Predictive analytics uses data, statistical algorithms and machine learning techniques to identify the likelihood of future outcomes based on important historical data.
The goal of predictive analytics is to go beyond what has happened in the past but instead utilizing the data points to predict what will happen in the future.
Some people often are confused on what’s the difference between predictive analytics and prescriptive analytics?
Key Takeaway and the quickest would be: Predictive analytics uses collected data to predict future outcomes; while prescriptive analytics takes that data and goes even deeper into the potential results of certain actions.
In the highly competitive landscape in 2021, predictive analytics helps businesses gain a competitive advantage. Businesses can make evidence based decisions based on their data.
This data could comprise:
To gain that competitive advantage, predictive analytics help businesses solve problems, develop innovative products and solutions, and augment value to the end client.
It also assists businesses make sense of their data to predict future outcomes and meet business goals, such as: saving time, lowering costs, reducing waste, increasing the bottom line and gain a competitive advantage.
Predictive analytics utilizes several data analysis techniques including machine learning, data mining and statistics. As machine learning comprises the core of predictive analytics, we’ll focus on how we can use specific prediction based approaches within the machine learning field to get better insight to future trends and events.
One of the most common predictive analytics models are classification models. These models operate by categorizing information based on historical data sets. Classification models are used in different industries because they can be easily retained with new data and can provide a broad analysis for answering important questions.
Classification models facilitate organizations more efficiently allocate resources, human or otherwise. For example, companies become better able to keep inventory at appropriate levels and support in preventing overstaffing of a retail store at a certain hour.
Decision Trees tend to be the method of choice for predictive modelling because they are relatively easier to comprehend and are also very effective. The basic goal of a decision tree is to split a population of data into smaller segments.
While decision trees solve many kinds of classification problems, they can answer much more complex questions when employed in predictive analytics.
Businesses use predictive models to forecast inventory, managing resources more efficiently and operating more effectively. This could be the use of customer data to offer specific services and products at certain times and prices.
Common examples include:
If you want to attract and retain customers for your business, then you need to go the predictive analytics route by predicting their purchases and responses.
If you’re still unsure of whether your marketing efforts need to rope in predictive analytics; we have some important facts and figures to convince you to take the plunge.
This of it like this: Utilizing available data for planning, designing and deploying a marketing campaign is like having a superhero cape that will almost guarantee better visibility and better return on ad spend.
Some common examples that we’ve come across predictive analytics helping businesses come up with better marketing campaigns include:
Which types of content work better for certain leads can be answered with predictive analytics. Once you are clear on which type of content resonates with a specific audience; you can easily customize content creation and distribution. When leads receive higher quality communication from an organization, that will help you in managing better conversions.
Customer lifetime value (CLV) is how much a customer is worth to you throughout the entire span of relationship between the customer and the corporation. With predictive analytics, you can utilize past data to understand how much a new lead or conversion will help in bringing revenue to a business. These estimates can help you to set budgets for customer acquisition; hence, giving you a more accurate picture of predictive Return on Investment (ROI).
Businesses can better predict demand using predictive analytics and business intelligence. For example, consider a hotel chain which needs to understand how many visitors will flock to their hotel during a certain time of the year. If they utilize predictive analytics to understand this fact they would be in a position to aptly manage and arrange for staff that would look after its clients in a more organized and well-rounded manner.
Predictive Analytics helps companies target and retain customers by cultivating a proactive and personalized customer service experience.
Here are four of the ways that predictive analytics is making customer service more efficient:
As more and more companies start to understand the possibilities of machine learning and automation.
There is a need to rationalize the time and resource needed to prioritize predictive modelling, and create a pros and cons matrix to weigh against possible potential benefits.
There are four major prerequisites of an effective predictive modelling. They are:
While getting started with predictive analytics isn’t exactly a quick turnaround event, it’s a task that virtually any business can handle as long one remains committed to the approach and is willing to invest the right amount of time and funds necessary to get the project moving in the correct direction.
Beginning with a limited pilot project is a critical business area is the best way to cap startup costs while minimizing the time before financial rewards start to pump in.
For a deeper look, we would recommend the following resource for understanding how to get started with predictive analytics.