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AI for Predictive Analytics

Introduction

AI is a broad phrase that refers to a set of technologies that enable robots to do cognitive activities as well as or better than humans, such as seeing, writing, moving, reading, and analysing data.

Even if you aren’t aware of it, AI is utilised everywhere around you. Every voice assistant, like Alexa, is powered by it. It’s the software that analyses emails and filters out spam in every email client. Predictive marketing analytics is a field that has been around for a while but is just now realising its full potential due to AI.

Predictive analytics isn’t limited to a single industry or sector; it could be used across the board, just with a little bit of innovation.

What Is Predictive Analytics and How Does It Work?

Predictive analytics uses machine learning to forecast outcomes based on previously stored data (in some cases, live data). It is used to inform a prediction model in predictive analytics platforms for detecting patterns in large datasets. Machine learning may then utilise what it’s learned to forecast future trends from your data, typically done via regression analysis methods in the predictive model.

A predictive analytics tool has a wide range of applications in business and marketing, many of which are concerned with forecasting future events and behaviour. Everything from predicting customer turnover to forecasting equipment maintenance to detecting possible fraud may be done with it. These predictive skills have the potential to avert significant losses or provide substantial value to a company.

Predictive analytics software often uses big data or massive datasets. Companies are increasingly relying on predictive modelling to figure out what will happen next, particularly in marketing, now that they have access to more extensive vast data than ever before.

Predictive Analytics and Artificial Intelligence

Prediction machines are the most intelligent AI technology. They examine vast amounts of data using algorithms to optimise toward a goal. They learn to enhance their outcomes over time as they optimise.

Marketers now have a lot of data at their hands because of the shift from conventional to digital marketing. We can use Google Analytics, HubSpot, or other analytics and CMS systems to obtain web analytics. We have solid CRM systems in place, as well as customer data platforms. Furthermore, we have a wealth of information from promotional channels such as search engines, advertising, social media, etc.

We’re just human, after all.

Even the data scientists among us have limited time and cognitive ability to generate real-time insights for marketing teams.

However, with AI, we can begin to use accurate predictive analytics throughout our marketing departments. The following is what SAS has to say about the advantages of predictive analytics in marketing:

Predictive analytics is used to predict consumer reactions and purchases, as well as cross-sell possibilities. Businesses may use predictive models to acquire, keep, and expand their most lucrative consumers.

This is just the start.

A predictive model driven by AI may extract enormous value from the data you currently have. AI-powered data analytics can tell you what’s working and what’s not on your website, forecast which will convert into customers, uncover competition insights, and anticipate what your target audiences want to purchase and consume.

We asked hundreds of experts to evaluate the value of intelligently automating more than 60 typical AI use cases using our AI Score for Marketers evaluation tool. After that, we compiled a list of the top 25 applications of AI in marketing. Many of the top use cases included analytics data or insights obtained via analytics:

  • Make content that is based on data.
  • Learn about the most effective content and marketing.
  • Adapt audience targeting based on lookalike analysis and behaviour.
  • Predict the performance of material before it is deployed.
  • Predictive analysis is used to forecast campaign outcomes.
  • Goals should be set based on past data and projected performance.
  • Conversion probabilities are used to score leads.
  • To visualise performance data, create dynamic charts and graphs.

Use Cases for Predictive Analytics

Today, we’re seeing marketers utilise AI-powered predictive analytics to boost revenue, save expenses, and gain a competitive edge in a few key areas.

Discover New Perspectives

Human analysts are capable of bringing insights from analytics systems to the surface. However, they are unable to do it on a regular and large scale. However, AI is particularly good at identifying patterns in big datasets. It can also see patterns that people overlook. Marketers may use this information to gain a competitive advantage.

Other AI-powered services can perform the same thing with your confidential company information. Given the appropriate data, some solutions can answer questions about business issues you’d want to address and do predictive modelling by evaluating your data and predicting how to solve your problem.

Make forecasts

It comes to the reason that AI systems, often known as prediction machines, are capable of making accurate predictions. They do. There are now AI-powered analytics tools that can look at what your rivals are up to on the internet. Everything from product and price adjustments to staff announcements to content strategy is included in the data. These algorithms then forecast which rival actions will have the most significant impact on you and your company. Marketing companies attempting to assist their brands to gain market share will need this kind of competition information.

AI-powered technologies may also provide you with these kinds of in-depth information about your target consumers.

Businesses are using powerful AI to analyse data on internet audience interests, demographics, and psychographics. They’re also looking at how people behave online and on social media. What’s the result? Predictions about what your target market will desire to purchase, see, and consume.

Organise Your Information

Artificial intelligence-powered analytics solutions can assist you in closing the loop on all of your reports from both first- and third-party sources.

Some AI solutions, for example, may combine data from several first-party sources into a single unified customer view across channels, allowing you to keep everything in one place. These technologies then use machine learning to evaluate a person’s probability of becoming a client and create more complex lead segmentation using this unified data.

Call monitoring and analytics are also using AI to link call centre sales and marketing operations. Everything from closing the loop on attribution across channels to dynamically routing calls between representatives and teams can be done using AI.

Insurance

In the insurance industry, making accurate forecasts about future occurrences has always been critical. In the policy-making procedure, accurate predictions and risk assessments are crucial. Artificial intelligence in the insurance industry has resulted in significant developments, such as automating error-prone human procedures. There are now technologies that can cross-reference many data sources to guide pricing choices and identify false allegations.

Healthcare

Google was one of the first firms to use predictive analytics in the healthcare industry. Google Flu Trends (GFT) used anonymised, aggregated internet search behaviour to forecast flu trends by providing real-time estimates of influenza activity for a given area. The GFT, although helpful, produced exaggerated figures, resulting in less-than-ideal data.

Predictive Maintenance Using AI

With the emergence of Industrial Artificial Intelligence and the Internet of Things, business entities have reformed their analytics structure. Since Maintenance is the key functional area of every organization, therefore, AI-driven predictive maintenance analytics is put to use for savings operational cost and enhancing product value. Over the years organizations have improved their effectiveness in the quest for optimum operational efficiency. AI and Machine Learning are potent to collect and analyze massive amounts of sensor data and provide deep insight into the future possibilities with great accuracy.

Role in Total Productive Maintenance

The holistic maintenance and improvement system of TPM focuses on critical assets and related processes. The major aim of TPM is to result in fewer breakdowns and increased production with immense safety. Thus, the overall TPM gets its biggest boost from Artificial Intelligence with leads to the effective execution of predictive maintenance analytics and creates a healthy difference in the organization.

Adopting and Implementing Autonomous Maintenance

AI allows effective businesses to adopt overall machine reliability and challenge the irregularities in the manufacturing process. The active involvement of AI tools in predictive maintenance has shaped the entire ecosystem into a competitive place where with common prediction different sets of strategies are framed. As a result, an AI and predictive analytics is the most commonly observed combination and paves the way towards upscaling maintenance process in the all set of organizations.

Companies Using AI for Predictive Analytics

Pecan

Pecan, an AI platform adds value to the predictive analytics concept for automating data processing, preparation, engineering, and selection. Moreover, it helps to convert raw and scattered data into actionable insights in the shortest duration. The Pecan platform works on some fundamental aspects to leverage maintenance benefits to the organization. Defining the business question and objectives to stay centered to the effective organizational performance. Moreover, it focuses on model building which further automates the entire business process and prioritizes increasing the visibility of the essential factors to predict future outcomes. Hence, Pecan facilitates action and connects them with the prediction derived from data exports and API.

Salesforce Einstein

It is the first comprehensive AI platform for Customer Relationship Management ( CRM) and paves the way towards a smarter business ecosystem. The Salesforce Einstein enables companies to predict sales, market opportunities, customer needs, and helps to create personalized strategies for both employees and the customers. Thus, it is an integrated approach where business data is put into the sales force and Einstein uses these data to leverage benefit to the associated industries. The Einstein can be accessed through Service Cloud, Marketing Cloud, Analytics Cloud, Community Cloud, Sales Cloud. Thus, it is the robust and flexible platform that provides elaborated security architecture and serves more than 150,000 companies across the world with its data–ready, production-ready, and modeling–ready framework.

Presenso

The company provides AI-driven solutions for real-time asset maintenance and predicting asset failure using sensor signals. The adaptive algorithm of the Presenso helps to analyze and understand the machine behavior which makes it easy to identify loopholes in the asset. Recently, Presenso’s industrial tool is integrated into Siemens’ remote diagnostic service tools to generate smart data using field sensors. These data science innovation and collaboration has deployed healthy solutions in the various industries across the world and aims to improve the performance of every organizational sector by leap and bounds.

AI for Predictive Analytics Examples

AI predictive analytics is used across the different industrial frameworks and helps to strengthen the manufacturing and maintenance ecosystem at large.

  • The retail sector like Amazon uses predictive analytics for improving sale positions and fostering healthy relations with customers using its recommendations feature. Moreover, it helps the brand to analyze behavioral patterns for all similar items in the list.
  • In the health sector, AI-driven predictive analytics helps to monitor million of patients and track their behavior online. The predictive algorithms help to predict epidemics and public health issues for providing intensive care and mitigating the potential health risks.
  • AI Predictive Analytics helps to forecast weather conditions more accurately and prepare people for atmospheric changes. As a result, AI-driven culture has brought a big boost to mitigate natural catastrophe.
  • Predictive analytics helps to assess risk factors and frame financial models considering user preferences. It also provides deep insight into the competitive prices of the industry to introduce relatable financial and insurance products.
  • Energy and power plants use AI predictive analytics for reducing maintenance costs and ensuring the proper availability of power. The smart meters based on the AI concept are built to send alerts to the customers to plan their work accordingly in case of irregular resource supply.

Machine Learning (ML) and Predictive Analytics

The major use case of Machine Learning is to build predictive models based on the thorough pattern study which includes price prediction, risk assessment, document classification, and analyzing customer behavior. This diversified consideration helps businesses to transact raw data into useful information and generate fruitful learning with the least error. Moreover, machine learning supports each phase of the organizational process from development to service delivery and helps to formulate a business plan. Machine learning is the prominent sub–set of Artificial Intelligence that works in close alignment with predictive analytics and helps to expand its use cases to almost all industrial types. Thus, the amalgamation of Machine Learning and Predictive Analytics is bringing better fortune to the industries with higher conversion rates which in turn fetches huge turnover.

Topics in Predictive Analytics

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