Nobody can see future events before they happen. However, anyone with the right tools can use past and present data to predict future outcomes. In business, predictive analytics is the use of data to predict future trends, potential outcomes, and performance. It’s a form of analytics that applies predictive analytics algorithms to a statistical model to generate the most likely business outcomes.
It’s not looking into a magic crystal ball, but predictive models are highly useful for a wide range of business purposes. Here, we examine ten powerful examples of predictive analytics and explore some of the most common use cases in today’s organizations.
Predictive Analytics Examples
Data scientists and business analysts can use predictive analytics in many ways depending on organizational goals. Whether your company wants to optimize the customer experience, increase sales, prevent fraud, or improve employee performance and productivity, statistical analysis can help.
1. Sales Forecasting
One of the most common examples of predictive analytics is sales forecasting. It’s some of the lowest-hanging fruit in predictive modeling, using historical data to predict sales performance in the coming weeks, months, quarters, or any other period of time. (Of course, the farther you go out, the harder it is for the model to make accurate predictions.)
Based on your company’s existing sales data, predictive analytics tools can forecast likely sales performance, factoring in natural growth, variables like the calendar, staff changes, demand increases, production increases, and much more. Using models, analysts can input a variety of factors to see how the model output changes, thereby identifying key factors influencing sales. That can offer powerful, actionable insights to hone your sales strategy.
2. Employee Retention and Attrition
Employee turnover is a natural part of every organization’s annual lifecycle. Depending on the industry, voluntary attrition ranges between 12 and 60% annually. That comes with significant costs, considering that hiring one new employee costs about $4,700.
Predictive analytics applications can predict employee turnover based on a range of factors. Whether your organization went through a round of layoffs, cut contracting staff, introduced new HR policies, adjusted business priorities, or a range of other factors, predictive analytics can deliver accurate insights on how certain changes impact employee retention.
Identifying key factors that contribute to employee churn will help business leaders reduce the impact of those factors or avoid them entirely to slow down employee turnover.
3. Talent Acquisition
Whether replacing departed employees or expanding the business, predictive analytics are also useful in talent acquisition. Models can be integrated with HR technology to flag the skills or expertise most associated with high performers within the organization. This informs the talent acquisition process as human resources leaders or recruiters know what to look for both in candidates who applied for a role and in passive candidates who haven’t actively pursued the role.
Likewise, models can be useful in predicting candidate success. By analyzing relevant data points like skills, experience, and answers to application questions, analytics teams can gauge which candidate is most likely to succeed in a role or even be a company culture fit.
4. Customer Churn Prevention
Just as predictive analytics can help predict employee churn, it can also predict customer behaviors.
By analyzing your sales funnels, models can get a complete picture of where your customer base most commonly abandon carts, what products they’re most likely to abandon, when they stop returning emails, how long salespeople typically go without customer contact, and many other data points.
Using that data, models can identify customers at risk of churning and help you develop targeted customer retention strategies for a range of audiences.
5. Marketing Campaign Optimization
Marketing is more competitive than ever today, meaning it’s absolutely vital to know your customers well. But it’s not enough to know your current customers, you also must understand how to engage audiences that haven’t yet become customers. Great marketing campaigns are omnichannel and include several variants to target many different audience profiles or buyer personas directly.
Predictive analytics applications predict campaign performance based on the success (or failure) of previous campaigns. Using historical data on messaging, channel, ad spend, and many more factors, you can tweak campaigns to better appeal to a range of target customers. As you test potential campaign performance, your marketing team can optimize marketing strategies and resource allocation to create more successful marketing campaigns.
6. Financial Risk Assessment
Finance teams use analytical tools for financial forecasting, risk assessment, and more. In larger organizations that take on a lot of credit or debt, prediction models are useful for predicting credit risk and default probability. Attempting different variables against the model can help enhance risk management strategies and find ways to reduce the impact of specific operational costs and other cost-reduction opportunities.
7. Project Success Prediction
Efficient organizations find ways to reduce waste. Failed projects are prime waste examples, so using data from past projects is extremely valuable in projecting the potential success of future ones.
Testing proposed projects against analytical models can help your organization prepare better project management principles from the get-go. Predicting project outcomes will help determine success factors, making it easier for project managers to set goals, prioritize tasks, and allocate resources and staff effectively.
A project’s success isn’t determined solely by its outcomes but also by its process. A project that goes over budget or beyond the timeline — even if it is ultimately completed with high quality — is less successful than a similar one completed on time and under budget. Optimizing project management and resource allocation with predictive analytics before you start can help ensure your team’s process is sound.
8. Demand Forecasting
Somewhat related to sales forecasting, demand forecasting predicts product or service demand. This could be based on seasonal trends, current or historical market trends, upcoming marketing campaigns, or many other variables.
For instance, a home goods retailer knows that sales holidays like Black Friday and the Christmas season will result in increased customer demand as people look to save on their products or buy gifts for loved ones. Using historical data, planned marketing campaigns, and production factors, predictive analytics models can help find the right balance to optimize inventory management, streamline supply chain management, and properly align teams to avoid waste and maximize profit margin.
9. Employee Performance Prediction
Advanced analytics can help recruiters, HR departments, and hiring managers predict the success of job candidates, but it can also be used to track and improve employee productivity. Using user behavioral data and individual performance data, employee monitoring tools can use analytics to identify your highest-, lowest-, and average-performing employees.
Not only that, but solutions may also flag high potential indicators, like short project turnaround time, and performance concerns, like a high absentee rate. This historical and real-time data helps leaders determine who is most worth investing in, who might need a little incentivization, and who may need a performance plan.
With employee data from monitoring solutions, prediction models can also aid teams in optimizing performance management and professional development strategies. Performance review cycles will become more data-driven, upskilling and training opportunities will be more targeted, and staffing shortages can be averted ahead of time.
10. Fraud Detection
Predictive analytics also has at least one important security application. By analyzing transaction patterns, predictive analytics can flag atypical large transfers, cross-border activities, and other suspicious activities or risk factors of potentially fraudulent activities.
Of course, fraud detection helps ensure the organization doesn’t get cheated out of payments, but it also helps catch malicious actors plaguing the industry. Most importantly, the better your predictive analytics become, the better your fraud prevention measures will become, too.
Predictive Analytics Use Cases
Most departments within an organization can use predictive analytics and statistical techniques in some capacity to make informed decisions. While at first blush, you may think of it as more of a sales and finance solution, prediction models can be useful for exploring a range of future outcomes or future trends.
Human Resources
As we discussed in the previous section, predictive analytics is highly useful for talent acquisition and predicting future employee performance. Human resources departments that use predictive analytics can more effectively plan and optimize an organization’s workforce and develop stronger employee engagement and satisfaction programs to retain the best talent.
Sales and Business Development
Many sales and business development departments use predictive analytics to forecast sales, but they can also use models in a few other key ways. Models may be leveraged to score and prioritize leads, saving salespeople time by helping them focus on the most likely-to-convert leads.
Analytics can also propose potential cross-selling and upselling opportunities for different customers by identifying where they are in the sales funnel and what products or solutions may make the most sense for their business.
Marketing
Marketing campaigns are only as good as their relevance. There’s a reason you don’t see ads for silly string on CNN. Predictive analytics can help marketing teams hone their campaigns to create better customer segmentation and targeting. Through more personalized content and recommendations, ads will have a better chance of converting and will save campaigns a significant amount of budget.
Finance and Accounting
Financial forecasts are a common application of predictive analytics. However, the finance and accounting departments can do even more with machine learning. Forecasting is just one element of budgeting and financial planning to help an organization remain lean, especially as it scales or invests heavily in certain parts of the business.
During periods of change, development, or innovation, predictive analytics can help finance departments forecast cash flow to ensure the organization has the solvency it needs.
Operations and Supply Chain
Depending on the size of your company and the industry you’re in, operations can be quite straightforward or an absolute bear to manage. Either way, predictive analytics can save time and money, and increase efficiency and product quality.
Consider a company that distributes its products to stores or directly to consumers worldwide. Predictive analytics can help optimize logistics and route planning to ensure items arrive on time, help create emergency solutions in the event of natural disasters or other obstacles, and account for barriers like the daily rush hour or increased postal traffic during the holidays.
Moreover, predictive analytics applications can predict equipment failures, assess maintenance schedules, and serve actionable predictions for improved asset management. Tracking the likely lifespan of individual pieces of equipment will help you fix potential issues before they get out of hand and effectively manage assets based on project needs.
Customer Service
Customer service is a hard, often thankless job. Machine learning and predictive analytics can make it a bit better, at least. Analytics models will help your organization properly staff support centers by predicting call volume at a certain time or around a specific product release.
Additionally, natural language processing (NLP) machine learning algorithms can help gather customer sentiment analysis to understand customer experiences with a new product, gauge customer loyalty and customer churn, and help support agents anticipate questions and issues. They can also predict feedback so you can begin working on solutions to improve the customer experience before you’ve heard many complaints.
Legal and Compliance
Avoiding legal or compliance pitfalls is essential for any business. Data violations, illegal activity, and compliance failures can lead to financial harm and reputational damage. Predictive models can help avoid these issues through potential risk assessment, insider fraud detection, and real-time analysis of potential issues with organizational activity.
Your organization could even set up real-time regulatory compliance monitoring and predictions with the proper parameters.
How Teramind Makes Predictive Analytics Easy
Our platform, Teramind, can be utilized for predictive analytics in the context of workforce behavior and security. Here are some ways Teramind can be used for predictive analytics:
- Behavioral Analytics: By analyzing historical data of user actions, such as application usage patterns, file access behaviors, and communication patterns, Teramind can identify anomalies or deviations from normal behavior. These anomalies can be flagged as potential security threats or indicators of productivity issues.
- Risk Scoring: Teramind can assign risk scores to users based on their behavior patterns. You can predict the likelihood of a user engaging in risky behavior or becoming a security threat in the future. These risk scores can help prioritize monitoring and intervention efforts.
- Anomaly Detection: Teramind detects unusual or suspicious activities in real-time. By comparing current user behavior against historical data, Teramind can identify deviations that may indicate potential security incidents, such as data exfiltration or unauthorized access attempts.
- Insider Threat Detection: Teramind can analyze user behavior to identify potential insider threats before they escalate. By monitoring for unusual patterns, sudden changes in behavior, or access to sensitive information, Teramind can provide early warnings of insider threats and potential malicious activities.
Conclusion
Everyone could benefit from being able to see the future. While that technology (or magic) has yet to exist, predictive analytics is the next best thing.
Using historical data and trends, proper data sourcing, and continuous improvement of prediction models, your organization can make data-driven decisions to assess the most likely business outcomes for various actions. As this post makes evident, almost every department in your organization can benefit from using predictive analytics solutions.
