5 Ways to Organize Your Marketing Data for Machine Learning

5 Ways to Organize Your Marketing Data for Machine Learning


Machine learning may sound like something out of a science fiction story, but it is a very real part of a successful business strategy. This is especially true in the world of marketing, where machine learning can help predict how customers will react to new campaigns or products, as well as evaluate the effectiveness of past campaigns. Getting to those answers does require a bit of prep work, however. Here are five steps that you should use to organize your datasets before starting machine learning for marketing analysis.

1. Prep All the Tools

The first step in getting ready for analysis is to make sure that you have the right tools ready to go. This starts with machine learning software and computers. There are many different software options out there, so you’ll need to do your research to find the best fit for your business. When it comes to computers, you’ll want to opt for one that has big enough chips for machine learning, as this is a very computational-heavy process.

2. Identify All the Sources

Next, you’ll want to determine which data you want to be analyzed, and where it is all coming from. This may include sales records, CRM databases, social media ad performance results, website metrics, and more. You’ll want to make a master list of where the data is stored and which specific datasets you want to use. This list is a very important reference tool for the next step, so make sure it’s accessible to everyone on your team.

3. Combine All the Data

Now that you’ve identified what data you want to use, it’s time to move it all into a central location. Machine learning programs need a central database to work from- they can’t be programmed to access multiple databases across different networks. You or your data team will need to pull the data from each source and upload it to one file location or database. This can be the most time-consuming step, so be sure to give yourself a reasonable deadline when planning out the project.

4. Remove All the Outliers

With all of the data uploaded, the next step in preparing it for analysis is to do clean the data. Parts of this may happen during the previous step, especially if incomplete data is found on the initial pulls. However, you or your team will be going much more in-depth in this step. You’ll want to look for missing or incomplete data, data that has been incorrectly entered, and outliers. Outliers are data points that are much higher or much lower than the rest of the data points in a given set (often determined by being two or more standard deviations above the norm). Outliers are also rare, and you may only find one or two or even none in your datasets. Outliers can skew your analysis, so you may want to consider removing them during the cleaning process.

5. Add All the Tags

The final step in the process is to add some descriptive language to your data. This doesn’t mean changing any of the data itself- you never want to do that. Instead, you can add descriptors such as tags to your data within the central database. This will help you see which data belongs to which set, as well as help your machine learning software parse out which data to include in the analysis. 

Machine learning doesn’t have to be a scary topic. Instead, it can be a very beneficial tool for your marketing success. Pulling all the data and preparing it for analysis requires planning for the necessary time and effort. However, it is well worth it. Machine learning can deliver data analysis that is far beyond what a human can do in the same timeframe, and may even be able to help predict future trends. Machine learning can not only save you time and money, but it could set you up for success in years to come.