MBA means serious business. After all, it can be the key to a successful career and a fulfilling life. With the advent of the online world, a plethora of portals and websites have come up which offer guidance to MBA Aspirants. To understand the pulse of the students and steer their preparation in the right direction, it becomes crucial for study portals to understand what kind of aspirants are visiting them – Serious or Non-Serious. With trusted data analytics tools and techniques, it becomes easy to mine information about the users.
Serious and Non-Serious Aspirant
So, how does a machine decide who is a serious or a non-serious aspirant? In simple words, a serious MBA aspirant will search, click, and spend time on pages that provide him/her information about various entrance exams, preparation strategies, and college information. While a non-serious aspirant might also look in for similar kind of pages, but the activity time and other related searches would be random and probably non-specific to MBA exams and courses.
Role of Data
Thus, data plays an important role. With proper analysis of the data a user shares, and their digital footprints, relevant insights about the type of users and their interests can be extracted. There are multiple ways to collect and analyze the data points from various sources, then break them down into information clusters, and add automated algorithms and streamline the process. With increased volumes of data, the decisions are made faster, reliable, precise, and more transparent.
Data Analytic Tools
There a many online tools like Google Analytics, Crazy Egg, Clicky, Kissmetrics, Optimizely, etc. that are used for data mining, text mining, and getting other useful insights. Here are a few ways of how data analytic tools gather information about students:
- Depth of details shared at sign up
- Website Usage - Search Activity and History
- Social Networks
With this data, portals can perform predictive and prescriptive analytics, which will help them in catering to the needs and interest of the aspirants.
Types of Analytics
Predictive analytics uses artificial intelligence and machine learning to predict the likelihood of a particular event happening. It processes the already existing information or activity of users and finds out what could happen next. Based on the data sourced, targeted information and sponsored links can be shared with them. For example, if a user is observed to search extensively for CAT 2019, targeted emails and ads about CAT preparation links, mocks, and respective colleges can be sent to them.
Prescriptive analytics, a relatively newer concept, uses complex mathematical algorithms, simulation, optimization, and decision-analysis methods to not only make decisions based on the predictions but also understand the impact and effect of it.
Advantages of Using Data Analytics
With such useful and versatile data analytics tools decision-makers can:
- Analyze the current trends and send relevant information
- Differentiate between different types of aspirants
- Direct the interests of students in a specific direction
- Predict percentiles
Recommend valuable options in terms of qualifying colleges and future prospects.
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