Predictive analytics utilizes and reflects a variety of statistics-driven techniques from data acquisition, predictive models, and machine learning that combine to meaningfully analyze current and historical data trends and current realities to generate predictions on future events and continuations of trends related to future or not-yet-real events and circumstances. There are many predictive analytics examples in use across many industries today.
Examples include items like competitive advantage for market behavior, exploring new opportunities to generate revenue, optimizing fraud detection early, streamlining business performance and processes, maximizing utilization of company assets, optimizing capacities for production, quality control, and more, and risk management.
Below are a few examples of how companies use their data analytics and business intelligence complexes to drive better decision-making, improve customer experience, and achieve more sustainable growth.
Applying predictive analytics to assess customer behavior
Today’s companies must keep attracting new customers to stave off the loss of customer churn in order to avoid long-term revenue loss. The cost required to acquire a new customer is almost always higher than the cost of keeping a current customer, so special measures must be taken to retain customers as aggressively as possible.
As one of the most typical examples, predictive analytics can utilize machine learning and historical data to help prevent customers from leaving unnecessarily and keep a customer base happy by looking at historical markers of unhappiness that led to a customer leaving (negative reviews, slow processing times, etc.) and by predicting the kinds of customers or customer groups who are most likely to depart in certain conditions.
Companies can use real-world data science to take the kinds of nurturing and preventive actions needed to optimize customer experience and prevent revenue disruption.
A predictive analytics model can also allow marketers to understand large volumes of consumer data to predict the kinds of potential customers that spend the most dollars in their revenue sectors, and thus contribute to the most profit for their division over time.
This is one type of many predictive analytics examples and informs at a high level how marketing campaigns can be targeted and how to avoid potential waste.
Achieving these insights can happen only through powerful predictive analytics that process volumes of information that would be impossible for a single human being or even a team, which in turn helps companies make large-scale decisions on marketing investments and correct customer targeting to get the customers with the highest customer-cycle value.
A tool can even analyze data on social media to help anticipate customer behavior. With the ability to analyze millions of words in seconds, this kind of deep learning simply isn’t accessible to or possible for a human analyst.
Because today’s large companies have multiple and varied requirements and therefore need to break their customers into segments based on unique business variables, it can be difficult at times to know how to allocate marketing dollars, customer support dollars, and more.
But a predictive analytics tool appropriately utilized can align a company’s business data to target the right audience, segments, and even full customer markets within a larger customer population based on variables and filters the tool is equipped to utilize.
Applying predictive analytics to anticipate hardware needs
Similar to using historical data to predict customer behavior, predictive analytics can use real-world data science to look at the focal data on machinery and other hardware and materials to predict things like equipment failure, system strains, and much more. This can, in real-time, improve operations and maximize revenue continuity.
Applying predictive analytics work to help anticipate repairs, replacements, maintenance and more is a significant factor in a company’s work, as it plays a crucial role in risk management, safety, and many other variables.
For hardware-sensitive environments, by utilizing things like cloud sensors in concert with predictive analytics, companies can anticipate and look out for maintenance needs and expenses months or even years before they occur. This is done through data capture and scanning information the equipment/machines generate to know when things aren’t working optimally or when they’ll decline. This results in controlling the costs involved with crushing preventive maintenance downtime and maximizing an asset’s life cycle.
Applying predictive analytics to help with risk management
Predictive analytics can help anticipate potential areas of business risk by combing through trends and tendencies in your performance data, employee trends, customer trends, operational trends, seasonal trends, and more to make accurate predictions on how these items might impact your organization over time.
These analytics, built by people with actuarial backgrounds and other analytic platforms, can be combined with a precise and calculated risk management process to understand the most critical threats and risks a company has, as well as prepare for worst-case scenarios and build effective response protocols.
Applying predictive analytics to help with supply chain issues
In some situations, predictive analytics can be deployed to help anticipate and plan for accurate customer demand forecasts. This can be useful for scenarios like avoiding overstocking inventory, as inventory can be very expensive to manage and store over time.
For example, a predictive analytics model might help to make sure a hardware company doesn’t assemble and stock too many lawnmowers during fall and winter months, as demand trends for most of the country will be on the lower end for lawn mowers in general.
The model will help to gauge supply chain errors and scenarios to guide purchasing decisions in order to not have too much or too little inventory at any given time.
Predictive analytics can also help to adjust prices for key items based on customer demand, market factors, and so on, and even generate things like targeted product markdowns, promotional offers, and variable-targeted items for multiple customer groups.
Predictive analytics examples: Applying predictive analytics to help with staffing needs
Predictive analytics can even be employed to help assess things like staffing needs in industries that have significant ebbs and flows over time, such as the entertainment or hospitality industry, for example.
One predictive analytics example is a model that can help utilize historical data and customer behavior data to anticipate that a certain cruise line may need to increase staffing for different months of the year, provide specialized skills for different months of the year, and so on.
Conclusion
More businesses than ever utilize predictive analytics than ever before, and predictive analytics examples across multiple industries are only growing as human beings find ways to be more efficient and scaled with their data science. As machine learning and artificial intelligence develop, predictive analytics will soon grow to be able to help with decision-making that until now has only been the domain of human beings.
