Five Steps Toward Machine Learning for Marketers

As a marketing analyst, you may be bombarded with information about new technologies that can assist in your job. With so many tools out there, it can be difficult to figure out which ones are useful and which ones to avoid. One technology that continues to gain steam is machine learning, providing ways for people in all roles and industries to up their game and improve their work.

In the marketing world, there’s no doubt that machine learning can be extremely helpful when it comes to analyzing and understanding how certain marketing campaigns are impacting your business. Machine learning goes above and beyond what traditional business intelligence tools can provide.

Instead of focusing on retrospective, historical data analyses, machine learning is predictive. For example, by analyzing patterns of behavior on your website, machine learning can help predict which customers will churn, enabling you to optimize offers to prevent this from happening. 

The good news is that you can gain all the benefits of machine learning without having to know Python or any other coding language. With just a few simple steps and the help of some cutting-edge technology, you can start to work more efficiently and make better, well-informed, data-driven decisions.

Step 1: Acquire Knowledge

While you don’t need an advanced degree in data science, you will need to educate yourself on the basics of machine learning, specifically on topics like data considerations, classification and regression models, and model performance metrics.

It isn’t necessary to go in-depth and learn the intricacies of how machine learning works, but you should read resources and watch tutorials to master the terminology and gain a basic understanding of the different types of algorithms and their applications. 

Once you have a general understanding of the principles involved in machine learning, you can move on to the next step.

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Step 2: Find the Right Tools

While you’re familiarizing yourself with the basics of machine learning, you’ll definitely see many references to code in use. Don’t panic! You can skip right over those parts and just focus on the big-picture concepts. This is because there are a number of low-code tools out there designed specifically for people who don’t know how to code to use machine learning. 

How do you know which tool will be right for you? Some of the key elements to look for include:

  • Easy-to-use and intuitive interface
  • Able to integrate with a variety of data sources
  • Automated model building
  • Fast results


Put simply, you are looking for a tool that will allow you to easily connect your data source, automatically build the model relevant to the questions you aim to answer, and provide you with quick results. You’ll also want it to be simple to tweak the model when adjustments are needed to get the most relevant results.

Pecan’s platform, for example, provides pre-built templates and SQL to get you the fast answers you need about everything from predicting customer churn to predicting return on ad spend, allowing you to create the most optimal marketing strategy. 

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Step 3: Create Your First Project

This is where the fun part starts. Once you’ve selected the tool you’re going to use, next decide what specific question or issue you are going to tackle using machine learning. This will, of course, depend on the current priorities of your business or team. Possibilities include:


The options are almost endless. Any issue that you and your team are currently dealing with is likely to be addressable with machine learning, helping you to make decisions based on real live data and what it can tell you about the future.

Step 4: Run the Machine Learning Process

There are a few steps necessary to get the results you need from the machine learning tool. These steps include:

  • Preparing the data: This is one of the most critical steps in the process. Selecting the right data for modeling is critical as you need to provide the tool with the relevant information it needs to answer the specific questions you’re asking. If the platform is given “bad” or incorrectly chosen data, the results will be inaccurate and unhelpful. It’s crucial to ensure that the data source is clean with no duplicates or errors. 
  • Building the model: The tool — with some input from you — will create a model for you and begin to make predictions. During this part of the process, the platform will experiment with different algorithms behind the scenes and adjust parameters until you get results you are satisfied with.
  • Evaluating the results: The purpose of this step is to determine how well the model performed, using metrics like accuracy, precision, and recall. In machine learning, different performance metrics may be used that reflect the business goal of the model. 


When a data scientist has to perform the above steps manually, it’s a long and tedious process. Yes, they have the coding and analysis skills necessary to build and evaluate a model, but by the time they get the results, market conditions may have already changed, making the information already out of date. 

When using an automated, low-code platform, on the other hand, you’ll get fast and reliable results quickly. Pecan, for example, includes an automated data preparation process, saving a lot of time during that step. You still need to select which data set to use — and as the analyst you are in the perfect position to do so, since you know your team’s data best — but you don’t have to worry about cleaning up the data manually.  And, if you aren’t satisfied with the model’s predictive performance the first time, the platform offers the agility to make adjustments and re-run the model for new results within minutes.

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Step 5: Interpret the Results

The model will provide you with predictions based on your data, and then it’s up to you to interpret those results and use them to make business decisions. If, for example, you’re using a machine learning tool to help you evaluate the early performance of ad campaigns soon after launch, the results will tell you which campaigns will provide the highest longer-term return on ad spend (ROAS). You can then use this information to allocate your budget accordingly, focusing on the campaigns, customers, and channels that will give you the best return. 

As a marketing analyst, you have deep insight into your team’s needs and goals. You know what kind of results you want to see from various marketing activities, so you’re well-positioned to look at predictions generated from machine learning and understand what they say about your marketing efforts. Instead of making educated guesses as to what steps to take next, all of your decisions will be backed up by data and knowledge of the future, giving you the confidence to move forward to greater success.

Ready to Get Started?

If you’re ready to start using machine learning to enhance your work and help you make more informed and more effective decisions, we invite you to try out Pecan. 

Start a free trial today — or contact us to set up a demo  — and learn how you can better contribute to your team’s success.

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