7 Predictive Analytics Examples To Inspire Your Next Move
Humans may never be able to predict the future, but machines might just succeed in doing so. Predictive analytics is helping many businesses solve the problems of tomorrow by identifying them today and giving actionable insights.
This guide reviews what predictive analytics and data science are and how you can leverage them to gain a leg up on your competitors through better business intelligence and big data-based decision-making skills. I'll also outline seven real-world examples of predictive analytics in use to inspire your next move.
What Is Predictive Analytics?
When collecting data to discover actionable insights, businesses may use four types of analytics. Predictive analytics focuses on what's likely to happen in the near future so your business can prepare and respond accordingly. One of the largest drivers of predictive analytics today is the emergence of machine learning and automation technology. This is becoming the basis for the best marketing analytics tools.
Machine learning uses what happened in the past to find links in peoples’ behaviors and decisions to predict what they’re going to do in the future by creating predictive models and studying data sets in real-time.
In this article, I'll dive into seven predictive analytics examples and how they helped businesses completely transform their playing fields. When used properly, predictive data analytics can help you stay ahead of the pack as well.
7 Predictive Analytics Examples
Predictive analytics can solve many problems for businesses across multiple areas of expertise. You could use predictive analytics to determine how to cut your overhead costs by 25%, increase your sales closing rate by 15%, or scale your business to meet rapidly growing demand. By predicting future events, you can move your business into an ideal position to benefit.
We’ve selected the following case studies as examples of businesses that owe their success to predictive analytics, data-driven marketing strategies, and machine learning technology.
Seebo reaches a 98% delivery rate
Biotech and healthcare company Seebo needed multiple solutions for problems that plagued its production and delivery capacity. The company found it spent too much time on cleaning and maintaining equipment and was disposing of a lot of waste. Production stoppages were eating into Seebo’s profits by reducing its manufacturing capacity and increasing delivery times.
Seebo turned to the use of predictive analytics to identify and solve the core issues at play. Downtime was reduced by more than 83%, production capacity went up by over 5%, and Seebo reported 98% delivery time. The reduced downtime saved Seebo a lot of money, with an estimated downtime cost savings of 72%. Fewer costs and streamlined production led to incredible returns on investment.
Airbnb grows 43,000% in 5 years
One of the greatest challenges new businesses have is how to scale when facing exponential demand. Airbnb is an example of a company that went from nothing to a major player in the industry nearly overnight. It credits a 5-year growth rate of 43,000% to using machine learning technology and predictive analytics.
The model Airbnb used started with historical data to establish patterns of human behavior. The insights gained from data mining and predictive analytics tools were the basis for Airbnb’s next phase of planning, but Airbnb didn’t stop there. It tested these insights to determine the accuracy of the predictions, and the results were fed back into the analytics machine to create better predictions.
Amazon's predictive ordering patent
One common problem for retailers is trying to keep products in stock that customers want while not dedicating space to items that won’t move. Amazon invested in the creation of a new predictive analysis technology that allows it to remain one step ahead of its customers and fill its inventory space with products just as they become in demand.
The result was that Amazon placed orders for products it knew customers were going to buy and had them sent to fulfillment centers close enough to reduce delivery times significantly. This process is why many people enjoy such quick deliveries from Amazon and Amazon runs its supply chain so effectively.
Gramener & Microsoft identifying wildlife
Nonprofit organizations need to put every dollar they invest to good use. It paid huge dividends for the Nisqually River Foundation to team up with Gramener and Microsoft to solve one of its greatest problems.
One of the core purposes of the foundation is to track the many species of fish along an 81-mile stretch of river in Washington. Having people catch, tag, and release fish can become time-consuming and tedious, so Microsoft and Gramener created a predictive analytics model for the nonprofit organization. An AI used videos of the river to effectively identify and count the various types of fish in the river.
The foundation reported the accuracy of the predictive analytics model was a 73% improvement over manually tracking the fish, and it reduced related costs by about 80%. Now the Nisqually River Foundation can invest that money in other, more productive ventures.
Triaz Gruppe saving on catalog costs
Triaz Gruppe used predictive analytics to accurately assign a value to each of its customers when sending out catalogs. This also allowed the company to determine the best potential return on investment for each of its ads so it could discontinue ineffective ads in favor of ones that would garner more attention from potential customers.
One of the problems Triaz Gruppe identified was that it was sending out too many catalogs. The potential return on investment was greatly reduced due to the cost of printing these catalogs, so determining the right number to send was crucial to reducing costs and optimizing conversion rates for its marketing campaigns.
Würth raising sales efficiency
Knowing what products to show potential customers can make a difference between getting the sale and watching the client walk. Würth uses an algorithm to study customer behavior and determine what products each customer is most likely to be interested in before they’ve made initial contact. Once the customer reaches out, the algorithm determines which product segment they belong to.
Sales representatives are given new customers in segments they specialize in. With a success rate of assigning customers to the correct segment of over 85%, machine learning technology has greatly increased Würth’s closing rates and helped sales representatives optimize their time by working with customers with a high probability of making a purchase. Another benefit is that the customer experience has improved, leading to better retention rates.
Global chain increases coupon usage by up to 15%
Artificial intelligence is helping grocers solve a problem they’ve been aware of for years. Many customers don’t pay attention to the coupons printed on their receipts, and the rise of digital media has reduced the number of coupon-cutters who use their weekly newspapers to find savings.
In our last predictive analytics example, a global food retail company learned how to increase coupon usage by 15% and used the knowledge to generate three times as many coupon campaigns. This had a positive impact on over 10,000 stores as customers purchased more products so they could use their coupons.
Getting Started With Predictive Analytics
Deep learning technology is being deployed rapidly across the financial services, human resources, and healthcare industries. Being able to identify and predict future trends has many potential applications for B2B predictive marketing.
With advances in artificial intelligence making the news headlines frequently lately, you may be wondering how you can leverage machine learning algorithms to benefit your business. Before you jump into the deep end of the pool, you need to know a few things. Predictive analytics isn’t the end-all-be-all solution to everything, and it’s a good idea to combine it with other analytics strategies.
You should be aware of what real-world problems you’re trying to correct when using any analytics, collect the right information, and build your predictive analytics models. After you’ve gathered actionable insights, it’s time to put what you’ve learned into action so you can reap the rewards.
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Identify the Problem You Need to Solve
As we’ve shown in the stories above, every organization that used predictive analytics to its advantage had a unique set of problems. In some cases, it was that production costs were too high or they weren’t reaching the right client base. Other examples required companies to consider ways to increase their reach and engagement with customers.
There are many types of data to consider when forming your own predictive analytics solution. Try to evaluate your own company’s weaknesses so that you have a purpose for using analytics. Once you identify one or more problems, you can begin to look for insights on how to overcome them.
Ensure You Have the Necessary Data
A seasoned data analyst knows that machine learning technology is only as good as the information you feed it. If you’re trying to determine how to increase customer retention rates, for example, collecting data about the cost of production is a waste of time and resources. When you know what problems you’re trying to solve, you also know what predictive analytics algorithms to use.
In many cases, businesses are trying to gain insights into multiple problems. Just don’t fall into the trap of collecting a ton of data that you’re not using or gaining any insight from. You want to maximize the effectiveness of your time and resources, including your investment in marketing intelligence software.
Build Predictive Analytics Models
Don’t be afraid to build your own predictive analytics models. Businesses have put machine learning to use in profoundly unimaginable ways. For example, you could use predictive analytics to determine when the optimal time is to repair the machinery at a production facility. You could schedule employees so that busy times are covered and there's minimal downtime, maximizing the value of your staff’s wages.
Another useful model can predict how much cash you’re going to have on hand in the future and where it would be best invested. Having an accurate prediction of how much money you’re going to have available in the next 6 months to a year can be really informative if you’re trying to scale your business or open new locations.
Put Your Insights Into Action
The final step is to do something with the information you’ve acquired. You can spend decades gathering information and gleaning insights from it, but it’s a waste of time if you’re not making any adjustments. One of the problems some businesses have is that they sit on their hands despite having valuable insights at their fingertips.
If you don’t create an action plan with these insights, one of your competitors might.
How Do You Plan To Use Predictive Analytics In The Future?
Have ideas for how to use AI and machine learning technology we didn’t discuss today? Join the conversation and let us know how you think predictive analytics will shape the digital marketplace in the years to come. Don’t forget to subscribe to our newsletter for the latest news and stories.
