Anomaly detection, also referred to as outlier detection, encompasses the process of identifying uncommon, non-typical, or irregular patterns within a dataset. These anomalies can represent data points that significantly deviate from normal or expected behavior, potentially indicating problems, fraud, errors, or exceptional events.
In the realm of digital marketing, anomaly detection proves to be a valuable technique for spotting unusual behavior in critical metrics and data. For instance, it can be employed to identify unexpected surges or drops in website traffic, notable fluctuations in conversion rates, suspicious patterns in transactions or interactions, and more.
Various approaches and algorithms are utilized for anomaly detection, including statistical methods, machine learning, time series analysis, cluster analysis, among others. These approaches aim to identify outliers or anomalous patterns by measuring deviations from anticipated behavior or by identifying unusual patterns inherent in the dataset itself.
Anomaly detection within the domain of digital marketing yields valuable insights for campaign optimization, troubleshooting technical issues, detecting fraudulent or suspicious activities, and generally enhancing the performance and efficacy of digital strategies.
The Sales Glossary is a compendium of all the most commonly used terminology in sales strategy. Many of the concepts listed here are used when implementing a CRM system or a digital sales funnel, no matter if they are legacy systems or an online CRM. See also our blog that deals with sales techniques, marketing and sales culture.