulse of the audience. Otherwise, all marketing strategies will have little impact on the business’ performance. And eventually, the product or service will become redundant without any takers.

Types of Data Analysis in Marketing

Data analysis in research can be broadly categorized into two categories: qualitative and quantitative. As the names suggest, the former deals with non-numerical data such as symbols, images, summaries, etc. Quantitative analysis, on the other hand, deals with numerical data only.


1. Qualitative data

Qualitative data analysis in marketing can reveal useful insights into consumer behavior. It seeks to understand the likes and dislikes of a consumer, their buying behavior, etc. One way to conduct qualitative research for marketing purposes is to hold detailed interviews with clients and organize focus groups.

2. Quantitative data

Quantitative data refers to information gathered by using methods driven by numbers, facts, and statistics. This can be used to gauge how many consumers like a particular product, the volume of traffic on your website, and more. Polls, surveys, and questionnaires are some common methods to do quantitative data analysis.

Remember: based on your goals and objectives, you can choose to integrate both qualitative and quantitative methods to analyze data.

How to Conduct Data Analysis

Data analysis is a cornerstone of all growing businesses. More and more businesses are looking to hire experts who are adept at data analysis and data science. The following are some key points to remember while conducting data analysis from a marketing perspective.


1. Define your research goals

The best way to approach data analysis is to ensure that you have clearly laid down the objectives and questions for the research. This determines the approach you will take for the research. Ask yourself why you are conducting the analysis: is it to calculate the impressions on your website in the last six months? Is it to gauge your audience’s buying habits?

2. Organize your data

Use any electronic medium to organize and transfer the collected data. Ensure that you are entering the right data in the right places. Maintain a master file that you can keep going back to from time to time.

3. Have a plan

Have a proper plan around how to use the data. If you are sitting on a pile of data, not knowing what to do with it, then that’s a huge problem for the business. Your plan should not only include how to use this data, but also to ensure a high return on investment.

4. Focus on data discovery

Data discovery helps businesses trace patterns in data to gain insights into their operations. Add data discovery to the initial stage of planning to tap into data silos. Data silos are data available across multiple sources, such as websites, mobile phones, spreadsheets, etc., which need to be looked into as part of the data discovery process.

5. Break it down

As a marketer, you need to focus on uncomplicating data. This means that it is okay to start with the basic data sets, like those available from company websites and surveys, and gradually move to more advanced data.

6. Maintain data accuracy

Always check for data accuracy, especially when it is text or narratives. Most marketers struggle with this. Inaccuracy or discrepancies when evaluating data lead to ineffective marketing strategies.

7. Present the data accurately

It is easy to get lost in the large volumes of data during a data analysis process. So, it is always a good idea to convert huge number chunks into graphs and tables, which are easy to read and make sense of.

Focus on the presentation of the data and the findings. Using fancy charts may make the reports visually appealing, but they may not always be the best way of presenting the data. You may still have to use tables, or even Excel files to corroborate your findings and recommend the way forward.

8. Find practical applications of your findings

Know how to leverage the data to reach your target consumers. Herein lies the importance of data analysis. A huge part of the process is just to gather information and choices of people in order for the business to offer tailored content, products, and services to them.

9. Use both online and offline methods to gather data

This means doing a thorough secondary analysis, along with meeting real people and observing real events or situations, to collect information. Leverage social media platforms like Twitter and Instagram to gather more data on consumer trends and choices.

How to Write a Well-Structured Data Analysis Report

The final segment of understanding data analysis is how to write an effective report, and present the findings. A data analysis report should be easy to read, with simple structures and presentations. The following is a broad outline of a well-structured data analysis report, which can be used as a guideline.

1. Introduction

Always start your report with a compelling introduction. The cover page should contain the title of the report, reflecting what it presents. This should be followed by a brief introduction that may cover the following points

A brief summary

Background of the stud

Research objective

Summary of conclusions

● A brief outline of the report

You may also insert a list of acknowledgements at the start of the report, if you had a team working on it together.

2. Body

You can choose to write the body, which is the main part of the report, using different formats. But all of them should talk about the methodology, data findings, analysis, and results. Alternatively, you can also opt for a single section that contains the analysis. Here, you can use different sub-sections that cover the various questions used to gather the data. Under each of these, you can add the statistical methods, specific findings, analysis, and results.

3. Discussions or Conclusion

This section should draw on the body to further elaborate some key findings, and offer recommendations and/or conclusions. You can also use this section to raise new questions, if any, that may inform future data analysis.

4. Appendices

This is the space where you put together different supporting data sets like survey questions and responses, a list of respondents, references used for secondary analysis, tables, charts, figures, or computer codes. You can also add images, or video links, relevant to the study.

Key Takeaways

Data analysis is the process of analyzing data to gather insights and predict future performance.

Data analysis can be quantitative or qualitative.

Data analysis is important for businesses to understand the value chain, assess the current industry and market trends, and to forecast future data and performance.

Learning how to conduct data analysis involves organizing and coding the data, running statistical tests to gather findings, and assess statistical significance, and finally presenting those for predictive analysis.

● A well-structured data analysis report contains a cover page, an introduction, a body, a section on discussions and conclusions, and finally, appendices.

The importance of data analysis in growing businesses cannot be emphasized enough. So, if you are not crunching numbers and gathering important information on consumer behavior and market trends, then it is probably time to shift gears and open up to this whole new world of possibilities.


1. How to conduct data analysis in research?

The process of data analysis involves the important steps of organizing the data, running statistical tests, and presenting the findings through a well-structured report.

2. How to write a data analysis report?

In order to write a data analysis report, you have to follow a prescribed structure that talks about the goals, methods, samples, and key findings.

3. What is the purpose of a data analysis report?

The purpose of a data analysis report is to help predict and forecast future performances of businesses by recommending effective strategies drawn from past insights.