Predictive analytics is a branch of data analytics, which uses statistical or machine learning modeling to predict the most likely future outcomes and behaviors, based on patterns in current and historical data. In essence, predictive analytics is used to answer the question “What is likely to happen?”.
The primary use of predictive analytics is for business solutions, where forecasts allow data-driven decision-making, improve operational efficiency, and identify risks.
Predictive analytics is set to boom for the remainder of the decade, with the market already valued at $20.5 Billion in 2022, and expected to increase at a CAGR of 20.4% and reach $30 Billion by 2028.
When discussing predictive analytics, it’s important to differentiate it from other types of data analytics, which progress in terms of complexity and logical sequencing.
Descriptive analytics is the foundation of all data analytics, and you’ve probably done it in some way before. When you identify trends in data to describe “what happened”, you’re using descriptive analytics. It’s most commonly communicated through dashboards, reports, and visualizations.
Diagnostic analytics is the next progression and is used to understand the cause of the event, or in other words, answer “why it happened”. If you’ve ever identified trends, patterns, or correlations between variables in data to find causal relationships, you’ve performed diagnostic analytics.
Predictive analytics is the third type and marks the first category that ventures from the past into the future. The trends and patterns discovered in past data are now used in conjunction with predictive modeling to answer “what will happen”, which is an estimate. It’s a jump in complexity from the previous two, with manual analysis proving largely ineffective, usually requiring machine learning methods to perform.
Prescriptive analytics is the final frontier of data analytics, which aims to enable data-driven decision-making by answering “What do we do next”. Like predictive analytics, it uses machine learning to maximize future results by testing variables and suggesting the optimal course of action.
Before you undertake your predictive analytics project, you need to know which kind of machine learning models can solve what kinds of problems.
The first problem type predictive analytics can be used for is classification. This involves predicting the category, or class, that an unknown data point belongs to, based on its other features. It is supervised training, meaning that it must be trained on a labeled dataset where the class is known, and uses that to predict unknown testing data. Some classification algorithms include; Naive Bayes, K-nearest neighbours, decision trees, and support vector machine.
The second problem type of predictive analytics models is regression, also known as forecasting, models. Like classification, it must be trained on a labeled dataset where the target variable is known, however, instead of a class, it’s labeled with a number. Some regression algorithms include; Linear regression, multiple linear regression, and polynomial regression.
The third kind of problem predictive analytics can handle is with clustering models. Unlike the previous two, clustering is unsupervised, meaning it trains on unlabelled data. Clustering discovers hidden natural patterns within the data, and clusters datapoints it views as being similar.
The process of delivering a predictive analytics project within a business doesn’t start with building models there's a large amount of preparation to do before any data science happens.
You’re probably going to be using predictive analytics to solve a specific business problem. So it makes sense to start by consulting with the subject matter expert to fully understand the business problem. This step also involves defining the scope, technical requirements, and goal of the project.
For better or worse, the success of your predictive analytics project will probably come down to the quality of the data that you use. The collection and preparation of data is vital, and unfortunately for you, is usually one of the most complex and time-consuming parts to get right. To improve your chances, be sure to get your hands on high-quality data, judged based on its relevancy, suitability, and cleanliness.
Now that you’ve got your data, it’s tempting to want to get straight to model building. But hold off for a moment, because there’s an important step in between. Exploratory data analysis (EDA) will help you understand distributions, patterns, and correlations in data, and help you prepare the data for machine learning. Data cleaning, otherwise known as data wrangling, is transforming the raw data into processed data by merging data sources and resolving issues such as missing values, outliers, and improperly formatted data.
It’s time to start building the predictive models, which will depend on the kind of problem you have. As mentioned previously, different problems require different machine learning models, for example predicting a numerical variable will require a regression model, whereas predicting the category will require a classification model. For each type of model, there will be different algorithms that have different processes to reach its prediction. Using different algorithms will result in different levels of performance, speed, and other factors.
When machine learning models are trained, different methods can be used to validate the model performance. Whether it's the holdout or k-fold cross-validations, you’ll have evaluation metrics to help you decide if the model is appropriate to use for your new data. Model building and evaluation is an iterative process, so don’t be afraid of trial and error, and going back to fine-tune and repeat.
The final step is to deploy the model and use it in the real world. The way the results are used depends on how you set the project up, it may be used once, for example in a report to make a key decision. Or continuously, for example in a software application. If the latter, it’s a good idea to monitor the model, and circumstances will change with new data coming in.
The benefits of predictive analytics largely depend on your business, and what you’re using it for. If you’re using it to predict churn, the benefit will be an increased lifetime value of customers, if it’s for fraud detection the benefit will be savings on costs. In saying that, there are a few categories of advantages that predictive analytics brings, regardless of the use case.
Data-driven decision-making: Patterns and trends previously hidden allow companies greater confidence in strategic decision-making, knowing it’s based on concrete data that is unique to them, and not intuition or collective wisdom.
Operational process enhancement: Predictive analytics provides evidence to change the way an organization operations to optimize processes. This can drive revenue with a higher volume of sales, a higher rate at which the business can convert opportunities, or increase the lifetime value of existing customers.
Increased operational efficiency: Predictive analytics requires an investment at the beginning to introduce, however over the long run can lead to efficiency saving by reducing the time and people required for tasks, leading to savings in labour costs.
Improve risk management: With most businesses, there is some kind of potential risk associated with their operation. Being able to accurately forecast this, means businesses can adjust this to match their unique appetite.
Personalized services: Predicting customer behavior using predictive analytics means a business can improve customer satisfaction by providing more personalized services and communication at scale.
Hopefully, by now, you’ve learned what predictive is, why you might want to use it, and how you might go about doing it. But I’ll give you a head start and give you some examples of the most common uses across different industries.
With more data about customer behavior and preferences available, predictive analytics provides marketers with an effective way to better serve them, deliver more impact, and directly impact revenue. PA is already widely adopted in marketing with 84% of marketing leaders using it.
Customer segmentation: Segmenting customers based on data increases marketing effectiveness by personalizing what and how you market.
Customer churn prediction: With customer loyalty in decline across all industries, predicting customers at risk of churning gives marketers the ability to retain them before it happens.
Lead scoring: Using predictive analytics to determine how valuable a lead is, based on whether they match the characteristics of existing customers.
Many predictive analytics use cases that center around predicting customer behavior can be applied to a company’s employees, using internal employee and recruiting data. HR professionals can utilize predictive analytics to attain and retain top talent.
Candidate scoring: Like lead scoring in marketing, companies quickly filter candidates for a position based on the attributes that current high performers show
Employee turnover prediction: Keeping talent is vital for any business, and predicting employees who are likely to turnover in the future can help HR intervene and retain valuable employees.
With predicting customer behavior being such a pervasive use for predictive analytics, it comes as no surprise that retailers are noticing the competitive edge that it can provide.
Price optimization: Price has always mattered in retail, and more informed consumers make it even more important. Price optimization hits the sweet spot where shoppers are happy, and retailer's margins remain high.
Supply and demand forecasting: Inventory has been tricky for retailers to get right, too much and margins are reduced, and too little reduces the amount of sales. Predictive analytics can help forecast consumer demand and supply, to keep inventory at optimal levels.
Banking and financial services have access to high-quality data, and using that data to power predictive analytics offers a solution to banks, helping them save on costs by identifying risks ahead of time.
Loan delinquency prediction: Banks can filter out applicants who have a high chance of defaulting on their loans.
Fraud detection: Predicting which transactions are likely to be fraudulent allows banks to save on costs by reducing the quantity that occurs.
Customer churn prediction: It’s never been easier for customers to switch banks, predicting at-risk customers and intervening is paramount to retain valuable customers.
The insurance industry has always relied on data, and the quality of that data has put insurers in a great position to benefit from predictive analytics. Making predictions leads to more accurate risk assessments, which reduces costs and increases profits, so it comes as no surprise that 80% of insurers report a positive business impact from predictive analytics.
Dynamic policy pricing: Individualized pricing recommendations help insurers mitigate risk by ensuring profitable policies.
The manufacturing industry has undergone a data revolution, with manual data collection methods being replaced by IoT sensors connected to equipment. This has improved the quality of data and augments the impact of predictive analytics.
Predictive maintenance: The ability to detect machinery failure before an event helps manufacturers reduce downtime and have their machinery run reliably.
Facility optimization: Optimization of energy usage increases energy efficiency, leading to greater profitability for manufacturers.
Like manufacturing, IoT has improved the amount and quality of data that logistics and supply chain industries have at their disposal. Using this data to make predictions allows industries to be more resilient in changing market dynamics.
Demand forecasting: Forecasting demands provides a way for supply chain and logistics industries to mitigate risk against fluctuating levels, and increase operational efficiency.
Optimal routing: Predictive analytics can be used to calculate the most cost-efficient way to transport goods.
Real estate use cases for predictive analytics have some cross-over with those of banking, which carry the loans in their books. However, a unique application is property price prediction.
Property price prediction: Accurate, data-based property estimates using predictive analytics lead to improved decision-making around investment, and improved asset management with better risk evaluations.
As the predictive analytics industry grows, more tools are appearing that allow non-technical users to solve their business problems. Here’s a short list of a few, and who they might be best suited for.
The AI & Analytics Engine: The Engine can be used by non-data scientists, like marketers, business analysts, and entrepreneurs in either SMBs or SMEs. Key features include data visualization, data wrangling, and explainable AI.
Amazon SageMaker: AWS SageMaker is a powerful predictive analytics tool, its advanced features are geared towards beginner and advanced data scientists, and cost means it’s primarily used in large enterprises. Advantages of SageMaker include the hyperparameter tuning and ease of deployment.
Trifacta: Trifacta is also a tool that caters to small to large enterprises. It’s often the choice for data engineers and data analysts due to highly developed data transformation features.
Obviously AI: Obviously AI is appropriate for non-technical users in mid-market to enterprise-sized companies, and its free tier makes it great for students. Some unique advantages are its sharable models and ability to download model code.
The predictive analytics industry is one of the fastest growing industries expected to rise at a CAGR of 20.4% until 2028. Adoption is expected to rise exponentially, with its effectiveness being augmented by the trend of companies focusing on recording high-quality data. In addition, the adoption of cloud data services is making it more and more convenient than ever to put that data to use.
Some emerging trends of predictive analytics are:
No code predictive analytics: More and more predictive analytics is becoming more accessible to business users who have little to no experience with coding or data science. Dubbed “democratization”, users like marketers and analysts can perform predictive analytics without needing to code.
Predictive analytics as a service: With significant resources needed to set up predictive analytics infrastructure internally, outsourcing it to third parties is becoming increasingly attractive for SMBs.
Real-time predictive analytics: As mentioned, cloud data services increase the speed at which data can be stored and processed, and using predictive analytics as data is generated is likely to gain momentum.
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