Anomaly Detection for Marketing Using Machine Learning

There is no question that machine learning is an effective and valuable asset for marketing campaigns. As more people make the move to online shopping, the need to make smart choices with your marketing campaigns grows.

Machine Learning for Marketing

The ability to target marketing efforts is one of the many benefits of a digital campaign. Regardless of the demographic breakdowns you choose, machine learning makes the process easier than ever.

There is so much information that you can obtain from your potential customers, and so much to learn from existing customers, that it is vital to understand some of the information that comes from machine learning for marketing.

Customer Attribution

Customer attribution allows you to see what areas of marketing provide the greatest return as well as learn how customers prefer to receive information. Understanding the needs and wants of your customers is one of the most important aspects of marketing.

Marketing Mix

This is where you find out what is working and what needs to improve. There is some overlap between marketing mix and customer attribution, as you will use models developed to determine which of your marketing choices provides the greatest ROI. Marketing mix also helps you assess your current marketing programs.

Targeting and Segmentation

Wondering how one specific product performs in a particular demographic? Want to know how different channels influence different demographics? Looking for smart upselling opportunities? Then you want to take a look at market targeting and segmentation.

Lifetime Value

Knowing the lifetime value of a customer helps you make sense of your marketing expenses. Machine learning can help you determine variables that you use to determine a particular customer’s value, what factors play into the decision to make additional purchases or add to an existing purchase, and what different demographics look for when making buying decisions.

Customer Churn

Keeping an existing customer is less expensive than gaining a new customer. You can use machine learning to recognize the signs of customer attrition and determine ways to reverse that decision.

Predictive Targeting for Marketing

Predictive targeting ensures that your marketing message goes to the customer base that provides you with the results you want. Think of predictive marketing as segment marketing on steroids.

When you use segment targeting for your campaigns, you build a persona that centers on your ideal customer. Trigger targeting takes that one step further; using not only the persona but identifying when the customer is ready to complete the sales cycle.

Predictive targeting uses machine learning to personalize the information you have on a customer and make a prediction for that individual about when they are ready to buy. As you can imagine, predictive marketing, when done properly, has a higher success rate than either segment or trigger marketing.

Anomaly Detection in Marketing

One factor that cannot be overlooked when creating a marketing model is anomaly detection. Anyone working in digital marketing understands the quantity of data available. Data by itself has no value, it is only in the interpretation that you find the information that you need.

Anomaly detection is the process of detecting a situation that is outside the normal pattern. The simplest way to understand that is to think of a significant data point that you are familiar with. So, for example, if you know that January is always slow, you expect it, and don’t worry when you see the numbers drop. You know you can expect them to bounce back as they have in previous years.

Machine learning allows you to do that with many different data points. The machine learning model will constantly analyze the different bits of data and detect anomalies that you can then choose to act on.

The ability to detect anomalies early on and course correct is one way that machine learning adds value to your marketing plan.

Still not clear? Let’s look at a few different ways that machine learning can detect anomalies:

• Collective Anomalies: When you review your numbers, you notice nothing out of the ordinary. Maybe a few data points are slightly lower than expected, but sales are on track. Using machine learning, you learn that the various data points combined create a pattern you need to address. By recognizing the problem early, you can make adjustments before it creates friction with your customers.
• Global Anomalies: Global, or point, anomalies, are what you often think of when you hear of problems with data. They are data points far outside of the historical norm. It pays to trace these anomalies as they often point back to fraud.
• Contextual Anomalies: Contextual anomalies allow you to make sense of particular data points. Think back to the earlier example, where your sales are low in January but bounce back up in February. However, this year, February’s numbers are only slightly better than January’s.

On the surface, it appears the numbers are better, so things are fine. However, if the February bounce is significantly less than it has been in previous years, it should be addressed. Without machine learning, it can be challenging to recognize more complex contextual anomalies.

Customer Value Forecasting

Determining the lifetime value of a particular customer is an important part of the marketing process. Customer value forecasting allows you to use machine learning processes to build an accurate value on a particular customer.

Without using machine learning, the methods of determining customer value are less refined. Historical methods calculate customer value through an aggregate model. This uses the average revenue based on past purchases. Another method is the cohort model, where customers are grouped into various cohorts, sorted by date or another value.

Forecasting customer value with machine learning allows you to make connections between behavior such as logins, cart additions, purchases, and the customer’s lifetime value.

The predictions don’t stop there. Once you use machine learning to determine the value of various customers, you can easily determine the most valuable types of customers you should target for acquisition and learn which types of existing customers you should focus more marketing efforts towards.

Machine learning provides many valuable insights for today’s marketers. Using machine learning for anomaly detection is one way to keep your marketing plan on an effective course and best utilize all of the valuable data at your fingertips.