For those who do not already know, predictive analytics trains a computer model to learn from large amounts of data for the purpose of finding complex patterns that might be necessary for actions like inventory optimization and fraud prediction.
The employment of predictive analytics allows you to replace your gut feeling with a more data driven approach, without putting in significant extra effort.
Here are some tips that can make predictive analytics better:
Tip #1: Pick the Right Person
When it comes to predictive analytics, you should know that the results will only be as good as the person performing the job. This is the reason why you should find a creative problem-solver for the job; one who not only knows what the job is about but is open to on job learning as well.
Tip #2: Have an End in Mind
When starting off a project of predictive analytics, it is important to not get too caught up in the technicalities and have an end in mind. It is essential to remember that the basic objective of conducting any business related activity is to make money, meaning you need to know exactly how predictive analytics will help you in doing that.
Tip #3: Avoid Common Mistakes
Before starting off a model of your own, it is recommended to consult with someone who has already worked with them. This will do wonders for informing you on the common mistakes that come with predictive models and how they can be avoided.
Tip #4: Get a Lot of Data
Get as much data as possible about your historical clients, so that you might be able to create the most complete picture of your constituents. Moreover, regardless of how much data you might collect beforehand, there will always be gaps needing to be filled.
Tip #5: Don’t Hunt
It is not advisable for you to present your employee with a data set and hope for a miracle that they might go about breaking some code. Why so? It is because you need to find an area in your organization that needs optimization first.
Tip #6: The Data Driven Culture
The data-driven culture results into a lot of success. However, that is only possible when the different processes in an organization are not centered on people. Everybody in the organization needs to be on board.
Tip #7: Presentation Matters
At times, the outcome of a predictive model is the input of a larger optimization chain. Bearing this in mind, the importance of presentation and data visualization cannot be stressed upon enough.
Tip #8: Data Leakage
When designing your predictive analytics, it is essential for you to beware of data leakage. For those who don’t know, data leakage refers to the incorporation of such information about the future that you normally wouldn’t have.
Tip #9: Compete Within the Industry
You need to create the right data centric culture in your organization, as this is the only way for you to attract the best talent. Without the best talent, you will be unable to compete on your analytical capability.
Tip #10: Be Mindful of Privacy
Some customers tend to be incredibly private about their data and lives. This is the reason why you need to ensure that the knowledge about your customers will not be considered privacy infringement by the same set of customers.
+1: Try AnswerMiner
AnswerMiner’s built-in prediction model builds automatically prediction trees based on your data. Several algorithms work behind the scenes to make better prediction trees that you can use in your analysis. When it comes to presentation, automatic charts swing into action to visualize your data fast and easy.