The power to predict future trends and events is the most influential factor across industries, companies, and even an essential business strategy.Â
Predictive Analytics is used in your day-to-day business operations more often than you might know, from algorithm-enabled medical advancements to weekly weather forecasts, it has brought diversified applications to the table that aren’t leveraged to the max yet.Â
This extension of Google Analytics enables people to ascertain potential events and opportunities to let them either capitalize on or avoid them. It is a string of statistical techniques involving machine learning, data mining, and predictive modeling. It uses current and historical statistics to formulate predictions and estimate future outcomes that benefit the business.
Your business can also employ Predictive Analytics to dive deep into historical data and couple it with current trends to predict future outcomes. This allows companies to adapt to industry changes and remain agile as an organization. Predictive analytics isn’t a new technology, but companies across all industries are now gradually realizing its value in the future prediction game and all aspects of the business.Â
Even though all this sounds great, the true potential of this analytical tool can be best described by illustrating the most advantageous use cases that exist in businesses today. Also, let’s look into those use cases vis-a-vis their most applicable industries.
Different Use Cases for Predictive Analytics
Here are a few different ways business users can use predictive analytics within their organization to avoid missed opportunities, preemptively make themselves more informed, and plan ahead.
In the context of sales forecasting, proficiency to home in on future opportunities, building mistakes, and avoiding errors is crucial.Â
Predictive analytics assesses historical purchasing activity data and links it with popular trends such as buying patterns, customer behavior, and estimated industry-related inflation to forecast sale opportunities for any given amount of time.
This tech can also provide meaningful insights into the types of products and services that are most likely to be in demand and possible changes in the industry or economy to maximize those income opportunities.
The marketing aspect in any organization opens doors to new business relationships and keeps the door open for existing connections.Â
It can be used to predict future trends and help a business create marketing campaigns keeping those predictions in mind.Â
Such campaigns can successfully move customers through the pipeline. Predictive analytics can also be used to understand how your customers interact with your brand and how likely they will do it in the coming years. Not just that, it allows businesses to make interactions with them easily.
Forecasting maintenance issues and avoiding machine breakdown scenarios is critical for a manufacturing entity. It is so because costs arising due to production slowdown are far more burdening than the cost of repair.Â
Using predictive analytics, an enterprise can maneuver real-time data, which can help them accurately predict how much time a machine is most likely to break down. Such efficient use of technology allows you to address the situation before it causes an array of problems or, better yet, avoid them right before it comes about.
Credit Risk and Fraud Prevention
Ascertaining the credit risk and locating fraud is the top priority when running a finance-related business.Â
Predictive analytics can be utilized to identify potential areas of risk from various points in your company’s historical data, allowing you to make well-informed and data-backed decisions.Â
This tech can also be used to spot and prevent fraudulent transactions by monitoring and flagging transactions that are not of the typically expected nature.
Customer Lifetime Value
Customer lifetime value refers to the total potential profits that a particular customer can bring to your organization over the most extended period of time.
Let’s quickly understand this with an example; let’s assume you run an e-commerce site where you sell bicycles online as well as all the additional products. Next, a customer buys a bike from your site. Hereafter, he may buy a basket, new tires, or a helmet from you. Plus, at some point, they may buy a new bike altogether. All these potential sales to this customer would be deemed as his Customer Lifetime Value (CLV).Â Â
This insight from your ML system is quite essential. When you know the total cash flow of a given customer, it becomes relatively easier to understand how far you have gotten with customer retention and maximize ROI (return on investment).
Predictive analytics paired with machine learning can easily manage to do a specific job without additional instructions, relying on patterns in big data sets. This way, it makes your life easier as you don’t have to deal with many numbers and still bring the most excellent lifetime value to your company.Â Â
Predictive analytics can be used in key industries like retail, Banking, Telecommunications, Insurance, and Utilities.
Every company defines its market differently and segments it based on the aspects they value most in its particular industry, product, and services.
A wise business person can make use of predictive analytics to pinpoint target markets based on accurate data and further identify the segments of those markets that are the most responsive to what you offer.
Putting all the relevant information into standard business Key Performance Indicator (KPI) terms, historical data can tell you which customer provides the best Return on Investment. You can quickly figure out that you need to spend on nurturing this particular segment of the customer as opposed to one whose attributes demonstrate a low ROI in an event where a new customer comes on board, and their demographics and behavior don’t match the KPI of high-performing customers.Â
This data can help you potentially classify segments of entire markets that you don’t even realize existed.
Predictive analytics is mainly forecasting the likelihood of an event occurring. Instead of indulging in standard customer segmentation where every 20-year-old is sent the same message on social media, each 20-year-old is dropped a message at the exact time that the modeling suggests they need it.
Key industries that can use this approach are Life Sciences/pharmaceuticals, Insurance, Automotive, Banking, Utilities, Retail, and Telecommunications.
Predictive analytics can help you with product propensity as well. It can combine data regarding people’s purchasing behavior and activities with online behavior metrics from avenues like e-commerce and social media and ascertain correlations of that data to lay down insights. These insights can be regarding the effectiveness of different campaigns.
This mechanism allows companies to ascertain what products or services the customers are likely to buy along with which channels are most likely to reach them. This further helps you maximize channels with the best chance of producing significant revenue.Â The key industries that can utilize this application are retail, banking, and insurance.
Using Predictive Analytics, you can move beyond simple reactive operations into data-informed decision-making and proactive activities that help businesses identify new areas of business and new segments in the market and plan for the future. You can only reap so many benefits from your data, events, and inputs without involving intelligent business operations software.Â Â
Regardless of what trade you are employed in, predictive analytics help you with insights that can project your next moves in the most profiting direction. Whether it is formulating marketing strategies, driving financial decisions, working in hospitals to save lives, or changing your course of action, building a quality analytical system can serve you well.