5 Predictive Data Analytics Applications

SHAIVI CHAPALGAONKAR | May 31, 2021 | 124 views

According to Google trends, predictive data analytics has gained a significant amount of popularity over the last few years. Many businesses have implemented predictive analytics applications to increase their business reach, gain new customers, forecast sales, and more.

Predictive Analytics is a type of data analytics technology that makes predictions with the help of data sets, statistical modeling, and machine learning. Predictive analytics uses historical data. This historical data is fed into a mathematical model that recognizes patterns and trends that are then applied to current data to forecast trends, practices, and behaviors from milliseconds to days and even years.

Based on the parameters supplied to them, organizations find patterns within that data to detect risks, opportunities, forecast conditions, and events that would occur at a particular time. At its heart, the use of predictive analytics answers a simple question, “What would happen based on my current data and what can be done to change the outcome.”

In the current times, businesses have multiple products offerings at their disposal to choose from vendors of big data predictive analytics in different industries. They can help these businesses leverage historical data discovering complex data correlation, recognizing patterns, and forecasting.

Organizations are turning to predictive analytics to increase their bottom line and gain advantages against their competition. Some of those reasons are listed below:

• With the growing amount and types of data, there is more interest in utilizing it to produce valuable insights
• Better computers
• An abundance of easy to use software
• Need of competitive differentiation due to tougher
  economic conditions

As more and more easy-to-use software have been introduced, businesses no longer need statisticians and mathematicians for predictive analytics and forecasting.

Benefits of Predictive Analytics

Competitive edge over other businesses

The most common reason why multiple companies picked up predictive analytics was to gain an advantage over their competitors. Customer trends and buying patterns keep changing from time to time. The ones who can identify it first will go ahead in the game. Embracing predictive analytics is how you will stay ahead of your competition. Predictive analytics will aid in qualified lead generation and give you an insight into the present and potential customers.

Business growth

Businesses opt for predictive analytics to predict customer behavior, preferences, and responses. Using this information, they attract their target audience and entice them into becoming loyal customers. Predictive analytics gives valuable information about your customers such as which of them are likely to lapse, how to retain them, whether you should market directly at them, etc. The more you know about them, the stronger your marketing will become. Your business will become the leader in predicting your customer’s exact needs.

Customer satisfaction

Retaining existing customers is almost five times more difficult than acquiring new ones. The most successful company is the one that invests money in retaining those customers as much as acquiring new ones.

Predictive analytics helps in directing marketing strategies towards your existing customers and get them to return frequently. The analytics tool will make sure your marketing strategy caters to the diverse requirements of your customers.

Personalized services

Earlier marketing strategies revolved around the ‘one size fits all’ approach, but gone are those days. If you want to retain and acquire new customers, you have to create personalized marketing campaigns to attract customers.

Predictive analytics and data management help you to get new information about customer expectations, previous purchases, buying behaviors, and patterns. Using this data, you can create these personalized marketing strategies that will help keep up the engagement and acquire new customers.

Application of Predictive Analytics

Customer targeting

Customer targeting divides the customer base into different demographic groups according to age, gender, interests, buying, and spending habits. It helps companies to create tailored marketing communications specifically to the customers who are likely to buy their products. Traditional techniques do not even come close to identifying potential customers as well as predictive analytics does.

The major constituents that create these customer groups are:

• Socio-demographic factors: age, gender, education, and marital status
• Engagement factors: recent interaction, frequency, spending habits, etc.
• Past campaign response: contact response, type, day, month, etc.

The customer-specific targeting for the company is highly advantageous. They can:

• Better communicate with the customers
• Save money on marketing
• Increase profits


Customer churn prevention

Customer churn prevention creates major hurdles in a company’s growth. Although it has been proven that retaining customers is cheaper than gaining new ones, it can become a problem. Detecting a client’s dissatisfaction is not an easy task as they can abruptly stop using your services without any warning.
Here, churn prevention comes into the picture. Churn prevention aims to predict who will end their relationship with the company, when, and why. The existing data sets can help develop predictive models so companies can be proactive to prevent the fallout.

Factors that can influence the churn are as follows:

• Customer variables
• Service use
• Engagement
• Technicalities
• Competitor variables

Using these variables, companies can then take necessary steps to avoid the churn by offering customers personalized services or products.

Risk management

Risk assessment and management processes in many companies are antiquated. Even though customer information is abundantly available for evaluation, it is still antiquated.

With advanced analytics, this data can be quickly and accurately analyzed while maintaining customer privacy and boundaries. Risk assessment thus allows companies to analyze problems with any business. Predictive analytics can approximate with certainty which operations are profitable and which are not.

Risk assessment analyzes the following data types:

• Socio-demographic factors
• Product details
• Customer behavior
• Risk metrics


Forecast sales

Evaluating the previous history, seasonality, and market-affecting events make revenue predicting vital for a company’s planning and result in a company’s demand for a product or a service. This can be applied to short-term, medium-term, and long-term forecasting.

Predictive models help in anticipating a customer’s reaction to the factors that affect sales.

Following factors can be used in sales forecasting:

• Calendar data
• Weather data
• Company data
• Social data
• Demand data

Sales forecasting allows revenue prediction and optimal resource allocation.


Healthcare

Healthcare organizations have begun to use predictive analytics as this technology is helping them save money. They are using predictive analytics in several different ways. With the help of this technology, based on past trends they can now allocate facility resources, optimize staff schedules, identify patients at risk, adding intelligence to pharmaceutical and supply acquisition management.

Using predictive analytics in the health domain has also helped in preventing cases and risks of developing health complications like diabetes, asthma, and other life-threatening problems. The application of predictive analytics in health care can lead to making better clinical decisions for patients.

Predictive analytics is being used across different industries and is good way to advance your company’s growth and forecast future events to act accordingly. It has gained support from many different organizations at a global scale and will continue to grow rapidly.


Frequently Asked Questions

What is predictive analytics?

Predictive analytics uses historical data to predict future events. The historical data is used to build mathematical model that captures essential trends. That predictive model is based on current data that predicts what will happen next or suggest steps to take for optimal outcomes.


How to do predictive analytics?

• Define business objectives
• Collect relevant data available from resources
• Improve on collected data by data cleaning methods
• Choose a model or build your own to test data
• Evaluate and validate the predictive model to ensure


How does predictive analytics work for business?

Predictive analytics helps businesses attract, retain, and grow their profitable customers. It also helps them in improving their operations.


What tools are used for predictive analytics?

Some tools used for predictive analytics are:
• SAS Advanced Analytics
• Oracle DataScience
• IBM SPSS Statistics
• SAP Predictive Analytics
• Q Research

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ACA Group Acquires Data Specialist Ethos ESG to Offer First Data Analytics Product

ACA Group | September 26, 2022

ACA Group (ACA), the leading governance, risk, and compliance (GRC) advisor in financial services, today announced that it has acquired Ethos ESG (Ethos), a provider of environmental, social, and governance (ESG) ratings data and software for financial advisors, asset managers, institutions, and investors. This acquisition marks ACA’s first analytics offering, which will be paired with ACA’s ESG experts to form an integrated tech and advisory offering under the ESG Advisory practice. ACA’s existing ESG Advisory practice supports with a range of programmatic needs for firms that integrate ESG into their business or investment activities. This currently includes advice and implementation support around strategy, policies/procedures, regulations and frameworks, training, and external reporting, among other areas. With Ethos, ACA’s clients will now also be able to easily analyze investments and automate several elements of ESG reporting. Founded in 2019, Ethos offers an interactive platform that allows for the evaluation of over 350,000 impact ratings including companies, stocks, and funds through a prism of 45 ESG causes such as climate change, racial justice, mental health and more. Providing full transparency into how each impact score is calculated and the ability to upload portfolios and create models, Ethos allows for GRC professionals to understand the ESG characteristics of their investments and make responsible decisions that align with their firm's values and ESG commitments. Ethos uses a proprietary set of approximately 100 underlying databases to generate its ratings. These databases provide a unique impact view of ratings, as well as provide insight into key metrics where available. The databases are fully transparent, so clients can see which underlying database source for each data point. Ethos also has capabilities developed to quickly scrape the public domain for material publicly available information to include in the ratings. These state-of-the-art capabilities allow Ethos to quickly add company coverage to help clients achieve full coverage of their investment portfolio. Ethos has invested in innovation through the recent launch of its Impact Calculator, an embeddable widget that takes a dollar amount and immediately calculates the real-world equivalent impact of investing that amount in a specific fund or other product, compared to a benchmark. Additionally, Ethos recently introduced its Carbon Neutral Certification program for mutual funds and ETFs, developed in conjunction with Change Finance. Through the certification, Ethos performs an independent analysis of a funds carbon footprint (covering Scope 1 and Scope 2 emission) and carbon credits (offsets) to verify whether the fund is carbon neutral during a specified period. “This is an exciting step in helping to grow our presence in the ESG space and is ACA Group’s first foray into analytics as a service,” said Shvetank Shah, CEO of ACA Group. “We are invigorated to be building out and launching our data capabilities, starting with Ethos ESG. Combining data with our scalable solutions will continue to empower our clients to reimagine GRC and protect and grow their business.” “We are thrilled to partner with ACA Group, as their brand and reach in the GRC space is well-known. “Not only is taking into consideration the ESG impact of your decisions right on its merits, but greater transparency into ESG issues helps firms mitigate risk and make informed choices while growing sustainably.” Luke Wilcox, Founder and CEO of Ethos ESG “This pairing will help us to leverage data in a new way to help firms of all sizes develop and monitor their ESG programs to mitigate risk, make informed choices, combat greenwashing, and grow profitably and sustainably in the process. Access to high-quality, transparent ESG data is an essential part of any ESG endeavor, and our partnership with Ethos will allow us to build and protect our clients’ ESG strategies in ways few others can,” said Dan Mistler, Head of ESG Advisory at ACA Group. About ACA Group ACA Group (ACA) is the leading governance, risk, and compliance (GRC) advisor in financial services. We empower our clients to reimagine GRC and protect and grow their business. Our innovative approach integrates advisory, managed services, distribution solutions, and analytics with our ComplianceAlpha® technology platform with the specialized expertise of former regulators and practitioners and our deep understanding of the global regulatory landscape. About Ethos ESG Founded in 2019, Ethos ESG provides data and analytics for financial advisors, asset managers, institutions, and investors. With over 350,000 impact ratings of stocks and funds across 45 causes, Ethos ESG helps firms offer robust impact reporting, monitor and address sustainability risks, and enhance quantitative research and modelling with transparent ESG data.

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BUSINESS INTELLIGENCE,BIG DATA MANAGEMENT,DATA SCIENCE

Stardog Joins Databricks Partner Connect

Stardog | September 26, 2022

Stardog, the leading Enterprise Knowledge Graph platform provider, today announced it had joined Databricks Partner Connect, which lets Databricks customers integrate with select partners directly from within their Databricks workspace. Stardog is the first Databricks partner to deliver a knowledge-graph-powered semantic layer. Now with just a few clicks, data analysts, data engineers, and data scientists can model, explore, access, and infer new insights for analytics, AI, and data fabric needs — a seamless end-to-end user experience without the burden of moving or copying data. Together, Stardog's availability on Databricks Partner Connect enables joint customers to: Easily define and reuse relevant business concepts and relationships as a semantic data model meaningful to multiple use cases. Link and query data in and outside of the Databricks Lakehouse Platform to provide just-in-time cross-domain analytics for richer insights. Ask and answer questions across a diverse set of connected data domains to fuel new business insights without the need for specialized skills. "Data-driven enterprises are increasingly looking to build more context around their data and deliver a flexible semantic layer on top of their Databricks Lakehouse. "Stardog's Enterprise Knowledge Graph offers a rich semantic layer that complements and enriches a customer's lakehouse and we are excited to partner with them to bring these capabilities to Databricks Partner Connect." Roger Murff, VP of Technology Partners at Databricks A commissioned Forrester Consulting Total Economic Impact™ study concluded that a composite organization using Stardog's Enterprise Knowledge Graph platform realized a 320 percent return on investment over three years driven by $3.8 million in improved productivity of data scientists and engineers from faster analytics development, $2.6 million in infrastructure savings from avoided copying and moving data, and $2.4 million in incremental profits from enhanced quantity, quality, and speed of insights. "Our mission at Stardog is to help companies unite their data to unleash insight faster than ever before," said Kendall Clark, Founder and CEO at Stardog. "Databricks Partner Connect enables Stardog to deliver a seamless experience for Databricks customers to quickly add a semantic layer to their lakehouse, unlock insights in their data, and discover more value-impacting analytics use cases." About Stardog Stardog is the ultimate semantic data layer to get better insight faster. Organizations like Boehringer Ingelheim, Schneider Electric, and NASA rely on the Stardog Enterprise Knowledge Graph to accelerate insights from data lakes, data warehouses, or any enterprise data source.

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