Adobe Sensei and Adobe Analytics
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- Data Science
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Adobe Sensei makes Adobe Analytics more intelligent, and helps marketers discover meaningful insights about their customers. This video includes additional explanation of key features in Adobe Analytics powered by Adobe Sensei, including Anomaly Detection, Contribution Analysis, Intelligent Alerts, Clustering, Segment IQ, and Propensity Modeling.
Transcript
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 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 advance analyst, to catch the important trends that they can respond to in the customer experience in real time. Adobe Sensei is a framework and set of technologies for artificial intelligence and machine learning here within Adobe. To stay relevant organizations need to utilize machine learning and artificial intelligence. This is a vanguard of where these organizations are going to find competitive advantage in the future. The Adobe Sensei powered features and Adobe Analytics comb 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 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. 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 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. 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 to find, 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 was interested in understanding if their new calculator that they built for calculating loans was having an impact on conversion. What this customer did is they used segment IQ to compare and contrast a few segments to see how this calculator actually improved business. And 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 provide. Algorithmic attribution powered by Adobe Sensei removes bias from the media optimization process and insures 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 million 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. -
Learn More about Adobe Sensei HERE.
This video is part of a playlist Analytics Fundamentals for Leaders!
Analytics
- Analytics tutorials
- Introduction to Analytics
- What is analytics
- What Can Adobe Analytics Do For Me?
- How Adobe Analysis Workspace Can Change Your Business
- It’s More Than Data. It’s Customer Intelligence
- Adobe Sensei and Adobe Analytics
- Customer Use Case - ServiceNow
- Customer Use Case - Accent Group
- Customer Use Case - The Home Depot
- Summit 2019 Super Session - Travel and Hospitality
- Summit 2019 Super Session - Retail
- Summit 2019 Super Session - High Tech
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- Apply segments to your Analysis Workspace project
- Apply ad hoc segments
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- Quick segments in Analysis Workspace
- Building Customer Journey Segments
- Building Customer Journey Segments - Part 2
- Metrics
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- Attribution IQ
- Using Cross-tab Analysis to Explore Basic Marketing Attribution
- Adding side-by-side comparisons of Attribution IQ Models
- Attribution IQ in Calculated Metrics
- Using Attribution IQ in Freeform Tables
- Using the Attribution IQ Panel
- Using different Attribution IQ models with segments
- Algorithmic Model in Attribution IQ
- Custom Look-back Windows in Attribution IQ
- Cohort Analysis
- Cohort Analysis in Analysis Workspace
- Understand your data–Cohort Tables
- Overview of Cohort Tables
- Cohort Table Settings
- Churn Analysis with Cohort Tables
- Cohort Analysis Using Any Dimension
- Latency Analysis with Cohort Tables
- Calculate Rolling Retention in Cohort Tables
- Use Cohort Analysis to Understand Customer Behavior
- Voice Analytics
- How to Manage and Track Your Voice Assistant App Data
- Understand Differences Across Voice-Enabled Devices
- Finding Opportunities To Increase Engagement for Voice Apps
- Reducing Error Rates and Improving Success Rates in Your Voice App
- Understand User Behavior on Voice Assistants
- Understanding the User’s Voice Journey
- Analysis Workspace Basics
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- Getting the Right People on Your Analytics Team
- Gaining a seat at the table
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- Translating Adobe Analytics technical language in a non-technical way
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- Are you asking the right questions?
- Admin Tips and Best Practices
- Download the implementation playbook
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- Use doPlugins and implementation plug-ins
- Configure easy download link tracking
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- Prepare Tags for your Analytics implementation
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- Publish Tags libraries to stage and production
- Using JavaScript
- Components
- Segmentation
- Segment builder overview
- Finding and creating segments
- Rolling date ranges in segments
- Segment comparison in Analysis Workspace
- Segment containers
- Segment management and sharing
- Applying segments in Analysis Workspace
- Using segments as dimensions
- Using segments to limit data
- Differences between the segment builder and quick segments
- Sequential segmentation
- Before/After sequences in sequential segmentation
- Segmentation on distinct dimension counts
- Dimension models in segmentation
- Use ‘equals any of’ in segmentation
- Analytics Insider Webinar - Customer Segmentation Strategies
- Now just wait a segment… Using segmentation to discover new insights
- Calculated Metrics
- Calculated metric builder overview
- Calculated metrics - implementation-less metrics
- Calculated metrics - segmented metrics
- Calculated metrics - functions
- Approximate count distinct function in calculated metrics
- Quick calculated metrics in Analysis Workspace
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- Use dimensions in calculated metrics
- Take your data analysis to the next level with calculated metrics
- Classifications
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- Additional Tools
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- Get started with Report Builder
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- Integrations
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- Introduction to the Adobe Advertising DSP integration
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- Create Advertising DSP alerts with Adobe Analytics
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