How to Know When to Use (or not Use) Predictive Analytics?

Introduction

Predictive analytics is the use of data and statistics to predict the future. Predictive analytics is a very powerful tool that can be used for many different purposes. It can help companies find new customers, improve products or services, optimize supply chains, and much more. Here I will show you how to know when to use (or not use) predictive analytics in your business.

Predictive Analytics – a simple definition

Predictive analytics is the process of using statistics and machine learning to make predictions about the future. Predictive analytics can be used in many different ways, from helping businesses understand their customers better to identifying fraud at an ATM.

• It’s a tool that allows us to predict customer behavior so we can improve our marketing strategies and increase sales.

Why use predictive analytics?

Predictive analytics can help improve sales, customer experience and employee productivity.

• Predictive analytics helps you forecast demand for your products or services. It identifies patterns in data that indicate when customers are likely to buy something and helps you prepare for those purchases. This allows you to make sure that your inventory is adequate for these peak periods of demand so that there aren’t any gaps in supply between orders being placed by customers and when they actually receive their product or service from you (or one of your suppliers).
• Predictive analytics also lets marketers identify potential customers based on their activities online–for example: what sites they visit, what kinds of ads they respond favorably toward and whether those ads led directly back into an ecommerce experience where the user could purchase something immediately without having to fill out forms first (such as with Amazon).

When not to use predictive analytics?

• If you don’t have a lot of data:

Predictive analytics is about making predictions about the future based on past data. If you don’t have enough historical information to work with, predictive models will be less effective and accurate.

• If you don’t have the right people to work with the data:

In order for analytics projects to succeed, they need people who understand both statistics and business processes–and even then, it can be difficult! You also need someone who can communicate these findings back up into an organization’s decision-making process (or at least figure out how). This might mean hiring an expert consultant or bringing in someone from another department who has experience working with large amounts of information that could benefit from being analyzed using predictive models..

• If you don’t have a clear idea of what you want to achieve: As we discussed earlier, there are many different types of models available–from regression analysis all the way up through deep learning algorithms–so choosing one based solely on its name alone isn’t always going to yield good results once implemented! Before starting any project involving statistical modeling software such as R or SAS Enterprise Miner (which require some programming knowledge), make sure everyone involved understands why this approach makes sense given our particular circumstances so none get left behind midstream when things start getting complicated.”

Many businesses are using predictive analytics to help improve sales, but some companies should stay away from it.

Predictive analytics is a powerful tool, but it’s not for every business. If you’re wondering if predictive analytics is right for your company or project, consider these questions:

• Do you have a clear goal? Predictive analytics can help you achieve your goals by providing insights into how to reach them. For example, if one of your goals is to increase sales by 20{b863a6bd8bb7bf417a957882dff2e3099fc2d2367da3e445e0ec93769bd9401c}, then predictive analytics may be able to show how much money each marketing campaign generates and how many new customers it brings in.
• Is there enough data available? Predictive models work best with large amounts of historical data so they can learn from past experiences and make predictions based on the patterns they find.
• Are there tools available that enable easy access to this data? If not, then getting started might take more time than expected–and once again we come back around full circle: “If I don’t have time now then maybe later when things calm down…but will they ever?”

Conclusion

We hope that you now have a better understanding of predictive analytics and when it’s appropriate to use them in your business. If you want to learn more about the topic, check out our other blog posts on predictive analytics!