Article | January 6, 2021
As the organizations go digital the amount of data generated whether in-house or from outside is humongous. In fact, this data keeps increasing with every tick of the clock.
There is no doubt about the fact that most of this data can be junk, however, at the same time this is also the data set from where an organization can get a whole lot of insight about itself.
It is a given that organizations that don’t use this generated data to build value to their organization are prone to speed up their obsolescence or might be at the edge of losing the competitive edge in the market.
Interestingly it is not just the larger firms that can harness this data and analytics to improve their overall performance while achieving operational excellence. Even the small size private equity firms can also leverage this data to create value and develop competitive edge. Thus private equity firms can achieve a high return on an initial investment that is low.
Private Equity industry is skeptical about using data and analytics citing the reason that it is meant for larger firms or the firms that have deep pockets, which can afford the revamping cost or can replace their technology infrastructure. While there are few private equity investment professionals who may want to use this advanced data and analytics but are not able to do so for the lack of required knowledge.
US Private Equity Firms are trying to understand the importance of advanced data and analytics and are thus seeking professionals with the expertise in dealing with data and advanced analytics. For private equity firms it is imperative to comprehend that data and analytics’ ability is to select the various use cases, which will offer the huge promise for creating value. Top Private Equity firms all over the world can utilize those use cases and create quick wins, which will in turn build momentum for wider transformation of businesses.
Pinpointing the right use cases needs strategic thinking by private equity investment professionals, as they work on filling the relevant gaps or even address vulnerabilities. Private Equity professionals most of the time are also found thinking operationally to recognize where can they find the available data.
Top private equity firms in the US have to realize that the insights which Big data and advanced analytics offer can result in an incredible opportunity for the growth of private equity industry. As Private Equity firms realize the potential and the power of big data and analytics they will understand the invaluableness of the insights offered by big data and analytics.
Private Equity firms can use the analytics insights to study any target organization including its competitive position in the market and plan their next move that may include aggressive bidding for organizations that have shown promise for growth or leaving the organization that is stuffed with loads of underlying issues.
But for all these and also to build careers in private equity it is important to have reputed qualification as well. A qualified private equity investment professional will be able to devise information-backed strategies in no time at all.
In addition, with Big Data and analytics in place, private equity firms can let go of numerous tasks that are done manually and let the technology do the dirty work. There have been various studies that show how big data and analytics can help a private Equity firm.
Article | May 12, 2021
Decision-makers at consumer brands are finally realizing the full transformative potential of external data - but they’re also realizing how difficult it is to source. Forrester reports that 87% of decision-makers in data and analytics have implemented or are planning initiatives to source more external data. And those initiatives are growing outside of the IT team; 29% of those surveyed say that IT has primary ownership of data sourcing, down from 37% in 2016. To support these projects, organizations are increasingly turning to a new specialist: the data hunter, who identifies and vets external data sources. It’s a lot of work to build external data-focused teams, and many leaders are realizing that external data is difficult to scale as the source list grows. Perhaps that’s why 66% of those decision-makers surveyed by Forrester report that they’re using or planning to use external service providers for data, analytics, and insights.
Article | May 31, 2021
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
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.
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.
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.
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 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
• Competitor variables
Using these variables, companies can then take necessary steps to avoid the churn by offering customers personalized services or products.
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
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 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
"name": "What is predictive analytics?",
"text": "Predictive analytics uses historical data to predict future events. The historical data is used to build a 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."
"name": "How to do predictive analytics?",
"text": "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 "
"name": "How does predictive analytics work for business?",
"text": "Predictive analytics helps businesses attract, retain, and grow their profitable customers. It also helps them in improving their operations."
"name": "What tools are used for predictive analytics?",
"text": "Some tools used for predictive analytics are:
SAS Advanced Analytics
IBM SPSS Statistics
SAP Predictive Analytics
Article | March 31, 2020
The analysis of a large volume of data is already an indispensable part of the decision-making process for any business, regardless of its volume. Big data is used to resolve routine problems, such as improving the conversion rate or to achieve customer loyalty for an eCommerce business. But did you know that you can also use it to predict situations before they occur? This is the added value of predictive analytics, the use of big data to anticipate user behaviour based on historical data and act accordingly to optimise sales.For online businesses, periodically performing predictive analytics is synonymous with improving your understanding of the customer and identifying changes in the market before they happen. The predictive models extract patterns from historical and transactional data to identify risks and opportunities. Self-learning software will automatically analyse the data at hand and offer solutions for future problems. This will allow you to design new sales strategies to adapt to changes and boost profit growth.