Web Research

To ensure the success of a business, it is necessary that they have products and services that meet the needs of the client, and also offer an incredible experience during the time they interact with the brand. But how can you tell if you are succeeding? You can do it quickly and effectively through web research.

What is web research?

Online research is a data analysis method that is done over the Internet.

Through this method, companies can collect a large amount of qualitative and quantitative data, thanks to the different tools we have.

With the growing use of the Internet, web research has become a popular tool among companies, as it allows evaluating the performance of a product or service and obtaining information on consumer buying behavior.

What is the data analysis process?

The Data Analysis process involves the collection, transformation, cleaning and modeling of data in order to discover useful and important information for the interests of the organization.

The results thus obtained are communicated, conclusions are suggested and used to support decision-making.

The data analysis process consists of the following phases of an iterative nature:

  • Specification of data requirements
  • Data collection
  • Data processing
  • Data cleaning
  • Data analysis
  • Communication

Web Research

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A better analysis of the data is needed.

With the right data analysis process and tools, what was once an overwhelming volume of diverse information has become a manageable and effective resource that helps to understand diverse situations and justify decision making.

Specification of Web Data Requirements

The data required for the analysis is based on a question or an experiment.

Based on the requirements, the necessary data is identified, from the population or data collection to the variables or specific attributes of the same.

Questions or experiments related to your interests there are as many as your imagination gives you, here are some common ones …

  • Will this customer renew their subscription?
  • Is this combination of purchases very different from what this customer has done in the past?
  • How many new followers will I get next week?
  • What will my fourth quarter sales be in Madrid?
  • Is there a market for my product?
  • Am I getting the right message?
  • Is it worth investing in creating this resource or product?
  • How likely is it that each of my clients will leave my business to go with the competition in the next year?
  • How likely is this user to click on my ad?
  • What kinds of buyers have similar tastes?

A Better Analysis Of The Data Is Important


Data collection in the data analysis process deals with the collection of information on the variables selected as data requirements. The emphasis is on ensuring accurate and honest data collection. Data collection ensures that the data collected is accurate in such a way that the related decisions are valid. Data collection provides both a baseline to measure and a goal to improve.

Data is collected from various sources, from organizational databases to information on web pages or social networks. The data thus obtained may not be structured and may contain irrelevant information. Therefore, the data collected requires to be subjected to data processing and cleaning. Here are some common data sources …

  • User or customer database
  • Polls
  • Google analytics
  • Social networks
  • Comments on a blog
  • Emails
  • Official databases


The data that is collected must be processed or organized for analysis. This includes structuring the data as needed for the relevant analysis tools.

For example, the data may need to be placed in rows and columns in a table within a spreadsheet or in a statistical application. You may have to create a data model.

Techniques such as data mining, natural language processing, and text analysis provide different methods for finding patterns in this information or for interpreting it in some other way.


At this stage of the data analysis process, it is performed because processed and organized data may be incomplete, contain duplicates or contain errors. Data cleansing is the process of preventing and correcting these errors. There are several types of data cleansing that depend on the type of data. Similarly, quantitative data methods can be used to detect outliers that will later be excluded in the analysis.

Here are some examples …

  • Incorrect or no data email addresses have a significant impact on any marketing campaign
  • Inaccurate personal data can lead to lost sales opportunities or an increase in customer complaints or goods can be sent to the wrong places
  • Incorrect product measurements can lead to significant transportation problems

Data generally only has value when it supports a business process or decision making at the organization level. Quality standards are required in this context.


This is the fundamental phase of the data analysis process, to which we arrive with the processed, organized and clean data. Various data analysis techniques are available to understand, interpret, and draw conclusions based on the requirements.

Data visualization can also be used to examine data in graphical form and to obtain additional information about messages within the data. Statistical data models such as correlation and regression analysis can be used to identify relationships between data variables.

These descriptive data models are useful for simplifying analysis and communicating results. The process may require additional data cleansing or additional data collection, so these activities are iterative in nature.


Once the fundamental phases of the data analysis process are completed, the results should be presented in a format as required by users to support their decisions and future actions.

User feedback could result in additional analysis.

Data analysts can choose data visualization techniques, such as charts and graphs, that help communicate the message clearly and efficiently to users.

Graphical data analysis tools provide ease of highlighting required information with color codes and formatting in tables and graphs.

In short, a better analysis of the data is needed.
With the right data analysis process and tools, what was once an overwhelming volume of diverse information has become a manageable and effective resource that helps to understand diverse situations and justify decision making.