Making Informed Decisions About Experience Analytics
There’s a lot of noise in the industry about Artificial Intelligence and Machine Learning. How do you know when these technologies are actually delivering results, and when they’re just being used as trendy buzzwords? When you truly understand the mechanisms at play in customer experience analytics software, you have the knowledge to make informed decisions and make the most of the tools at your disposal.
Learning from Anomalies in Your Customer Experience Data
What do you do with anomalies in customer experience data? Do you ignore them, look into every single blip, or make smart decisions about which anomalies to follow up on and which to ignore?
You already know that not all anomalies are relevant, but finding the important ones can be challenging. It is vital to determine which anomalous activities indicate problems to solve and which are merely noise in your customer experience data. Yet, with traditional customer experience analytics, parsing out important anomalies from the unimportant ones can be extremely challenging.
Thankfully there is another way. With Artificial Intelligence and Machine Learning, relevant anomalies can be found in giant mounds of data at lightning fast speeds. And with Machine Learning, relevant anomaly detection improves over time. Let’s dive into a fresh take on some previous whitepapers from Glassbox to understand how it works.
There are five steps to using Artificial Intelligence and Machine Learning to detect anomalies in your customer experience data.
1. Collecting metrics at a large scale – although it seems counterintuitive, the more data you collect, the easier it is to find out which anomalies are relevant and which are just noise. This is because machine learning is better able to detect patterns when it has extensive, granular, detailed data to parse through. With more data, machine learning is able to make better predictions about what is important and what is not.
2. Learning how normal user behavior shows up in the data – after reviewing adequate amounts of data over time, the system can learn what normal behavior and normal patterns look like. It becomes smarter and is able to take into account seasonal patterns and even typical daily or weekly patterns. It can even adapt to changes when a new pattern emerges that has become the “new normal” so that it does not provide false alerts.
3. Learning what abnormal behavior actually looks like in the data – some anomalies are not relevant or important. After soaking up mounds of granular data and determining usual patterns, the system will get better and better at finding truly important anomalies and providing alerts only when the anomalies need follow-up. It can even make sure the alerts go to the appropriate team for follow-up based on the type of anomaly.
4. Correlating between metrics – Artificial Intelligence can make connections quickly and easily between related anomalies to understand the context and identify root causes of issues. With Machine Learning, correlations are found automatically and in real time, which would be impossible to do with traditional analytics approaches.
5. Providing feedback to the system — the system learns from feedback. Letting the system know if an alert was relevant or not helps the system to learn faster and adjust to the needs of your business.
Why do you need to detect anomalies in real time though? The answers are numerous: to resolve issues quickly and efficiently, to retain current customers, to entice potential customers to continue along the customer journey because you are able to show you care about their experience every step of the way, to become or remain a leader in your industry, and to reduce staff time spent looking through backlogs of data trying to resolve issues that cropped up ages ago.
It is much more efficient to detect and resolve an issue right away. Seemingly complex issues can turn out to be simple problems when anomalies are correlated and contextualized for each alert by Artificial Intelligence. An application crash can be tracked back to a simple 500 http error, a product that is out of stock when the customer is viewing it, or a shipping restriction when the customer tries to check out.
Even minor blips and errors can lose you customers if they are not resolved quickly and effectively. Without real time alerts of anomalies in your online customer experience analytics, you may not see the problem until it is too late. Traditional analytics are no longer enough in an age of instant gratification and customers who no longer make purchases based on brand loyalty. To stay relevant, businesses need timely alerts and smart technology, and nothing could be smarter than a learning machine with the capacity to analyze terabytes worth of data instantaneously.