Using Adobe Analytics machine learning and AI for the moments that matter using-adobe-analytics-machine-learning-and-ai-for-the-moments-that-matter

Customer intelligence has never been more promising. But bringing data together that can be useful for business users across the organization — and catching the important trends in time to act on them — is not something even an advanced data science team can handle alone. Using the data you’re already collecting to your advantage is where these Adobe Sensei AI and machine learning capabilities in Adobe Analytics shine.

Customer intelligence is very tricky today, because there’s a lot of great opportunity to engage customers but there’s never been more opportunity for a fragmentation. 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, 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. So bringing that data together in a way that’s actually useful for business users all over the organization is a real challenge. It can be hard, especially with the proliferation of data, for anyone, even the most advanced analysts, to catch the important trends that they can respond to, in the customer experience, in real time. Adobe Sensei is a framework and set up technologies for artificial intelligence and machine learning here within Adobe. To stay relevant, organizations need to utilize machine learning and artificial intelligence. This avant-garde of where these organizations are gonna find competitive advantage in the future. The Adobe Sensei powered features in Adobe analytics come through every dimension, every metric, and every segment that matters to you as a marketer. 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. So 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. It can help find contributing factors to what those patterns are, what’s causing these anomalies. Anomaly detection is one of the core machine learning capabilities of Adobe Sensei and Adobe Analytics. 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. It’s great at answering the “what”; what happened in my data that I might not be aware of. Contribution analysis adds the “why”. With contribution analysis, I can click on an anomaly and within seconds understand the factors that likely contributed to that anomaly. 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. And from that, I can understand what my customers want and how to give it to them more effectively. So the next piece of virtual analyst is what we call intelligent alerts. What makes intelligent alerts so powerful is that they’re based on Adobe Sensei machine learning algorithms for anomaly detection. 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 in addition to virtual analyst, Adobe Sensei provides some really amazing capabilities when it comes to audience or segment discovery. We have a capability which we call clustering. Audience clustering is great because it uses machine learning to take all of the guess work out of that segment creation process. Once you’ve got your segments defined, Segment IQ uses machine learning to compare them across millions of dimensions, metrics, and even other segments. What it’s doing is looking across all of these behaviors and traits to find the similarities and differences between these segments. And the result is understanding for the marketer about what these segments are looking for and how best to engage with them across channels. For example, we had a customer who’s interested in understanding if their new calculator that they built for calculating loans was having an impact on conversion. So what this customer did is they used Segment IQ to compare and contrast a few segments to see how this calculator actually improved business. They found that the calculator actually improved conversion by 4%. So that led to further hypotheses and comparing and contrasting segments and it helped them actually place the calculator in top portions of their website, which ultimately led to not just a 4% conversion overall, but over a 10% conversion lift on their loan application process. Now another great Sensei powered capability that we have in Adobe Analytics is the ability to do propensity modeling and scoring. Any marketer would love to understand how likely it is that a given customer will engage or convert. This is exactly what propensity scoring does. It applies machine learning to your data set. You can actually assign scores and model out, next best actions that you can take with your customers. That can actually be facilitated through the Sensei capabilities and propensity modeling that we have provided. Algorithmic attribution powered by Adobe Sensei removes bias from the media optimization process and ensures that your brand is always achieving maximum ROI from your media spent. What Adobe Sensei provides to marketers and to analysts, is the ability to surface meaningful insights in the moments that matter to people and it’s hard enough to understand a single customer, let alone millions possibly at scale. You can discover the unknown unknowns if you will, you can surface insights and make sure that you don’t miss opportunities or even threats. To understand your customers as people, that’s the promise of Adobe Sensei.