Predictive analytics has become a popular concept, with interest steadily rising over the past five years according to Google Trends.
Increasingly often, the idea of predictive analytics (also known as advanced analytics) has been tied to business intelligence. But are the two really related—and if so, what benefits are companies seeing by combining their business intelligence initiatives with predictive analytics? How does business intelligence compare with predictive analytics?
What Is Predictive Analytics?
Predictive analytics is a vital process that uses data analytics, statistical modeling, machine learning algorithms, artificial intelligence, and other advanced techniques to predict future outcomes. In essence, predictive analytics takes large data sets, including historical data, and applies various predictive analytic models to forecast future trends, events, or results. In various industries like healthcare and financial services, it plays a crucial role in decision-making, optimization, and uncovering actionable insights. Predictive analytics is one of the four primary types of analytics.
Predictive analytics refers to using historical data, machine learning, and artificial intelligence to predict what will happen in the future. This historical data is fed into a mathematical model that considers key trends and patterns in the data. The model is then applied to current data to predict what will happen next.
Using the information from predictive analytics can help companies—and business applications—suggest actions that can affect positive operational changes. Analysts can use predictive analytics to foresee if a change will help them reduce risks, improve operations, and/or increase revenue. At its heart, predictive analytics answers the question, “What is most likely to happen based on my current data, and what can I do to change that outcome?
Predictive Analytics Tools
These tools leverage big data and various types of data to create predictive models. They are at the heart of predictive analytics, aiding in pricing strategies, automation, fraud detection, and more.
Regression analysis, including linear regression, is a statistical technique used to understand relationships within a data set. For example, it can help in predicting credit scores or pricing strategies based on previous trends.
Time Series Analysis
Time series analysis is the examination of sequential data points measured at successive time intervals. This analysis is used to forecast future events such as stock prices or supply chain demands using current data.
Classification Analysis can be used in healthcare or retail sectors to categorize data into predefined classes. Machine learning algorithms like decision trees and neural networks may be applied here.
In big data environments, cluster analysis helps in grouping data with similar characteristics. This is vital in market segmentation, pattern recognition, and even fraud detection.
Ensemble methods combine multiple machine learning models to achieve more accurate predictions. This approach is particularly useful in credit risk assessment and other complex analyses.
Anomaly detection identifies unusual patterns that do not conform to expected behavior. In industries like financial services, this is key for fraud detection and equipment failure prediction.
Real World Examples of Predictive Analytics in Business Intelligence
For many companies, predictive analytics is nothing new. But it is increasingly used by various industries to improve everyday business operations and achieve a competitive differentiation.
In practice, predictive analytics can take a number of different forms. Take these scenarios for example.
Retailers and Pricing
Predictive analytics in the retail sector has been a game-changer. From predictive modeling for optimum pricing to real-time marketing campaigns, retailers use this tool for a competitive edge. Predictive analytics, along with retail analytics, plays a vital role in shaping the future of the retail industry, particularly in pricing strategies.
By analyzing large data sets including historical sales data, market trends, and customer behavior, retailers can deploy predictive models to determine optimal pricing for various products. These models take into account retail KPIs (Key Performance Indicators), such as sales growth, inventory turnover, and customer retention rates, to ensure that pricing aligns with overall business goals. The integration of predictive analytics with retail analytics provides a more comprehensive understanding of market dynamics, enabling real-time adjustments to pricing in response to fluctuating demand or competitive pressures.
Predictive analytics allows retailers to create personalized marketing campaigns and forecast inventory needs, ensuring that the right products are available at the right time and price. Retail KPIs help to measure the effectiveness of these strategies, allowing continuous optimization and alignment with business objectives. The use of predictive analytics in retail has transformed the industry, paving the way for more responsive, data-driven decision-making that enhances profitability and customer satisfaction.
Identify customers that are likely to abandon a service or product
Consider a yoga studio that has implemented a predictive analytics model. The system may identify that ‘Jane’ will most likely not renew her membership and suggest an incentive that is likely to get her to renew based on historical data. The next time Jane comes into the studio, the system will prompt an alert to the membership relations staff to offer her an incentive or talk with her about continuing her membership. In this example, predictive analytics can be used in real time to remedy customer churn before it takes place.
In the ever-competitive world of marketing, predictive analytics has become a quintessential tool to ensure that marketing campaigns are precisely targeted and highly effective. By utilizing machine learning, data mining, and other predictive modeling techniques, businesses can anticipate consumer behavior and preferences.
Here’s how predictive analytics can influence marketing:
- Personalized Targeting: Using historical data and pattern recognition, companies can tailor their marketing materials to individual preferences, ensuring that each campaign resonates with its intended audience.
- Optimizing Budget Allocation: By predicting which channels and strategies are most likely to yield positive outcomes, companies can allocate their marketing budgets more effectively, reducing waste and improving return on investment.
- Real-time Engagement: Predictive analytics enables businesses to engage with customers in real-time, offering special deals or information precisely when they’re most likely to make a purchase.
- Forecasting Future Trends: By analyzing large data sets, predictive analytics helps in understanding future trends in consumer behavior. This means that businesses can stay ahead of the curve, adapting their marketing strategies to align with shifting consumer needs and desires.
- Enhancing Customer Retention: Predictive models can identify customers at risk of churning and prompt real-time initiatives to retain them. This is often done through special offers or personalized communication.
- Improving Content Strategy: Predictive analytics can analyze which types of content resonate best with different segments of the audience. This helps in crafting content that is more engaging and relevant.
In sectors ranging from retail to financial services, predictive analytics in marketing campaigns has revolutionized the way companies connect with their customers. By leveraging machine-learning algorithms, data analysis, and deep understanding of consumer behavior, businesses can create campaigns that are not only more engaging but also more efficient and cost-effective. Whether it’s through optimizing pricing strategies or enhancing the overall customer experience, predictive analytics provides a robust set of tools for modern marketing professionals.
Send marketing campaigns to customers who are most likely to buy
If your business only has a $5,000 budget for an upsell marketing campaign and you have three million customers, you obviously can’t extend a 10 percent discount to each customer. Predictive analytics and business intelligence can help forecast the customers who have the highest probability of buying your product, then send the coupon to only those people to optimize revenue.
In today’s customer-centric business landscape, predictive analytics has been instrumental in transforming the domain of customer service. Leveraging data science, artificial intelligence, and machine learning algorithms, businesses are able to predict customer needs and preferences, providing support even before issues arise. For instance, predictive models using historical data can identify potential problems in products or services, allowing businesses to proactively address issues before they affect customers.
Real-time analytics can guide customer service representatives in providing personalized solutions based on an individual’s purchase history or past interactions with the company. This proactive and personalized approach not only enhances the customer experience but also builds loyalty and trust. In sectors like healthcare and financial services, where timely and accurate support is paramount, predictive analytics has proven to be an invaluable tool, driving efficiency and customer satisfaction. By analyzing large datasets, recognizing patterns, and employing predictive analytics models, companies are taking customer service to new heights, ensuring that customers are always met with timely, relevant, and effective support.
Improve customer service by planning appropriately
Businesses can better predict demand using advanced analytics and business intelligence. For example, consider a hotel chain that wants to predict how many customers will stay in a certain location this weekend so they can ensure they have enough staff and resources to handle demand.
In the healthcare industry, predictive analytics models are used for patient care optimization, early disease detection, and logistics planning. Predictive analytics, coupled with healthcare analytics, has revolutionized the healthcare industry, bringing a new level of precision and efficiency to patient care. By employing algorithms that analyze vast amounts of data, healthcare providers are able to forecast patient needs and provide personalized treatment plans, guided by specific Healthcare KPIs (Key Performance Indicators).
These predictive models not only contribute to improved patient outcomes but also optimize resource allocation within hospitals and medical facilities. Healthcare analytics focuses on actionable insights that drive decision-making, including patient satisfaction, treatment efficacy, and operational efficiency. The integration of machine learning, data analytics, and KPI tracking enables early detection of potential health issues, allowing for preventative measures to be implemented before conditions escalate.
Additionally, predictive analytics supports decision-making in treatment plans, supply chain management, and even administrative workflow, ensuring that the healthcare industry operates with maximum efficiency. From predicting disease outbreaks to tailoring patient care based on Healthcare KPIs, predictive analytics in the healthcare industry stands as a beacon of innovation, demonstrating the enormous potential of data-driven insights in transforming lives.
Financial Services and Credit Scoring
Financial institutions use predictive analytics for credit risk evaluation, fraud detection, and investment planning. It helps in analyzing credit scores and future financial trends. Predictive analytics has become a cornerstone in the financial services sector, particularly in the field of credit scoring. By leveraging vast amounts of data, including transaction history, credit utilization, and repayment behavior, financial institutions can develop robust predictive models to assess an individual’s creditworthiness.
These predictive algorithms, combined with machine learning techniques and data mining, provide a more nuanced understanding of credit risk and can even predict future financial behavior. They enable a dynamic response to changes in a customer’s financial situation, allowing for more tailored lending and risk management strategies. In addition to credit scoring, predictive analytics is used to detect fraudulent activities, optimize investment strategies, and enhance customer engagement through personalized offerings.
Financial services companies that harness the power of predictive analytics not only improve decision-making but also drive innovation in product development, pricing strategies, and customer experience. This technological advancement has set a new standard in the industry, leading to a more efficient, transparent, and customer-centric approach to banking and financial management.
How Does Predictive Analytics Work?
An accurate and effective predictive analytics takes some upfront work to set up. Done right, predictive analytics requires people who understand there is a business problem to be solved, data that needs to be prepped for analysis, models that need to be built and refined, and leadership to put the predictions into action for positive outcomes.
Any successful predictive analytics project will involve these steps:
- Defining the Business Problem: Identify the issue, like reducing equipment failures or improving metrics.
- Data Collection: Collect relevant data, considering factors like data quality, type of data, and size.
- Data Analysis: Data scientists and data analysts apply statistical techniques, machine learning algorithms, and deep learning to analyze the data.
- Modeling and Prediction: Building predictive models using regression models, neural networks, and other analytics models. Regular updates ensure accuracy.
- Implementation: The actionable insights derived are put into real-time workflows.
First, identify what you want to know based on past data
What questions do you want to answer? What are some of the important business decisions you’ll make with the insight? Knowing this is a crucial first step to applying predictive analysis.
Next, consider if you have the data to answer those questions
Is your operational system capturing the needed data? How clean is it? How far in the past do you have this data, and is that enough to learn any predictive patterns?
Train the system to learn from your data and can predict outcomes
When building your model, you’ll have to start by training the system to learn from data. For example, your predictive analytics model might look at historical data like click action. By establishing the right controls and algorithms, you can train your system to look at how many people that clicked on a certain link bought a particular product and correlate that data into predictions about future customer actions.
Your predictive analytics model should eventually be able to identify patterns and/or trends about your customers and their behaviors. You could also run one or more algorithms and pick the one that works best for your data, or you could opt to pick an ensemble of these algorithms.
Another key component is to regularly retrain the learning module. Trends and patterns will inevitably fluctuate based on the time of year, what activities your business has underway, and other factors. Set a timeline—maybe once a month or once a quarter—to regularly retrain your predictive analytics learning module to update the information.
Schedule your modules
Predictive analytics modules can work as often as you need. For example, if you get new customer data every Tuesday, you can automatically set the system to upload that data when it comes in.
Using Predictive Analytics to Influence Business Outcomes
Predictive analytics is not just a theoretical concept; it leads to real, tangible business outcomes.
- Business Intelligence Integration: By combining predictive analysis with business intelligence, companies create metrics-driven strategies.
- Supply Chain Optimization: Predictive analytics provides insights into supply chain needs and optimization possibilities.
- Customer Engagement: From targeted marketing campaigns to personalized customer experiences, predictive analytics offers an edge.
- Decision-making and Automation: Predictive analytics allows better decision-making and automation in various business initiatives.
Predictive analytics has become a vital aspect of modern business strategy, permeating everything from healthcare to retail. Its applications are varied and significant, contributing to solving business problems and shaping the future trends of industries. Whether you’re looking to dive into data science or leverage predictive analytics tools for your business, understanding this powerful technology can lead to transformative results. If you’re interested in exploring more about how predictive analytics can be applied in your specific industry, consider reaching out to experts or exploring available SAS tools and resources.
Predictive analytics is only useful if you use it. You’ll need leadership champions to enable activities to make change a reality. These predictive insights can be embedded into your Line of Business applications for everyone in your organization to use.
Predictive analytics has its challenges but can lead to priceless business outcomes—including catching customers before they churn, optimizing business budget, and meeting customer demand. It’s not magic, but it could be your company’s crystal ball.
Bringing It All Together: Using Predictive Analytics to Influence Business Outcomes
All companies can benefit from using predictive analytics to gather data on customers and predict next actions based on historical behavior. This information can be used to make decisions that impact the business’s bottom line and influence results. If you’re ready to learn more about predictive analytics and how to embed it in your application, request a demo.