For company owners, relying on data to make better choices has gone from a competitive advantage to a new standard, regardless of industry.
Big Data is a priceless tool that allows companies to better forecast outcomes and enhance future strategies that build on previous experiences. Humans actively consider what other people will do and make decisions accordingly. Monitoring data for repeated trends and actions allow organizations to better target and engage consumers.
In today’s online marketplace, consumer behaviors need to be monitored in order for marketing campaigns to be effective. Online shoppers face millions of choices, making the retail industry incredibly competitive.
In order to excel in online shopping, you need to stand out from the crowd. Retailers would continue to see higher sales figures and higher consumer loyalty ratings by making accurate demand forecasts based on results.
Now, the era of Big Data has arrived, and with it comes the opportunity to forecast the future, which is becoming an incredibly important aspect of modern business practices. Whatever the sector, doing business today necessitates immersing yourself and your company in a plethora of chaotic, unorganized, real-time data from consumers, rivals, and industries — and figuring out how to leverage that data to predict what’s next.
Do Big Data tools help in predicting patterns and behaviors?
There are lots of resources available to predict consumer behaviors. Many industries are growing by analyzing Big Data to make better decisions for the future. Google Analytics and Oracle are two examples of Big Data tools to analyze trends and predict consumer behaviors.
What is Big Data?
The word “Big Data” is often overplayed and misunderstood. According to the simplest meaning of the word, Big Data sets are analyzed to identify trends and patterns. Big data is used in consumer behavior research to evaluate data points during a customer’s path from discovery to purchase, providing advertisers with the resources and information they need to make smarter decisions.
There are three types of Big Data that advertisers usually deal with:
- Predictive data is information that can be used to make better choices about the future by predicting what may happen.
- Descriptive data are details that help to clarify what happens in a given situation.
- Prescriptive data refers to information that suggests decision-making choices based on the findings of predictive and descriptive research.
Predictive analytics is the most commonly used data collection of customer behavior. In this post, we will be studying what tools businesses use for predictive analysis of customer behavior.
Big Data Tools Used For Predictive Analysis
Image source: https://www.marketingevolution.com/hubfs/the-role-of-predictive-analytics-in-data-driven-marketing.jpg
Big Data is a huge concern, and it requires collecting outstanding information and using tools to make sense of the information.
“Data is the new science. Big Data holds the answers.” Pat Gelsinger, Chief Executive Officer at VMware
With Big Data’s accelerated growth, the industry has been overwhelmed with its numerous tools. These Big Data tools assist in achieving more significant cost savings and, as a result, improve the speed of research. Below are some Big Data tools that are used to analyze and predict consumer behavior and patterns.
Hadoop:
In the Big Data sector, Hadoop is one of the most widely deployed tools. Hadoop is an Apache open-source application that operates on popular hardware. It’s used to collect, process, and interpret large amounts of data. Hadoop is a Java-based framework. Since it operates on several computers at the same time, Hadoop allows for parallel data processing. It is designed using a clustered structure. A cluster is a set of devices linked by a local area network (LAN).
It is divided into three sections:
- HDFS (Hadoop Distributed File System) is Hadoop’s disc layer.
- Hadoop’s data processing layer is called Map-Reduce.
- YARN – YARN is Hadoop’s inventory storage layer.
Using Hadoop, massive quantities of Big Data are produced, which can be used to gain a detailed view of demand and sales volume using Predictive Analytics.
Google Analytics:
Google Analytics offers several bits of information to help interpret user behavior when they communicate with the platform or service with limited instrumentation. The number of people who connect with your app, the number of connections they make, and the screen or web pages they access are all standard metrics.
In Google Analytics, user data is collected through first-party browsers, procedurally chosen IDs, or a smartphone SDK. Users can opt out of Analytics on websites and SaaS applications by adding a plugin add-on to their web browser. Inside smartphone apps, they can adjust their configuration to opt out (if supported by the mobile app).
Google Analytics helps monitor the number of sales and revenue generated by the website or mobile app with improved E-commerce analytics. Product reviews, product searches, displaying product information, adding a product to a shopping cart, starting the checkout process, sales, and refunds are all monitored through improved Ecommerce monitoring.
Oracle:
Oracle is a cloud-based service that offers a secure platform for Data Science, Machine Learning, and Data Analytics. It’s a framework that encompasses the whole Data Science lifecycle, from data planning to machine learning to predictive model implementation. For miniature, medium, and wide exclusive editions, it provides a variety of licenses.
RapidMiner is a Java-based open-source application. RapidMiner is highly effective, even when used in conjunction with APIs and cloud services. It comes with a collection of powerful Data Science tools and algorithms. These algorithms help analyze customer behaviors on online shopping portals like E-commerce platforms and makes correct decisions based on the results.
SAP:
SAP Predictive Analytics is a mathematical analysis and data analytics solution that allows you to create predictive models to reveal secret insights and interactions in your data, enabling you to forecast future events. Data can be accessed from spreadsheets, on-premise storage, cloud databases, or a combination of the three. Import Data Connections collect data from the networks for data processing and mixing, while Live Data Connections use native communication.
SAP is not only used for predictive analysis; it offers a whole lot more features and tasks. SAP Analytics Cloud is a cloud-based solution that incorporates BI (Business Intelligence), augmented and predictive analytics, and scheduling capabilities. It supports advanced analytics through SAP’s Business Technology Infrastructure as the analytics framework.
Google Trends:
Google Trends is a web-based platform that allows users to see and understand trends in people’s browsing behavior through Google Search, Google News, Google Images, Google Shopping, and YouTube. When it comes to measuring a company’s SEO policy’s success, it’s a powerful tool. It will not only show users the topics and keywords that have been common in searches, but it will also include details about how often specific searches have been done over a predefined period of time, which you can identify in the filters. Google will then visualize the data by creating a trend graph of the searched subject over the specified period of time, quickly analyzing the results.
This method can also be used to analyze the success of your rivals. While it is not the most comprehensive competitor analysis possible, it can give a clear idea of how often the brand, product, or service is searched on Google’s search engines.
These Big Data tools help maintain vast amounts of data and analyze the Big Data more quickly, resulting in improved performance and prediction of consumer behavior throughout every season.
There are a plethora of Big Data tools to choose from. The important thing is choosing the appropriate method for your program’s needs.
Always remember, “When you use the right instrument and use it correctly, you will create something extraordinary; if you use it incorrectly, you will create a mess.”
References:
https://data-flair.training/blogs/hadoop-tutorials-home/
https://support.google.com/analytics/answer/10089681
https://www.intotheminds.com/blog/en/new-behaviours-coronavirus-google-trends/
https://www.oracle.com/ar/a/ocom/docs/executives-guide-to-predictive-data-modeling-wp.pdf
