Adobe Analytics Articles:
Building an Adobe Analytics Metrics & Dimensions definitions page with the Adobe Analytics API
Adobe Analytics Articles:
Building an Adobe Analytics Metrics & Dimensions definitions page with the Adobe Analytics API
Real estate is one of the world’s most productive businesses. According to MSCI, the value of business acquisitions worldwide will rise to $9.6 trillion in the coming years. Like many other industries, the real estate industry is switching to a “data-driven” world and is creating use cases of artificial intelligence. AI is entering the real estate business from consumer buying and selling strategies to investing in the construction of large commercial projects. However, the real estate sector remains in the early stages of deploying AI services. With the advancement of technology, AI in real estate will become apparent.
AI is increasingly being used in the real estate market to streamline and improve various processes, such as property valuations, lead generation, and property management. Here are some examples:
Overall, AI is poised to revolutionize the real estate industry in many ways, including improving the accuracy of property valuations, streamlining property management processes, and providing more efficient and effective ways for real estate agents and brokers to generate leads.
Maket leverages AI to quickly generate thousands of architectural plans instantly based on programming needs and environmental constraints to help architects, builders and developers find the best floorplan for their clients
Get interior design ideas using Artificial Intelligence and virtually stage interiors for real estate listings with different interior styles.
AI Room Planner is a great tool to generate interior design ideas. It can give you an idea of how a space could look and feel like.
Getfloorplan creates 2D, 3D floor plans and 360° virtual tours at no time. With our materials your ordinary listing can be turned into a picture of a dream house for your clients
Chat by Copy.ai is the smarter ChatGPT that helps you get more done. Scrape websites for public data, generate personalized copy, summarize YouTube videos into key bullet points, and so much more with the next generation of AI chat.
I can say without hesitation that copy.ai is a fantastic tool that can help anyone create high-quality content in no time. The platform is incredibly easy to use and provides a wide range of features that make it stand out from other content creation tools.
Whether you need to create social media posts, blog articles, or product descriptions, copy.ai can help you do it quickly and efficiently. The platform uses advanced AI algorithms to analyze your inputs and generate content that is both engaging and relevant to your target audience.
Another great feature of copy.ai is its ability to generate multiple variations of a single piece of content. This allows you to test different versions of your copy and find the one that performs the best. Additionally, the copy.ai user interface is incredibly user-friendly, making it easy for anyone to create great content regardless of their skill level.
More specifically, here are some fun copy.ai prompts to try!
Overall, I highly recommend copy.ai to anyone looking for a powerful and easy-to-use content creation tool. Whether you’re a content marketer, blogger, or social media manager, copy.ai has everything you need to create engaging and high-quality content that resonates with your audience.
In Adobe Analytics, the equivalent to record count in Adobe Experience Platform would be the “hit count” or “request count” for a specific report suite or data source. A hit or request is a unit of measurement in Adobe Analytics that represents a single user interaction with a website or mobile app.
The hit count in Adobe Analytics is the number of hits or requests recorded for a specific time period, such as a day, week, or month. This metric can be used to track the volume of traffic to a website or app and to measure the effectiveness of marketing campaigns.
To view the hit count in Adobe Analytics, you can create a custom report that includes the “hits” metric, or you can navigate to the “Traffic” report in the “Site Metrics” section of the Analytics interface. You can also use the Adobe Analytics API to programmatically retrieve hit count data.
Google Analytics 4 (GA4) is the latest version of Google’s web analytics platform. It was introduced in 2020 and is designed to provide marketers and website owners with a more complete and accurate understanding of user behavior and website performance.
GA4 is different from the previous version of Google Analytics (Universal Analytics) in a few ways.
Firstly, GA4 uses a different data model that is event-based rather than session-based. This means that GA4 is better suited for tracking interactions and behaviors across multiple devices and touchpoints.
This fundamental difference in data models has several implications for how the two platforms work. For example, GA4 is better suited for tracking user behavior across multiple devices and sessions, and provides more granular data on user interactions. It also has more advanced machine learning capabilities and predictive analytics features that allow users to gain insights into user behavior and anticipate future actions.
Secondly, GA4 has a stronger focus on privacy, with enhanced data controls and a more sophisticated approach to data collection and processing.
Another key difference between the two platforms is their approach to data privacy. GA4 includes more advanced data controls and is designed to be more privacy-friendly than previous versions of Google Analytics. For example, it includes built-in consent controls and automatically detects and excludes traffic from bots and spam.
Finally, GA4 includes new features such as machine learning-powered insights and predictive metrics, which help users better understand and anticipate user behavior.
Some examples of these features:
Overall, Google Analytics 4 represents a significant upgrade from previous versions of the platform, and provides marketers and website owners with more powerful and sophisticated tools for understanding and improving their digital experiences. Other article on Google Analytics.
Web analytics (aka: digital analytics) refers to the collection, measurement, and analysis of data about visitors and their behavior on websites. It provides insights into the number of visitors, their sources, behavior on the site, and which pages they interact with. The goal of website analytics is to improve the website’s performance and user experience.
Digital analytics and marketing analytics are related but distinct fields. Website analytics is focused on measuring and analyzing data about website traffic, user behavior, and other online metrics. Marketing analytics, on the other hand, is a broader field that encompasses web analytics, as well as data from other marketing channels, such as email, social media, and paid advertising.
Marketing analytics provides a more holistic view of a company’s marketing performance and helps marketers make data-driven decisions about their marketing strategy and budget. Digital analytics, by contrast, is more focused on website-specific data, and is typically used to optimize website performance and user experience.
In summary, digital analytics is a subset of marketing analytics, and marketing analytics is a more comprehensive approach to measuring and analyzing marketing performance.
Note: The ranking can vary depending on the specific use case and the size of the organization.
Email analytics, on the other hand, refers to the measurement and analysis of data related to email marketing campaigns. This includes metrics such as open rates, click-through rates, conversion rates, and subscriber engagement. The goal is to understand the effectiveness of email marketing efforts and make informed decisions about future campaigns.
In summary, web analytics focuses on website performance and visitor behavior, while email analytics focuses on the performance of email marketing campaigns.
Note: The ranking can vary depending on the specific use case and the size of the organization.
Outlined below is the kind of relationship managers should try to forge with employees who report to them directly. As I see it, the manager-employee relationship should emphasize clarity and trust while also prioritizing the individual worker’s needs.
I’ve managed some great people since I first started. At some point I discovered the idea of servant leadership, which for me means that leaders should center the needs of their people rather than themselves.
I ask only that my direct reports reciprocate by giving me in return what I need most: The truth.
I want them to give me their feedback, tell me when I’m wrong, and tell me their ideas for how we can do better. If we trust each other, we can learn and grow together. That’s how I want to work with my direct reports.
This is what good leadership can look like. Read the full article here. Follow Matthew Rechs @MrEchs
Robert Cialdini, considered the leading social scientist in the field of influence, was initially drawn to the topic because he saw how easily people could step over an ethical line into manipulation or even abuse. His 2001 book Influence, which laid out six principles of persuasion, was eloquent about the dangers of persuasive techniques in the wrong hands. A best-selling article he wrote for HBR the same year, “Harnessing the Science of Persuasion,” looked at the positive side of persuasion: how managers could use those principles to run their organizations more effectively.
Extensive scholarly training in the psychology of influence, together with over 30 years of research into the subject, has earned Dr. Cialdini an international reputation as an expert in the fields of persuasion, compliance, and negotiation.
His books including, Influence: Science & Practice and Influence: The Psychology of Persuasion are the results of more than 30 years of study into the reasons why people comply with requests in business settings. Worldwide, Influence has sold over 3 million copies and has been published in thirty languages. Additionally, USA Today lists Influence in their 12 Best Business Books of All Time.
Cialdini is the Regents’ Professor Emeritus of Psychology and Marketing at Arizona State University and the president of the consulting firm Influence at Work.
The earliest virtual teams were formed to facilitate innovation among top experts around the world who didn’t have time to travel. Today teams of physically dispersed employees are more often just a necessity of doing business.
There’s a lot of advice out there, but research concludes that there are four must-haves: the right team, the right leadership, the right touchpoints, and the right technology.
The best predictor of success for managers leading dispersed teams is experience doing it before. Novices can excel by practicing some key behaviors that, while also critical in face-to-face settings, must be amplified in virtual ones: Fostering trust, Encouraging open dialogue, Clarifying goals and guidelines.
Virtual teams should come together in person at certain times. The stages at which it’s most critical are Kickoff, Onboarding, Milestones.
Even top-notch virtual teams—those with the most- talented workers, the finest leadership, and frequent touchpoints—can be felled by poor technology. We recommend using team communication platforms that integrate all types of key components such as: Conference calling, Direct calling, Discussion forums, Easy recording, etc.
Virtual teams are hard to get right but the appeal of forming virtual teams is clear. Employees can manage their work and personal lives more flexibly, and they have the opportunity to interact with colleagues around the world. Companies can use the best and lowest-cost global talent and significantly reduce their real estate costs.
The good news is that research now indicates that well-managed dispersed teams can actually outperform those that share office space. So if you lead a virtual team or are a member of one, realize there are methods and tactics you can use to give your team the best chance of success.
Additional Resource:
From McKinsey.com: Revisiting Agile Teams after an Abrupt Shift to Remote
Agile teams traditionally excel when their members are co-located. Here’s a great article by McKinsey on how to ensure they’re effective after COVID-19 forced them to work remotely.
Great resource for Adobe Analytics and Google Analytics terminologies for mapping GA variables to AA variables and vice versa.
Original Post: https://yuhuisdatascienceblog.blogspot.com/2020/05/adobe-analytics-and-google-analytics.html
When going from an Adobe Analytics world to a Google Analytics world or vice versa, you’re bound to face terminologies that you’re unfamiliar with, mainly because of how both products have evolved in their functionality over time.
But never fear! Here’s a handy cheat sheet to help you know what something in Adobe Analytics is similar (but not always equivalent!) to in Google Analytics, or the other way around.
Adobe Analytics | Google Analytics | Remarks |
---|---|---|
Report Suite | Property | Besides data storage, an Adobe Analytics Report Suite provides the primary reporting interface.
A Google Analytics Property does not have a reporting interface. Instead, its reporting interface is a View. |
Virtual Report Suite | View | An Adobe Analytics Virtual Report Suite is based on applying a segment to a Report Suite.
A Google Analytics View is based on applying many filters to a Property. |
Visitor ID | Client ID | Modern implementations of Adobe Analytics rely on the Adobe Experience Cloud ID Service to provide the Visitor ID, which is a common visitor ID across all Adobe Experience Platform products. |
Unique Visitor | User | |
Visit | Session | Learn more about the measurement differences. |
Adobe Analytics | Google Analytics | Remarks |
---|---|---|
Referrer | Source when Medium is “referrer” | |
Tracking Code (Campaign) | Source / Medium / Campaign / Ad Content / Keyword | Strictly speaking, this is not comparing like-for-like.
In Adobe Analytics, Tracking Code is a special kind of Conversion Variable. It does not replace the Traffic Source. In Google Analytics, Source / Medium / Campaign / Ad Content / Keyword are synonymous with Traffic Source. |
Marketing Channel | Channel Group |
Adobe Analytics | Google Analytics | Remarks |
---|---|---|
Page | Page | |
Entry Page | Landing Page | |
Exit Page | Exit Page | |
Page Not Found | N/A | |
Server | Hostname | |
Site Section (Channel) | N/A | |
Hierarchy | N/A | Google Analytics derives a hierarchy (Content Drilldown) from Page. |
N/A | Content Group | In Adobe Analytics, a Traffic Variable can be setup to record this. |
Page View | Pageview | |
N/A | Unique Pageview | In Adobe Analytics, “Visit” can be used in place of this. |
Bounce Rate | Bounce Rate |
Adobe Analytics | Google Analytics | Remarks |
---|---|---|
Custom Link | Event Category/Action/Label | Adobe Analytics provides one field name, whereas Google Analytics provides three field names. |
Download Link | Event Category/Action/Label when used to track a download link | Adobe Analytics provides one field name, whereas Google Analytics provides three field names. |
Exit Link | Event Category/Action/Label when used to track an exit/outbound link | Adobe Analytics provides one field name, whereas Google Analytics provides three field names. |
Custom/Download/Exit Link Instance | Event | |
N/A | Unique Event | In Adobe Analytics, “Visit” can be used in place of this. |
N/A | Event Value | In Adobe Analytics, a “Numeric” Success Event can be setup to record this. |
Adobe Analytics | Google Analytics | Remarks |
---|---|---|
Traffic Variable (prop) | Custom Dimension (hit-scoped) | |
Traffic Variable (prop) | Custom Dimension (hit-scoped) | |
Traffic List Variable | N/A | |
Conversion Variable (eVar) with “Most Recent” allocation and “Hit” expiry | Custom Dimension (hit-scoped) | |
Conversion Variable (eVar) with “Most Recent” allocation and “Visit” expiry | Custom Dimension (session-scoped) | |
Conversion Variable (eVar) with “Most Recent” allocation and “Never” expiry | Custom Dimension (user-scoped) | |
Conversion Variable (eVar) with other allocations and/or other expiries | N/A | |
Merchandising Conversion Variable (eVar) with “Most Recent” allocation and “Hit” expiry | Custom Dimension (product-scoped) | |
Merchandising Conversion Variable (eVar) with other allocations and/or other expiries | N/A | |
Success Event of “Counter” type and “Record Once Per Visit” | Goal Conversion | The Adobe Analytics Success Event must be set in the tracking code.
Google Analytics’ Goals are configured in the Admin interface based on Pages, Events, Page Views per Visit or Time on Site. |
Success Event of “Counter” type | Custom Metric (hit-scoped) of “Integer” type with value “1” | |
Success Event of “Counter” type set with Products | Custom Metric (product-scoped) of “Integer” type with value “1” | |
Success Event of “Numeric” type | Custom Metric (hit-scoped) of “Integer” type with any value | |
Success Event of “Numeric” type set with Products | Custom Metric (product-scoped) of “Integer” type with any value | |
Success Event of “Currency” type | Custom Metric (hit-scoped) of “Currency” type | |
Success Event of “Currency” type set with Products | Custom Metric (product-scoped) of “Currency” type | |
N/A | Custom Metric (hit-scoped) of “Time” type | In Adobe Analytics, a “Counter” Success Event can be used in a Calculated Metric with a “Time” format to report this. |
N/A | Custom Metric (product-scoped) of “Time” type | In Adobe Analytics, a “Numeric” Success Event can be used in a Calculated Metric with a “Time” format to report this. |
Adobe Analytics | Google Analytics | Remarks |
---|---|---|
Category | Product Category | Google Analytics allows for drilldowns in Product Category (with Enhanced E-commerce only). |
Product, if product name is set | Product | Adobe Analytics does not dictate if the “Product” field should be the product’s name or SKU.
If “Product” is used to record the product name, then a Merchandising Conversion Variable can be setup to record the product SKU. |
Product, if product SKU is set | Product SKU | Adobe Analytics does not dictate if the “Product” field should be the product’s name or SKU.
If “Product” is used to record the product SKU, then a Merchandising Conversion Variable can be setup to record the product name. |
N/A | Product Brand | In Adobe Analytics, a Merchandising Conversion Variable can be setup to record this. |
N/A | Product Variant | In Adobe Analytics, a Merchandising Conversion Variable can be setup to record this. |
N/A | Product Coupon Code | In Adobe Analytics, a Merchandising Conversion Variable can be setup to record this. |
N/A | Product List View (Product Impression) | In Adobe Analytics, a “Counter” Success Event can be setup to record this. |
N/A | Product List Click (Product Click) | In Adobe Analytics, a “Counter” Success Event can be setup to record this. |
Product View | Product Detail View | |
Cart | N/A | In Google Analytics, a hit-scoped “Integer” Custom Metric can be setup to track this. |
Cart Addition | Product Add | |
Cart Removal | Product Remove | |
Cart View | N/A | In Google Analytics, a hit-scoped “Integer” Custom Metric can be setup to track this. |
Checkout | Product Checkout | |
Order | Transaction | |
Unit (when reported with Order) | Quantity | |
Unit (when reported with Product) | Product Quantity | |
Revenue (when reported with Order) | Revenue | |
Revenue (when reported with Product) | Product Revenue | |
N/A | Average Price | In Adobe Analytics, a Calculated Metric (based on Revenue / Unit) can be setup to report this. |
N/A | Tax | In Adobe Analytics, a “Currency” Success Event can be setup to record this. |
N/A | Shipping | In Adobe Analytics, a “Currency” Success Event can be setup to record this. |
N/A | Transaction ID | In Adobe Analytics, a Conversion Variable can be setup to record this. |
N/A | Affiliation | In Adobe Analytics, a Conversion Variable can be setup to record this. |
N/A | Order Coupon Code | In Adobe Analytics, a Conversion Variable can be setup to record this. |
N/A | Internal Promotion Name | In Adobe Analytics, a Conversion Variable can be setup to record this. |
N/A | Internal Promotion ID | In Adobe Analytics, a Conversion Variable can be setup to record this. |
N/A | Internal Promotion Creative | In Adobe Analytics, a Conversion Variable can be setup to record this. |
N/A | Internal Promotion Position | In Adobe Analytics, a Conversion Variable can be setup to record this. |
N/A | Internal Promotion View (Internal Promotion Impression) | In Adobe Analytics, a “Counter” Success Event can be setup to record this. |
N/A | Internal Promotion Click | In Adobe Analytics, a “Counter” Success Event can be setup to record this. |
Is this list missing something in Adobe Analytics or Google Analytics? Leave a comment to let me know, and I’ll do my best to update this list with your suggestion(s).