Smart Data Series: An Introduction to Predictive Analysis

Last week, we started our Smart Data Series. We gave you an introduction to data science and business intelligence. Well, you can't talk about data science without talking about predictive analysis. So let's dive right in. Have you ever wished you could predict how much a customer would purchase in the future? Or maybe you wish you could figure out what advertisement to place on what publication? Predictive analytics may sound intimidating, but it's really just a fancy term that means listening to data to make smarter business decisions. Even if you're already utilizing big data in some aspects of your business, you may be surprised to learn what else you can do with it. According to a VentureBeat report, "73 percent of marketing analytics reporting time is spent on evaluating the past and the present, while only 27 percent is spent on predicting and influencing the future." This marks a huge, untapped opportunity for improvement among the majority of businesses today.

How It Works

If you've ever studied probability, you already have a rudimentary understanding of how predictive analysis works. These analytics tools collect data, analyze that data and construct models to predict the likelihood of certain events. Then, you are given access to this information—typically via spreadsheet—without having to trudge through the difficult calculations yourself. Other predictive analytics tools display data through integrated development environments or workflow models. While predictive analytics tools were limited by data access issues in the past, advancements in technology are slowly bridging the gap and providing businesses with better resources. Relational databases are now used most often, as they allow for easier reorganization of data. Some predictive analytics tools now operate from the cloud as well, giving businesses a chance to scale comfortably as their data size grows.

What You'll Need

While your needs will depend largely on the platform you choose, there are a few things to consider and prepare for. First, you'll want to determine your data sources—where your data will come from. Businesses typically use up to five data sources from which predictions can be generated. There are also unique languages with which your results can be interpreted. These include SQL, R, Java and more. Ultimately, the most important factor to determine ahead of time is what areas you'd like to improve the most. Is your digital marketing strategy in need of a serious boost? Do you have only a minimal understanding of your customers' needs and desires? These questions will help you determine where you need additional knowledge and which data sources would be appropriate to use.

Opportunities Are Limitless

When considering how predictive analysis can benefit your business, the possibilities are virtually endless. There are several common ways predictive analysis is already being used in modern business—for example, calculating customer lifetime value (CLTV), or the total predicted profit from a single customer. Another example is recommended product sections, which feature a personalized list of items one customer might be interested in (Amazon famously utilizes this tool). Yet another incredibly helpful and common calculation is customer retention, which can empower businesses to target subsections of customers with tailored offers. Some businesses employ analytics primarily for marketing and sales guidance. Data that separates customers based on their interactions with your website can reveal your best-bet leads—those you'll want to reach out to ASAP. Some businesses have been able to cut down their budget, generating quality leads with less effort thanks to these tools. Regardless of your reasons for employing predictive analytics, you can expect to increase efficiency, save money or streamline processes—or even all three.
If you want to learn more about how predictive analysis can grow your business, contact us today or visit our website.

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