Home → Blog → Predictive Analytics for Business Leaders: Daily Operations Into Decisions
Predictive analytics is the future of data-driven decision-making. From customer relationship management to sales forecasting – it helps organizations better understand their internal processes and make data-based decisions.
In this blog post, we will review the most popular predictive analytics use cases in different industries and best practices of using predictive analytics in your workflow.
Main reasons business should apply predictive analytics for operations
Global predictive analytics is expected to grow by 21.9% annually from 2024 to 2033 and reach $108 billion by 2033.
Such industries as finance, healthcare, manufacturing, and sales already use predictive analytics to make smarter business decisions, forecast customer purchase behavior, and offer more personalized solutions.
The most popular use cases of predictive models in business strategy are:
- Customer analytics
- Financial analytics
- Marketing & sales analytics
- Risk analytics
- Supply Chain analytics
- Web and social media analytics
Let’s review the main reasons why companies decide to integrate predictive analytics into their workflow.
Better decision-making
One of the key reasons for the predictive analytics market growth is the growing demand in data-driven decision-making.
Businesses process and generate a large amount of data, that’s why it’s crucial to regularly analyze this data and extract meaningful insights for business growth.
Business intelligence and predictive analytics process historical data and identify common patterns and trends, helping companies forecast demand, revenue, and risks.
Let’s take the finance industry as an example. Predictive models can analyze past income and expenses to predict future cash flow. This helps companies to plan budgets more accurately, avoid cash shortages, and know when it’s safe to invest.
Better customer experience
With the help of predictive analytics companies can gather structured and unstructured customer data from various sources such as CRM, social media, and website interactions. This helps businesses better segment their customers based on purchase preferences, demographics, or online behavior.
Let’s say you want to segment your customers based on the price sensitivity. With the help of predictive analytics in business, companies can forecast how customers react to price changes by dividing them into price-sensitive customers, value-driven customers, and premium buyers. Based on this data, companies can offer personalized discounts instead of giving everyone the same promotion.
Costs reduction
Predictive operational analytics helps reduce costs by anticipating problems and optimizing decisions before budget is spent. Instead of reacting after losses happen, businesses can predict risks and allocate their budgets smarter.
For example, in manufacturing, predictive analytics can prevent equipment breakdowns by forecasting when equipment is likely to fail. In addition to cost savings, manufacturing companies get fewer emergency repairs, less downtime, and longer asset life.
Predictive analytics best practices
Before implementing forecasting tools for business, we recommend to prepare and make sure your business is ready for such changes,
Define your the main business goals you want to achieve with predictive analytics
Don’t use predictive analytics just for the sake of using. First, you have to understand why exactly you need predictive analytics and how it will help you achieve your business goals.
Create a list of your main business goals and their impact on your revenue and think how you automate them with the help of predictive analytics. We recommend to start small, apply predictive analytics to a specific use, measure the results, and only then scale further.
Check if you have enough data and merge the data from various sources
According to Demandsage, 40% of companies state that one of their key challenges in predictive analytics is isolated or insufficient data.
In order to make predictive analytics work precisely, you need at least 6-12 months of past data about sales, customers, operations, or finance. Also, your data has to be well-structured, consistent over time, and match the output you want to predict (sales, churn, demand, costs, etc.).
However, it’s wrong to assume that you need a perfect database. Modern technologies are able to work with imperfect data, handle inconsistencies, and structure even messy data.
What’s more important is uniting the data across various repositories to get a holistic view of and avoid data silos. Companies need to ensure that their data is consistent to avoid inaccuracies in predictions.
Assess return on investment
According to statistics, almost 30% of organizations consider cost of implementation as the top concern when it comes to adopting predictive planning tools. That’s why companies should calculate return on investment and assess if the tools will really add value to their business and outweigh the implementation costs.
To assess the return on investment of predictive decision-making, compare the financial gains it brings with the total cost of the project.
Start by measuring what improvements you will get, such as lower operational costs, reduced churn, fewer inventory losses, or higher sales conversions.
Then calculate how much money these changes save or generate, and weigh that against the cost of development, tools, and ongoing maintenance. The difference will show the ROI and helps determine how quickly the investment pays back.
Constantly track the model performance
Predictive analytics is not about setting and forgetting. You need to constantly feed the model with new data, track if the model interprets this data in the right way, and make adjustments if necessary.
Track key indicators like changes in data, errors, and business impact to ensure the model stays reliable in real-world conditions. If performance drops, retrain or update the model with new data to keep it relevant and effective.
Real-world impact of predictive analytics
Predictive analytics is on the rise, helping organizations better process their internal data. Let’s review the real-world impact of predictive analytics in various industries.
Predictive analytics in healthcare industry
The healthcare industry generates a large amount of data from such sources as wearable devices, mobile applications, electronic health records (EHRs), and patient data bases. That’s why hospitals and medical organizations use AI and predictive analytics to better understand their patients, forecast their needs, reduce wait times and control drug and operational supply costs.
For example, a clinical intelligence platform Cohere Health has launched predictive analytics tools to forecast medical utilization and shifts in medical loss ratio. Such an approach helps medical organizations prepare for potential utilization growth in advance and forecast their potential expenses.
Predictive analytics in finance and banking
Banks use predictive analytics to assess financial risks, prevent fraud, understand customer behavior, and make smarter financial decisions. Predictive analytics models also analyze transaction patterns to detect suspicious activity in real time, reducing fraud losses.
For example, JPMorgan Chase has implemented a predictive analytics platform to automate the analysis of complex legal and financial contracts.
This helped them save 360,000+ hours of human effort annually spent on the contract review process and achieve better contracts accuracy and consistency than with manual reviews.
Conclusion
Predictive analytics turns complex data into clear and actionable insights, helping businesses make data-driven decisions, personalize customer experience, forecast risks, and save costs on manual work.
The key here is starting with a specific business problem and ensuring the data is structured and consistent to make a model work in the right way. If businesses take a strategic approach, predictive analytics becomes a valuable tool that optimizes internal processes and helps companies get the most out of their data.
