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Business intelligence comprises a variety of practices and techniques used to analyze data and come up with ways to improve business practices. Examples may include a competitive analysis or compiling industry reports to see which markets are still available. These days, business intelligence often relies on advanced analytics techniques, such as statistical methods or using machine learning to leverage data science. One such advanced technique is predictive analytics.

A predictive model essentially uses data to create forecasts to determine the likelihood of future outcomes for a business. This is done with a combination of historical data, current data, and machine learning algorithms that are able to learn from collective experiences and use them to inform future decisions. This allows business users to transform information and insights gained from it into innovative plans for the future to gain a competitive advantage. Predictive analytics are used in a wide variety of complex industries including marketing, insurance, pharmaceuticals, finance, and more. Here are just three ways that you can use predictive models to improve your business process.

1. Boost your marketing effectiveness.

One of the greatest use cases for predictions is using them to determine how effective a marketing campaign will be and which potential customers you should apply each element to. By data mining your past marketing campaigns, you can determine which elements worked and which didn’t and apply that information to the new one. Customers who have spent a premium for your highest-quality items in the past, for example, are prime targets for your most expensive services.

Data-driven insights and machine learning now allow you to receive information about the effectiveness of campaigns in real-time, so you can preview outcomes and make adjustments on the fly. If you find that your online video advertisements are performing better than your other methods, for example, then you might want to improve your YouTube channel with a customized YouTube banner template. Your YouTube banner is important for putting your best foot forward online, as is your logo, font, and other visuals. If past descriptive models support a greater reliance on video content, you can use predictive analytics to determine how much you should invest.

2. Increase production efficiency.

The manufacturing process and supply chains are crucial for any business, and you’ll especially want to boost safety and efficiency on your factory floor if you’re creating your own products. Fortunately, there are many ways you can turn big data and predictive analytics to your advantage here. The Internet of Things (IoT) has made it easy to connect previously disparate parts of your business. Now, you can use sensors and cameras around your factory floor to ensure safety and increase efficiency.

Thanks to machine learning, computer algorithms can learn to predict when machines will fail and alert you to the fact before it happens. Even if a machine fails with no warning, sensors will set off an instant alert, so the problem can be addressed as soon as possible. The IoT even makes it possible for you to create a virtual version of your factory, so you can use predictive analytics to introduce and test new business processes to check their effectiveness before their full implementation.

3. Retain customers and provide exceptional service.

It’s always easier to keep an existing customer than it is to attract a new one, and predictive analytics tools are great for analyzing past customer behavior and using current data to determine how they’ll behave in the future. By predicting what customers will need before they realize they need it, you can make your business the solution to their new problems. Using predictive analytics alongside your CRM system will help you accurately forecast your sales revenue and gain actionable insights into how to better connect with customers in order to boost sales if it looks like you’ll fall short.

These are just a few use cases that demonstrate how predictive analytics can be applied to any part of your business. By experimenting with data and following best practices, you’ll likely find new uses for predictive insights in no time.

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