Developing a corporation from scratch in the 21st century has become much more difficult than you would’ve imagined a few years ago. In today’s world, almost everything is done through a proper analysis of the audience. And in most cases, it’s done through data analytics.
Usually, there are four keys to data analytics. These may include the following –
- Descriptive analysis.
- Diagnostic analysis.
- Prescriptive analysis, and
- Predictive analysis.
Descriptive analysis is about answering the question, “what happened?” It’s generally done after you have implemented a new strategy in your business and failed.
Then the process of diagnostic analysis tries to dig even deeper and finds the answer to “why’s such a thing happened?” With it, you can understand where everything went wrong for you.
Once it’s done, you’ll need to start with a prescriptive analysis to determine your next step. It may include apologizing to your target market for your mistake or making amendments.
Finally, predictive analysis is performed at the end to understand what may happen later. However, the ability to predict the future can be quite complex and convoluted.
So, it might be better to know more in this context before undergoing the process.
What is Predictive Analysis?
Predictive analysis is a specific branch of advanced analytics that can make a proper prediction about the future outcome of a business-oriented action. In this case, you’ll need to amalgamate data mining and statistical modeling with data mining techniques to get the result you want.
Why is it being used, though?
As mentioned before, most companies use this technology to find out a flaw in their system that can affect them in the near future. However, in some cases, it can also be utilized for perceiving your brand audience and getting clarity regarding your business strategy.
The best thing about predictive analytics is that it can be conducted either through a machine learning technique or manually. Nevertheless, in either case, the outcome you’ll get is based on assumptions. Therefore, it might be better to predict the same result through different variables.
An Example of Predictive Analysis
The process of predictive analysis can be performed through more than one techniques. And amongst them one such option is the regression analysis.
When conducted, it can be used to determine the core relationship between two different variables, including – multiple regression and single linear regression.
The relationship between these is written as a type of mathematical equation, which can help you predict the outcome when one variable changes.
Instances of Predictive Analysis in Action
In this section, we will check some examples or instances of predictive analysis being in action. It will help you understand how the process usually works in different industries.
Let’s begin, then.
1: Finance Department – Forecasting the Cash Flow for the Future.
Earning a huge amount of money shouldn’t be the only goal of an organization. Besides, it should also focus on keeping a periodic financial record to keep an eye on its cash flow. And this is where predictive analytics can play a massive role for them.
With it, you can dig through the historical data and learn about your previous financial status or statement. In addition to this, it will also be easier for you to extract data from your industry so that you can project future sales accordingly.
The better you forecast your future, the easier it will be for you to set up a financial aim for the business and succeed in your venture. You can also clear out the uncertainties perfectly.
2: Marketing Department: Behavioral Targeting.
When it comes to marketing, consumer data will be available in abundance. Therefore, you can easily leverage the same to create advertisements, content, and strategies. And if you are able to do it correctly, you can reach your potential consumer base fairly quickly.
However, in this case, you will need to focus on your historical behavioral data and utilize it to predict what might happen later. And that’s where predictive analytics comes in. You might use this tool or procedure to forecast the trends related to sales and plan your campaigns.
Historical behavioral data can also help you predict your lead’s likelihood of moving down the sales funnel (from awareness to purchase). For example, you may use a single linear regression model to find out the number of content your lead is engaging through prediction.
The more engagements they’re making, the more likely their conversion to your customer from a potential one becomes. Thus with such knowledge, you can easily plan a targeted ad to propel their move down the funnel more quicker.
3: Hospitality and Entertainment – Determining the Staffing Need.
Casinos have been using predictive analysis almost since the beginning of their venture, and the one-and-only Caesars Entertainment, a hotel operator, is one of them.
How do they do it, though?
In the industry of hospitality and entertainment, customer outflux and influx usually depend on more than one factor. But, all of them generally play into a single aspect – the number of people or staff members working for you. For instance –
- Overstaffing can cost a lot of money and might lead your company to go broke.
- Understaffing might result in offering a bad customer experience, making costly errors, and dealing with overworked employees.
Hence, in order to predict the total number of check-ins on a single day, a team of Caesars has developed a multiple regression model.
It, in turn, enabled them to staff its hotels by predicting the number of guests that may arrive on that day and offering them fulfilling service. The model also predicted how many people might be required to serve a specific number of invitees – and prevented the issue of overstaffing.
4: Manufacturing Department – Reducing the Risk of Malfunction.
The previous examples essentially use predictive analytics to take a specific action based on an event that might happen in the future. And the same can be done in manufacturing too.
For example, in this field, you can use various algorithms to learn your business’s historical data accurately and predict when a piece of specific machinery will malfunction. When the criteria for a forthcoming malfunction are met, the specific algorithm will trigger an alert to inform you.
You or another employee, who is using the machine, can stop it and save the organization from losing a massive chunk of money. However, remember this analysis will only predict all of the malfunction-related scenarios at the moment.
Hence, if it has predicted a machine to go down in the near future, it may be better to try taking a careful approach while working.
In any case, some algorithms can also recommend optimizations and fixes for a specific tool or machine in order to –
- Saving your time,
- Improve the efficiency of the product,
- Avoiding future malfunctions, and so on.
However, in this case, you might need to use prescriptive analysis alongside the predictive one to get a clearer assessment. The problem will be solved anyway.
5: Healthcare: An Earlier Detection of Various Allergic Reactions.
In the healthcare industry, predictive analysis can be used to detect an allergic reaction before it even occurs. An example of such is AbbieSense. It’s a sensor that can detect the early signs of an allergic ailment known as anaphylaxis. And it does so far quicker than a human being can.
Here’s how it works.
The sensor is usually connected to the body of the patient. So when an allergic reaction is about to occur, a specific algorithmic response gets triggered. This, in turn, can predict the severity of the reaction, alert the caregiver and the patient, and inject epinephrine if necessary.
Predictive Analysis – Benefits and Disadvantages
When used alongside another technology or analytical technique, the predictive analysis generally doesn’t make any mistakes at all. However, in some cases, it might falter a little. Hence, in this section, we are going to learn about both sides of the coin and see if the predictive analysis is ideal for your business or not. So, let’s get started with it.
The Benefits
- It offers more than one actionable insight to aid you in getting ahead of your industry’s competition and generating more revenue.
- It can save a lot of your time that would have been used for manual testing and doing a bout of research on your working models.
- It may reduce the overall wasted capital on different yet ineffective marketing processes or campaigns.
- Predictive analysis can help you ‘predict’ nearly any calamity you might encounter later on. This, in turn, can help in saving a lot of your money while optimizing your common flow of work through and through.
- Predictive analysis, although a little less tangible early on, can become pretty reliable or dependable later on. The issue of making mistakes or proffering an error-prone decision can also become quite a rare occasion as time goes by.
The Cons
- It might take a little bit of time to produce proper results.
- The data-gathering effort, in this aspect, will be time-consuming and require quite a lot of preparation upfront.
- It may require a lot of expense at the beginning, alongside some initial disruptions.
How Do You Make the Most of Predictive Analysis?
When it comes to predictive analysis, there are quite a few drawbacks hanging with it, as you’ve learned already. However, most of these issues generally occur during the beginning session.
Once you get through that period, it will become much easier for you to analyze everything in a proper manner. Nonetheless, if you want to ensure better results during the earlier phase, it may be best to perform the analytical process twice.
This way, you will get something that’s closest to the correct result.
