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Predictive Analytics Tools
What is Predictive Analytics?
Predictive analytics can be a broad term, referring to a wide range of techniques and tools, but the basic concept comes down to this: You take historical data, and you use that to predict future trends.
Even without prediction software, this is something we do every day. If we know we went through that last bag of coffee beans in about a week, then we’ll take that into account when writing our grocery lists. More advanced predictive analytics techniques allow us to do this on a larger scale, applying it to business and marketing, scientific research, and so on.
Prescriptive analytics would be the next step, being data that is actionable rather than strictly informative. To give a few prescriptive analytics examples:
- Self-driving cars. Our decisions behind the wheel are often made on intuition, instinct, and experience. We don’t make a plan to give the driver in front of us a little extra distance, we just know that when someone is swerving like that, we feel safer a few extra car-lengths back. Self-driving cars can make these same decisions based on traffic safety data.
- Hospitals. When a healthcare facility has a lot of patients and limited staff, they may need to look at data for who is most at-risk, and therefore most in need of immediate care in order to prioritize patients.
- Publishing. Publishers have been using prescriptive analytics since long before we had the software to make it easy. If they see that books about animals are doing well, then they publish more books about animals. If they see that celebrity memoirs are selling like hotcakes, then they get in touch with some celebrities.
Effective predictive analytics tools integration means utilizing all four types of data analysis: Descriptive, diagnostic, predictive, and prescriptive.
Almost every business in the world uses predictive analytics testing tools to collect data, but they don’t always put that data to use, but they should. Predictive analytics helps you to remove the guesswork and make more confident decisions no matter what line of work you’re in.
Industries that use Predictive Analytics
Predictive analytics can be used, and have been used, in just about every single industry. But there are some industries where predictive analytics is more likely to serve as a cornerstone of a whole organization.
- Retail and e-commerce. Retail has always been based on supply and demand. With predictive analytics tools for e-commerce and in-person retail, we can develop new products based not only on what is currently in demand but on what will likely be in demand in the near future, staying a step ahead of the market.
- Banking and financial services. Predictive analytics in revenue organizations, banking, investing, and accounting can help us to predict upturns and downturns, and even identify fraud by detecting hidden patterns that might not be easy for a team of human investigators to identify.
- Human resources. HR analytics can be used on a microscopic and macroscopic level. Examples of prescriptive analytics in human resources would include a hiring department predicting a surge in business and hiring more staff to handle the extra workload, or analyzing a number of applicants to narrow it down to those who would be a good fit for our company, those who would adapt well to our culture, those who can learn new tools quickly and so on.
The above-listed industries rely heavily on predictive and prescriptive analytics, but the list of industries that can and do benefit from these tools is endless. Automotive repair, restaurants, web design, if it’s a business model, it can benefit from predictive analytics.
Descriptive Analytics vs. Prescriptive Analytics
As mentioned, there are essentially four types of data analytics: Descriptive, predictive, prescriptive, and diagnostic.
The difference between descriptive analytics and predictive analytics is that descriptive analytics tell you what has happened, where predictive tells you what will, or might, happen. And then from predictive analytics, prescriptive analytics tells us how we might act on what we know.
So, examples of descriptive analytics might be:
- Measuring the clickthrough rates for an ongoing Facebook ad campaign.
- Counting subscriptions to a streaming service before and after the release of a new series.
- Analyzing the results of a survey.
Predictive and prescriptive analytics may not be a crystal ball, but it’s not just guesswork, either. With predictive analytics technology, we’re taking what has happened in the past, and making an estimate on what might happen next. So predictive analytics examples would include:
- Observing that family movies tend to sell more tickets during the holidays, and scheduling new film releases accordingly.
- If you’ve seen the movie Moneyball, or read the book it was based on, you’ve seen an example of statistical analysis in building an effective sports team. This doesn’t have to apply strictly to sports, either. Predictive analytics tools can be used to measure a job candidate’s past successes and determine if there’s a place for them in your organization.
- Hurricane tracking is actually more accurate now than it was a few decades ago, thanks to predictive analytics.
In any for-profit organization, it’s easy to see how this sort of data would be useful from the ground up. Starting a boutique clothing store and not sure if you’ll be able to afford rent on some commercial space downtown? Check the analytics and find out. Wondering if your pizzeria can actually benefit from a local TV campaign? Check the data. At a base level, predictive and prescriptive analytics can, at the very least, give us an idea of what we stand to gain when taking new risks, how we should budget, and what next month’s revenue should look like.
How to Calculate your Predictive Analytics
So now that you have a solid idea of what predictive analytics is, how it differs from other data collection models, and how it’s used, how can you put it to use? Let’s dig into the real nuts and bolts of the matter.
Predictive modeling techniques are the method by which we can measure probability by looking at existing data. Predictive analytics tools and techniques cover a range of sophisticated approaches calculating dozens, hundreds, or thousands of points of data in order to come up with some usable, applicable results.
There are predictive analytics algorithms and methods you can actually do yourself right now. For instance: Multiple regression forecasting Excel sheets. You can run your numbers right into a spreadsheet and come away with some usable findings simply by tracking one set of data in column A, another set in column B, and then looking for patterns in column C.
For instance, if you wanted to know how well your car was going to hold its value, you could take the last ten years of models, but their sticker price in column A, subtract it from their current Kelly Blue Book value in column B, then across column C, you could see the general trend in value loss year over year.
If you know a bit of coding, you can up your game and work these same principles into a prescriptive analytics Python tool, or C++, Java, or any programming language you like.
Predictive and prescriptive analytics techniques are very simple at their core. You’re simply comparing a number of data points and looking for patterns. It’s not hard to get into the basics of predictive analytics for beginners. It gets a little more difficult and a little more complicated when we add more and more data into the equation.
With more data, you can get more accurate results, just as a survey is more precise with larger sample size. But as it gets more precise and more complicated, you’re going to need more sophisticated predictive analytics tools and techniques. Simply put: it gets harder to do this stuff on your own in an Excel spreadsheet when you’re adding up thousands of points of data that are constantly changing in real-time more quickly than any individual human can keep track of.
That’s where the software comes in.
If everything we’ve discussed above sounds complicated, this is where it gets easy: Letting the predictive analytics software crunch the numbers for you.
There’s plenty of forecasting software free on the internet, and these can get you started, but don’t be surprised to find that they do not tend to rank among the best prescriptive analytics tools. If you want advanced predictive analytics, you’ll eventually have to go beyond the free data analysis tools for Excel.
Most of what you’re going to get through a free data analysis software download can help you to solve small problems, but if you’re looking to increase revenue and make more efficient use of it, you’re eventually going to need a little more than a predictive analytics Excel template you downloaded from someone on Reddit.
With Canopy you can do all of this on a broader scale, in real-time, and with considerably less work on your part.
Canopy can help with revenue leadership, sales & revenue ops, productivity, and general efficiency through real-time analysis, keeping constant tabs on your numbers, and helping you to determine exactly what you’ve got to do to get where you’re going.
There are plenty of analytics platforms out there to choose from, but Canopy is built specifically around sales management, making it easier for you to lead your team by providing you with a precise roadmap of your market.
If you’re ready to get started, sign up for the free demo and see what you think.
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