Every business owner hopes to look into the future and figure out the best ways to invest capital and resources while working towards their company’s long-term success. This has led to the rise of advanced techniques like predictive analytics in the industry.
Predictive analytics can be applied to the huge amount of data businesses collect each day in order to make valuable predictions about their current or future actions.
It uses sophisticated analytics to assess a project’s success. It further assists you in making decisions by providing intelligent recommendations for potential future actions, all of which are based on your data.
In this article, we will discuss the common myths surrounding predictive analytics, but before we dive into that, let’s see what is predictive analytics and what are its benefits to your business.
What is predictive analytics?
Predictive analytics is the use of historical data, machine learning techniques, and statistical algorithms to identify the likelihood of future events. This could help anticipate customer needs, forecast wider market trends, or manage risks, which offers a competitive advantage and ultimately increases revenue.
As per a Research and Markets’ report, the global predictive analytics market size is said to grow to USD 21.5 billion by 2025 from USD 7.2 billion in 2020, at a CAGR of 24.5%.
Various factors such as increasing use of machine learning and artificial intelligence, acquisitions, and product launches in this market are expected to drive the adoption of predictive analytics software and services.
Now let’s take a look at an example to see how predictive analytics works.
Businesses are faced with a number of decisions every day, and their choices greatly impact the company’s success. So, let’s start with a typical decision faced by most businesses – forecasting sales.
For financial planning, a sales forecast is the first important step. It impacts almost every aspect of your business, such as hiring employees, buying raw materials to manufacture products, handling demand, and more.
Forecasting sales not only involves predicting how much product you’ll sell in an upcoming period but also involves forecasting who will buy, why they will buying, etc. But all these tasks take a significant amount of time, making it hard for you to prioritize resources. But that’s not the case if you use a predictive analytics tool.
A predictive analytics tool can be used for accurate sales forecasts. It predicts future behavior based on past behaviors and combines historical and current data to help you understand what your customers need and why they need it. This, in turn, leads to better user experiences.
Now that we have seen what predictive analytics is, let’s get into the key benefits of predictive analytics for your business.
Why is predictive analytics important?
Predictive analytics helps organizations to detect risks and uncover opportunities by generating actionable insights that can be used to achieve their business goals. So, here are a few key benefits of predictive analytics for your business:
1. Detecting fraud: Combining multiple analytics methods can identify suspicious behaviors and improve pattern detection. As cybersecurity becomes a growing concern, high-performance predictive analytics examines all actions on a network in real-time to spot abnormalities that may indicate fraud, zero-day vulnerabilities, and advanced persistent threats.
2. Improving operations: Many organizations use predictive models to forecast inventory and manage resources. For instance, hotels try to predict the number of guests for any night to maximize occupancy and increase revenue. This helps proactively improve their production processes and take appropriate actions when needed.
3. Forecasting customer churn: Making a churn forecast entails detecting the signals that precede your customers’ cancellation requests and evaluating the likelihood in each case.
You can make use of predictive models to compare data like customers’ satisfaction, service quality, and churn rate to identify which aspects lead to cancellation.
The main idea is to figure out what’s causing the customer’s loss and then reverse the process.
4. Optimizing market campaigns: Using predictive analytics, you can see the whole history of marketing campaigns to forecast better future results.
You can determine the most effective language for each target demographic, channels used for marketing campaigns, and other factors influencing consumer acceptability. As a result, when interacting and winning over your audience, you shoot squarely at the objective.
5. Customer relationship management (CRM): Predictive models can be used for customer relationship management to understand clients at every stage of their purchasing journey.
With the help of available data, you can build multivariate models and assess the widest range of possible links between purchase histories, behaviors, profiles, interactions, and consumer perceptions.
You can build long-term relationships with customers using customized content, promotions, and offers if you have this key information.
Now that we have looked into the importance of predictive analytics, let’s see a few of its applications in different industries.
Predictive analytics use cases
Industries can use predictive analytics to optimize operations, reduce risks, and increase revenue. Here are a few applications of predictive analytics in real-life scenarios.
Healthcare: Predictive analytics is being incorporated in the healthcare industry for three crucial reasons – risk estimation, geo-mapping, and planning out the what-if scenarios in terms of surgery and patient inflow in the hospital.
Using analytics leads to better patient outcomes, more effective treatments, and cost savings across multiple departments.
For example, a device for asthma patients that uses predictive analytics can record and analyze the breathing sounds of patients and provide real-time feedback using a smartphone app to help patients better manage their symptoms and be prepared for an attack.
E-commerce: Predictive analytics in business helps sellers understand the highest price a customer is ready to pay for their products, analyze the buyer’s behavior, and create personalized product recommendations.
For instance, e-commerce giants like Amazon and eBay are the perfect examples of companies using predictive analytics for their businesses. They use it to monitor customer behavior and current market trends to predict changes and make real-time and data-driven decisions.
On-demand services: In the on-demand economy specific to transport and communication, predictive analytics come in handy in terms of estimating the areas that are going to ask for maximum fleet demand, the price that users are most likely to pay for a tip, the stage at which they are canceling the ride, etc.
Virtual assistants: Combined with the power of deep learning, predictive analytics works wonders when used for virtual assistants. Siri, Ok Google, and Alexa are real-world examples of utilizing the innovative technique in virtual assistants. These assistants learn from the user’s behavior and then deliver accurate results.
Supply chain management: A poorly optimized supply chain can have a deterrent effect on every area of your business. Thus, it becomes vital for enterprises to use advanced technologies like predictive analytics.
The information you gather using predictive analytics will be as up-to-date as possible as it can incorporate real-time data. You can also be more agile in your decision-making process since the model will indicate the impacts of different variables on your supply chain’s efficiency.
Banking and financial services: The financial industry has long embraced predictive analytics for fraud prevention and detection, measuring credit risk, maximizing cross-sell/up-sel
