What this means is that every alert you receive on your phone or in your inbox is going to be based on a real statistically significant anomaly that you should actually respond to in a metric that matters to you. So the next piece of virtual analysts is what we call “intelligent alerts.” What makes intelligent alerts so powerful is that they’re based on Adobe Sensei’s machine learning algorithms for anomaly detection. And from that I can understand what my customers want and how to give it to them more effectively. For example, I might see a massive spike in revenue and see that that’s attributable to people clicking through a certain campaign from a certain geography at a certain time of day. With contribution analysis, I can click on an anomaly and within seconds understand the factors that likely contributed to that anomaly. What happened in my data that I might not be aware of? Contribution analysis adds the why. It goes through the hundreds or even thousands of metrics that you might have in your data set and finds the statistically significant, the really meaningful changes in your data. Anomaly detection is one of the core machine learning capabilities of Adobe Sensei and Adobe Analytics. It can help find contributing factors to what those patterns are, what’s causing these anomalies. So a virtual analyst is something that we like to call “the analyst that never sleeps.” It’s a set of Sensei capabilities where you can go in and identify anomalies within your data. So whether you’re acquiring, converting, or retaining customers, Adobe Sensei is there to help you do it more effectively using the data that you’re already collecting. The Adobe Sensei powered features and Adobe Analytics comb through every dimension, every metric, and every segment that matters to you as a marketer. This is a vanguard of where these organizations are going to find competitive advantage in the future. To stay relevant organizations need to utilize machine learning and artificial intelligence. Adobe Sensei is a framework and set of technologies for artificial intelligence and machine learning here within Adobe. It can be hard, especially with the proliferation of data, for anyone, even the most advance analyst, to catch the important trends that they can respond to in the customer experience in real time. So bringing that data together in a way that’s actually useful for business users all over the organization is a real challenge. And so you have all this information that goes everywhere and brands are trying to lasso bits and tie it together to get customer profiles. ![]() ![]() ![]() When you think about all the devices that we have today, it could be our mobile phones, it could be our laptops, we could be watching something on an OTT device. TranscriptĬustomer intelligence is very tricky today because there’s a lot of great opportunity to engage customers, but there’s never been more opportunity for fragmentation. Your browser does not support the iframe element.
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