Beginner’s Guide to Data Analysis for Marketing

Data Analysis is the systematic application of logical and statistical techniques on raw data to gain useful information. Through data analysis, you can separate the phenomenon of interest from the ‘noise’ in the data.

You can also use data analysis to describe, condense, and evaluate data. 

The data analysis process can be used to guide decisions and actions in all fields. For instance, in the field of real estate, tools with data analysis capabilities can help automate real estate evaluation, optimize buyer selection process, monitor trends, and boost ROI. 

AI (Artificial intelligence) has just added to the potential and possibilities of data analysis.

The future of data analysis seems bright due to the growing emphasis on data-driven decisions and strategies in companies and the advancement of artificial intelligence and big data. The global data analytics market is set to grow at a CAGR of 29.4% during the period between 2023 and 2032.

Data analysis can help you generate new content ideas and unlock marketing opportunities. 

Reasons to Learn Data Analysis

Although high demand and predictions of high demand are reasons enough to raise curiosity, you may still want to know why you should learn data analysis and how this knowledge can benefit you.

Here are a few reasons (from the multitude) to dive into data analysis:

• Using data analysis, you will be able to comprehend audience preferences and engagement in a better way. With this understanding, you can create content that will resonate more with your target audience, and run highly effective and successful marketing campaigns.
  
• You can forecast the ROI of marketing campaigns with data analysis. These forecasts will help you make better decisions about your marketing budget. You can also optimize marketing campaigns for better ROI with data analysis.
  
• Companies use data analysis to discover and select influencers for their marketing campaigns. Data analysis also helps in identifying genuine influencers.
  
• You can be proactive instead of reactive; data analysis can be integrated with social listening and you can use the insights to guide your strategies. Combining social listening and data analysis will help you effectively identify, engage, and nurture leads.

• If you leverage data analysis well, you can gain a significant competitive advantage. 

Primary Methods of Data Analysis

Methods of data analysis differ based on the type of data collected. These methods may be used in combination or on their own, depending on the requirements. The two primary methods of data analysis are quantitative data analysis and qualitative data analysis. 

Quantitative data analysis involves numerical variables. In the context of social media, quantitative data analysis might revolve around metrics such as base, reach, engagement, and conversion.

Base and reach will guide your strategies and tactics, while engagement and conversion metrics will provide you insights into your actions and their effects.

Qualitative data analysis is more nuanced and difficult. It may include tracking and analyzing factors such as engagement quality and audience sentiment.

Most qualitative data analysis is still conducted manually, although user-friendly software and advances in natural language processing (NLP) are changing this.

Data Collection and Preparation

Data helps in measuring past performance, but it also informs future (and present) campaigns.

One of the greatest benefits of technological advances to marketers is the ability to collect data in real-time. Within a few hours of the launch of your marketing campaign, you will be able to gauge its initial success.

With data analysis, you will be able to gain a thorough understanding of the market that can help you pivot if required. 

Data Sources

These days omnichannel marketing campaigns are common. This means multiple data sources and lots of data. To stay on top of events, marketers have to monitor multiple platforms and collect data from a variety of sources.

As a marketer, you will have to work with Google Analytics, Google Search Console, advertising platforms, social media platforms, and your CRM (Customer Relationship Management) tool. The sales team and technical support team may also be able to provide you with vital data.

You should also avail of other public data sources for acquiring data that will help you better understand the consumers, geography, the industry, and the economy as a whole. 

Data Cleaning

To clean data is to remove or fix any data that is incomplete, incorrect, duplicate, corrupted, or in the wrong format. When you combine multiple data sources, there are more chances of data duplication, and such data can be misleading.

Due to incorrect data, algorithms and outcomes become unreliable. The data cleaning process will vary according to the dataset, so you will need to establish a template for a cleaning process that suits your data. 

Data Exploration

Data exploration will help you understand the nature of data. You could say that it is the first step of data analysis or initial data analysis.

For data exploration, you could filter data into a spreadsheet manually or write scripts to serve the purpose.

The third option in front of you is using automated data exploration tools. Data exploration will make it easier for you to assign and communicate the meaning of the rows and columns of data. 

Basic Types of Data Analysis

Each type of data analysis serves a specific purpose and follows a different method. Here’s a quick rundown of the basic types of data analysis:

Descriptive Analysis

With descriptive analysis, you get a comprehensive overview of the data, its structure, and properties. You will be able to understand the main characteristics of a dataset.

Inferential Analysis

In inferential data analysis, you will be analyzing data from a sample to make inferences about the greater population. It involves making evidence-based almost accurate generalizations. 

Predictive Analysis

Predictive analysis helps you predict events or outcomes based on past data. You will be able to identify patterns in historical data using statistical models and algorithms. Machine learning algorithms will also make reasonably accurate predictions based on these 

Prescriptive Analysis

Based on past marketing data, prescriptive analysis can recommend the most impactful actions that you can take in the present and future. You can use a prescriptive analysis tool along with a marketing automation tool to ensure instant implementation of recommendations. 

Text Analysis

With text analysis, you will be able to acquire meaningful insights from unstructured text data. Text analysis involves the use of techniques such as NLP (Natural Language Processing), topic modeling, and text mining. 

Tools for Data Analysis

Microsoft Excel

Microsoft Excel still wears the crown when it comes to data analysis. It remains the most sought-after application for data analysis because of its simplicity and versatility.

With Microsoft Excel, you can easily import, clean, explore, and analyze your data. Even if you have never worked with Excel before, you can easily acquire the necessary skills through online Excel training courses. 

Python

Python is a relatively easy-to-learn programming language. It is commonly used for data analysis and data visualization due to its scalability, flexibility, and impressive libraries.

A comprehensive understanding of Python can prove extremely beneficial for performing data analysis. 

R Programming

R is an open-source programming language that you can use to analyze data. The incredible and powerful packages of R make data computation, manipulation, and visualization easier and faster.

Once you get started with R, you will find that it has quite an easy style of coding. 

Data Analysis Software

A wide variety of data analysis software tools are available now with varying capabilities and features. SAS, Jupyter Notebook, and Tableau are some of the popular data analysis software.

In case you are stuck with a legacy system, Door3 services can help modernize the system, migrate the data, and build the required functionality in the new system. 

Ethical Considerations in Data Analysis

Data Privacy

One of the major concerns around data and analysis is the protection of privacy. You need to ensure the privacy of sensitive and personal data during collection and analysis. There needs to be transparency in the request for data. Start with a robust privacy policy and ensure regulatory compliance. 

Bias and Fairness

Although you won’t be able to eliminate bias in data analysis, you need to be aware that it exists (in the data and the people conducting the analysis) to actively try to mitigate issues and avoid its adverse impact on decisions. Fairness in data analysis means using data in a way that does not reinforce or create bias.

Data Security

Data security is not to be confused with data privacy. During the collection, cleaning, storing, and analysis of data, you need to ensure that it is safe from hackers and ransomware. Attacks on the data could potentially lead to a loss of revenue, customer trust, or both. Data is an asset to businesses, and it should be protected accordingly. 

Conclusion

Data is rightly considered the ‘new oil’. And, if data is considered as valuable as oil, then data analysis can be likened to the extraction, refining, and separation of oil to make it suitable for different uses. 

In the context of marketing, companies are able to offer personalization of experience due to data analysis. This holds benefits for both companies as well as consumers.

To make smarter business decisions, you need to harness the power of data analysis. In this data-driven world, data analysis has become a crucial skill.