Anomaly detection use cases for driving conversions.
There are countless anomaly detection use cases for driving conversion rates, such as identifying fraud and reducing cart abandonment rates.
But before we dive into all of that, here’s a quick overview of anomaly detection.
What is anomaly detection?
Anomaly detection, which is also referred to as outlier analysis or outlier detection, is the process of identifying data points or events that diverge significantly from the majority of the dataset. Developers, operations teams, and other stakeholders rely on a number of anomaly detection techniques to pinpoint bugs, glitches, and rare events so that they can identify new business opportunities and drive conversion rates.
While anomaly detection is often associated with data scientists, software engineers have built tools that deploy advanced machine learning algorithms so that non-technical teams can benefit from anomaly detection techniques as well. Now more than ever before, sales teams, UX designers, and other stakeholders are turning to platforms that offer anomaly detection tools, including Quantum Metric, in order to improve the customer experience.
Most anomaly detection methods look at 1 of 3 broad categories: global outliers, contextual outliers, and collective outliers.
- Global outliers are generally single points or a small cluster of points with a very high or very low value that deviate significantly from the majority of the data.
- Contextual outliers occur when a value or data point deviates from the majority of the data set when viewed in the same context.
- Collective outliers, on the other hand, are a subset of data points that deviate from, or are anomalous to, the entire dataset.
For companies to conduct anomaly detection, they must collect customer data, measure key metrics, and perform a statistical analysis.
You might be thinking that outliers and anomalies are always a bad thing, but that’s not necessarily the case—they’re simply a deviation from what is expected. Depending on the anomaly detection use case, an anomaly can actually be a positive signal, such as a sudden surge in sales or traffic to your website.
Your team can employ anomaly detection to better understand every single aspect of your company’s business activity, especially unexpected changes in metrics such as:
- Web page views
- Daily active users
- Bounce rate
- Volume of transactions
- Cart abandonment rate
- Macro and micro conversion rates
What are some anomaly detection use cases for enterprise organizations?
There are a number of important use cases for anomaly detection, including in industries such as retail, travel, media, banking, insurance, and financial services.
Monitor KPI metrics.
Anomaly detection is a useful technique for monitoring key performance indicators (KPIs), such as conversion rates. If there is a steep decline in conversion rates or a spike in the bounce rate, anomaly detection platforms can alert teams of sudden drops in checkout success by detecting conversion blocking issues. With the help of anomaly detection tools and platforms, teams are able to find and assess major API errors, load-time glitches, server downtime, and more.
Unusually high bank transactions, email phishing schemes, and e-commerce fraudulent activity—anomaly detection software expedites the process of uncovering serious security threats.
Anomaly detection is especially helpful at catching fraud, as teams can focus on micro events, particularly those that occur in the middle of the conversion funnel and often go undetected. This way, no stone is left unturned.
One financial services company, for example, reclaimed millions in fraud losses by using anomaly detection to pinpoint suspicious cut-and-paste activity. The company accomplished this by partnering with Quantum Metric.
- With the help of Quantum Metric, the company discovered that bots, or non-human traffic, were responsible for cutting and pasting various usernames in rapid succession, much faster than is humanly possible, on the login page.
In this example, the micro behavior (filling out too many forms) did not have an obvious impact on the websites KPIs, but nonetheless could have cost the company millions and, more importantly, put its customers at serious risk.
Enhance digital products.
While anomaly detection can be used to assist teams with addressing costly errors and stopping fraudulent activity, it can also help design and development teams find user pain points or flaws in the user experience that might not be as obvious as a broken cart button or a phishing scheme. Believe it or not, analytics are especially important for UX designers.
By drawing on anomaly detection methods from a product’s launch and through its various iterations, teams engage in Continuous Product Design (CPD), a customer-defined and quantified approach to perfecting digital products. CPD allows teams to better understand their customer’s needs so that they develop a product that anticipates future problems.
With anomaly detection, your team is better equipped to evaluate the effectiveness of your latest version release, A/B tests, new features, funnel changes, approaches to customer support, and more.
Quantum Metric’s anomaly detection features.
With Quantum Metric’s machine-learning anomaly detection tools, product managers, UX designers, marketers, developers and other stakeholders can quickly and more effectively identify crucial errors in a company’s application or system.
Quantum Metric deploys machine learning algorithms to detect anomalies and send teams Experience Alerts.
The platform collects and analyzes all of the data across a company’s digital portfolio, including all platforms, operating systems, and data centers. This cross channel approach to anomaly detection looks at a company’s digital website, mobile app traffic, and any other relevant digital products. With all of that data in hand, the Continuous Product Design platform conducts a statistical analysis that uncovers importantly anomalies by examining millions of user sessions.
Quantum Metric’s enterprise-class alerting features makes it possible to configure alerts for every single metric that your company collects. Teams can filter alerts, create aggregate functions, monitor user behavior via anomaly detection, and much more. We know that every company is unique, so Quantum Metric lets teams create custom alerts for various use cases and scenarios.
Quantum Metric’s Experience Alerts integrate with tools such as Slack, Microsoft Teams, PagerDuty, ServiceNow, and others. Teams receive a customizable alert whenever the software detects anomalies.
While most anomaly detection alerting tools focus on the servers, outages, cloud performances, and other components of the system, Experience Alerts show teams what the customer is actually experiencing, from rage clicks to broken links. Quantum Metric’s anomaly detection tools make it easier for teams to comprehend the issue, locate the root cause, and determine the best course of action for resolving the problem.
Are you interested in seeing Quantum Metric’s Experience Alerts for yourself?
Request a live demo today.