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4 Ways Predictive Analytics and Machine Learning are Redefining CRO

Although conversion rate optimization (CRO) is nothing new, adopting new technologies to better enhance your CRO practices can seem a little unfamiliar and challenging. However, when it comes to utilizing predictive analytics and machine learning, the benefits to your conversion rates can be significant.

Through analyzing user behavior and entrusting technology to make educated decisions about your website’s and funnel’s performance, it’s possible to provide a massive boost to your funnel. This can be particularly important for businesses as we begin to transition towards the era of the ‘new normal,’ away from the COVID-19 pandemic and the long periods of social isolation that affected business models worldwide.
Survey of Challenges With CRO

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What is AIOps or Artificial Intelligence for IT Operations? Top 10 AIOps Use Cases

What is AIOps

Artificial Intelligence for IT Operations (AIOps) involves using Artificial Intelligence and Machine Learning technologies along with big data, data integration, and automation technologies to help make IT operations smarter and more predictive. AIOps complement manual operations with machine-driven decisions.

Types of AIOps Solutions

At a high level, AIOps solutions are categorized into two areas: domain-centric and domain-agnostic, as defined by Gartner. Domain-centric solutions apply AIOps for a certain domain like network monitoring, log monitoring, application monitoring, or log collection. You will often see monitoring vendors claim AIOps but primarily they are domain-agnostic, bringing the power of AI to the domain they manage. Domain-agnostic solutions operate more broadly and work across domains, monitoring, logging, cloud, infrastructure, etc., and they take data from all domains/tools and learn from this data to more accurately establishing patterns and inferences.

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Predictive Modeling in Data Science

Predictive modeling in data science is used to answer the question "What is going to happen in the future, based on known past behaviors?" Modeling is an essential part of data science, and it is mainly divided into predictive and preventive modeling. Predictive modeling, also known as predictive analytics, is the process of using data and statistical algorithms to predict outcomes with data models. Anything from sports outcomes, television ratings to technological advances, and corporate economies can be predicted using these models.

Top 5 Predictive Models

  1. Classification Model: It is the simplest of all predictive analytics models. It puts data in categories based on its historical data. Classification models are best to answer "yes or no" types of questions.
  2. Clustering Model: This model groups data points into separate groups, based on similar behavior.
  3. Forecast Model: One of the most widely used predictive analytics models. It deals with metric value prediction, and this model can be applied wherever historical numerical data is available.
  4. Outliers Model: This model, as the name suggests, is oriented around exceptional data entries within a dataset. It can identify exceptional figures either by themselves or in concurrence with other numbers and categories.
  5. Time Series Model: This predictive model consists of a series of data points captured, using time as the input limit. It uses the data from previous years to develop a numerical metric and predicts the next three to six weeks of data using that metric.

Which Model Is Right For You?

To find out which predictive model is best for your analysis, you need to do your homework.

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10 Steps to Execute an AI Transformation Project

AI Transformation in Medical Diagnoses

Within every aspect of healthcare, time is considered the most valuable component. Even minutes of delay can result in the loss of life. Early diagnosis lies at the heart of healing patients, and timely execution of treatment is of primary importance. At an average, doctors spend 15 minutes with each patient, which when considered intently, is grossly insufficient in providing a comprehensive diagnosis of the illness. In an ideal situation, a diagnosis should be made after careful consideration of all relevant patient information, including similar cases and demographics.

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As the healthcare industry gradually moves toward an AI-driven world, things that were previously considered a hindrance or unlikely are now fairly simple tasks. Over the years, more than 90% of hospitals in the county have moved from paper-based systems to electronic processes. When it comes to medical diagnoses, patients' records are of primary importance. Risks towards critical illnesses can be caught through predictive analysis, thereby saving lives and costs. Early diagnosis is no longer a distant hope, but an actuality that can be easily accomplished through advanced systems.