Predictive analysis, when executed well, can be a powerful tool that helps your organization maximize the potential of its historical data. Although it requires an initial investment, the effort will help propel your analytics capabilities beyond performance reporting and into the realm of forecasting and machine learning.
The key to beginning the predictive analytics process is assessing the quality of your data set(s). An actionable data set needs to be robust enough to accurately describe the outcomes being forecasted and be varied in a way that prevents any resulting models from overfitting.
There’s no such thing as a perfect set of data, and even the most mature data collection teams will carry out the process of filtering, featuring engineering, and normalizing their existing data sets, also known as data cleaning, so that it’s ready to be modeled for forecasting. Many organizations will take the additional step of improving their data by appending third party demographic, contact, or financial information (data enhancement) prior to beginning to build the statistical model.
Once the data has be cleaned and enhanced as necessary, it can be used to develop a data model or statistical model. Depending on the size of the data warehouse in question, it may be beneficial to begin the model development process by carrying out initial data mining, looking for existing patterns that allow increased segmentation of your data models. These models take your historical data, combined with any additional enhancements, to make predictions relevant to your business. Factors like the a person’s demographics, their buying habits, and the time of year, can predict what they’re going to buy, how likely they are to return, and at what frequency.
The final stage of a mature predictive analytics practice is automation by way of machine learning. Generally speaking, statistics (and machine learning by extension) work better with extremely large data sets to pull samples from and run models against. That’s why you’ll commonly see predictive analytics, machine learning, and data science talked about almost interchangeably in some contexts. Although the lines are blurred, each is unique but often rely on each other to succeed.
At the highest level, machine learning is simply the automation of the statistical models you’ve developed using your big data set. Each round of predictions eventually gets a real-world result, which is then fed back into the data model to be retrained and redeployed to make your next round of predictions that much more accurate.
Businesses have more access to data than ever before. These vast data stores allow for the development of robust statistical models that provide accurate predictions on any number of outcomes, as long as their data is clean and accessible. Predictive analysis can allow you to more accurately stock your shelves, put the right product in front of an indecisive online buyer, optimize your ad spend, get in ahead of employee turnover, and much more.
Once all of the pieces are in place, there’s much to gain by incorporating predictive analysis into your business. In fact, you’ll:
From a marketing perspective, you are no longer blanketing your audience with the same message. Instead, you are targeting the proper audience by sharing your offers with the buyers who are most likely to purchase them. Therefore, your conversion rates go up, and your cost per conversion goes down. Everyone benefits.