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.