Data Wrangling in Market Research: A Comprehensive Guide

Data Wrangling in market research: A comprehensive guide

With the growing availability of data and the increasing use of technology, it’s easier than ever to collect, analyse, and communicate data. However, this can also be an enormous challenge for market research professionals. Data wrangling is uniting disparate datasets into a single information system. Think of it as connecting all the different dots that make up your study.

Market research is more than asking people questions and collecting feedback. It’s about extracting insights from a comprehensive set of data and turning them into actionable results. But how?

Data wrangling is the approach of transforming raw data into a more helpful format. Once you have all your data ready, it can be challenging to know where to begin. Luckily, there are numerous ways to transform raw data into something valuable and actionable. But remember, data is just one part of Competitive Intelligence. Without the rest, then, the insight it provides is questionable.

This comprehensive article will teach you everything you need to know about data wrangling in market research — including different methods, tools, and resources. Read on to learn more.  

What is Data Wrangling?

Data wrangling transforms raw data into a more helpful format. Several methods are used during data wrangling. The process helps to transform raw data into a structured layout that is easier to process and analyse. Data wrangling is often called data munging. 

In this process, raw data (often in CSV) is converted into a meaningful format for analysis. It encompasses transforming raw data into meaningful and valuable information that can be readily understood by business analysts, stakeholders, and marketing departments. 

The primary benefit of data wrangling

The primary benefit of data wrangling is that it provides a standardised and consistent format that can be used as input to any analysis tool or system. In addition, it can provide additional insight into the data, such as trend lines, conditional formatting, and calculating metrics like ROI or CPA.

Various tools can be used for data wrangling. When performing data wrangling, you must follow specific steps to ensure the final result is useful for your research. When collecting data, it is essential to validate and clean it so that no errors are present. 

What Are the Key Steps Involved In Data Wrangling?

Data wrangling involves the following tasks:

• Cleaning and preprocessing: Cleaning and preprocessing involve taking raw data and converting it into a usable format. It is also called deseasoning, un-trending, and scrubbing data.
• Transformations: Transformations involve basic manipulations such as joining, combining, extending, squishing, and summarising data. These operations can either be manual or automated.
• Visualisation: Visualisation is the final stage of data wrangling in which information is displayed in a way that is easy to interpret and understand. Based on how we would like to present our findings to a client or audience, it can generally be classified into two types: report building and storytelling.

What Are The Types of Data Wrangling in Market Research?

There are various types of data wrangling in market research. We will discuss each type in detail below. 

Data Visualisation

Data visualisation, also known as charting, is a technique for displaying data. It involves graphically representing data to help people gain insight. When done well, data visualisation can bring data to life and powerfully convey information. 

Data visualisation is often used to visually explain complex concepts and to obtain quantitative information. Graphs, charts, and diagrams are the most common visualisations. For that, utilising a market research tool such as Helpfull is beneficial, which offers you real-time downloadable data with granular data visualisation. 

Data Analysis

Data analysis is a process of finding insights in data. It involves transforming data into a more meaningful format to analyse it. Data analysis consists of exploring data, finding patterns, and making insights. It can be done manually or using software.

Data Scraping

This scraping, also known as data pulling or data extraction, generates a copy of data from a website or API. It helps you get data from one source, like an API, and bring it into your system.

Data Transformation

Transformation is the process of changing the type of data or format. For example, you may have data in spreadsheet format, but you want to change it to CSV format.

Data Cleaning

This is taking out any erroneous or unnecessary data. For example, you may have a survey where responses are missing, or the data from a tool may be faulty. In these cases, cleaning the data is necessary.

How to Organise Research Data For Data Wrangling?

As we discussed above, data wrangling transforms raw data into a more useful format. Ensuring that your research data is structured and organised while performing data wrangling is essential. You must ascertain that you follow specific rules while managing your research data. It is important to note that data wrangling is not only limited to using a particular tool.

You can use different techniques to make your data organised and structured. When you are performing data wrangling, you need to make sure that you are following specific rules. You can use different techniques to make your data organised and structured. However, here are a few basic rules for organising your research data.

Rule1: Keep data in a database

Most tools allow you to store your data in a database. This makes it easier to find data later, share it with other team members, and keep track of the data as it flows in and out of the project.

Keeping related data together, like survey responses, raw data, and comments, is useful in one folder or database. It will make it easier to find data later and make sense of it altogether. Know the types of information kept in your research data and how it is categorised. It helps you to organise your research data better. 

Rule 2: Create subfolders 

It is also useful to create subfolders to keep related data in them. For example, a folder named “Survey Responses” could contain subfolders named “Question 1” and “Question 2.” It will help you group related data together and make it easier to find related data later. 

Rule 3: Use labels for your data

Label your data to make sense when you look at it later. For example, instead of writing the question number for a survey, you could label the column with “Question 1” and write the question number underneath. This will make it easier when you are looking at your data later.

Rule 4: Short Your Data

It is important to sort your data before you start working with it. This will help you find data more efficiently. Use a file template to organise your research data. A file template is a pre-designed structure that you can use when you are creating individual files for your research data. The file structure should be such that it follows a logical order and allows for easy retrieval.

What Are The Key Benefits of Data Wrangling?

Several key benefits of data wrangling in marketing research can help marketers improve insights and make better decisions. 

So wrapping up Market research is a treasured part of any business owner’s operations and is key to developing new products and services. An effective marketing plan is key to building a successful business. Data analysis and wrangling aspects are crucial to successful marketing plans.

Data analysis is vital for market research professionals because it helps them to find the best target market for a product or service. Market research professionals need to have the correct data analysis skills as it allows them to find the right markets for the products and services. Hiring a growth marketing agency is a good choice to develop effective marketing plans. With data-driven insight, these market research professionals ensure profitable and sustainable growth. With the precise data on the side, they set a framework to scale revenue fast.

Treat it with respect

Since the data you will be wrangling is collected from the market, it’s best to treat it respectfully and follow a few golden rules to get the best results. The first golden rule is: Don’t Whine! If the data you’re working with is full of errors and anomalies, don’t look for excuses for your bad results. Instead, take ownership of your findings and iterate until you are satisfied with your product. 

While it’s unnecessary to discuss every detail in your product, it’s essential to stay as concise as possible while remaining accurate. This will help make your findings less prone to manipulation by others in the team or by faulty interpretations or interpretations of inaccurate data.

Also, adopting an endpoint management application with an intelligent, decentralised platform is a wise choice to safeguard the organisation’s data. These applications help maximise the security and compliance of your endpoints across any network and prevent cyber threats such as data theft or ransomware phishing attacks.

AUTHOR BIO: Growth Manager

Padmaja Santhanam is the Growth Manager at FirstPrinciples. She manages all inbound business development and outbound prospecting pipeline-building strategies. Padmaja’s expertise lies in positioning and managing SaaS companies’ growth. 

As a growth marketer, her role encompasses client onboarding to ensure the expected linear growth. She leads the marketing team to make strategic adjustments accordingly. Padmaja is actively working with the marketing & sales team to achieve the defined targets. Besides, she leads the other managers of Sales Development. 

Padmaja has decades of experience in business analysis, project coordination, and project management. She has worked with organisations of every size. For the last 48 months, she has forayed into Marketing and Growth and managed projects.