Product Management Insight

Adobe Experience Platform Web SDK is intended to replace the existing libraries across our various Adobe Experience Cloud solutions such as appMeasurement.js, at.js, dil.js, and visitorapi.js. The important difference is that a library was written from the ground up leveraging an updated schema via Adobe Experience Platform Experience Data Model (XDM) to map and collect data agnostic of the solution the data is pushed into for further processing. I coauthored this post on Adobe's Medium Tech blog to explain how to route data to Adobe Analytics through the newly launched Adobe Experience Platform Web SDK. Here's a link to it.

Adobe Experience Platform Web SDK consolidates solution-specific requests into a single payload for a seamless and much more simplified implementation. I coauthored this post on Adobe's Medium Tech blog as part of a series of blogs we're going to write about migrating existing Adobe implementations to the newly launched Adobe Experience Platform Web SDK. Here's a link to it.

I've been a data analyst in the past and one thing I can say for sure is that we don't have to be great analysts or statisticians to be able to read basics graphs and understand trends. Visuals are all around us whether it's stock market trends or data around the dreaded Covid-19 pandemic. By now, I'm sure all of us have heard about "flattening the curve". It literally took a pandemic for us to know what it means but the point I'm trying to make is that we are surrounded with data and people should ideally know how to understand it. I recently learnt the basics of R, which is a programming language mostly used in data analytics, statistical analysis and visualization. R is a good language to learn for data analysts and statisticians which resonates really well with professions who know SQL. 

In this post, I'll visualize traffic coming to my blog since 2017 (data captured in Google Analytics) and show some commonly used graphs and visualizations using R Studio. The most obvious trend you'll see is traffic started gradually increasing on my blog since I started writing again in January-18 and has really spiked in the last few months. So, let's dive in!

Basics of R

As I explained earlier, R is a programming language used to analyze existing trends and also do predictive analytics using statistics. For the purposes of this article, I'm using R Studio to run basic R commands to create simple visuals such as bar graphs, line graphs and slightly more complex visuals such as bubble charts, word cloud and a map using some commonly used packages. 

Data Frame

R packages are reusable code libraries that provide additional functionality to R and help simplify tasks. You can install packages using the install.packages() function and invoke them using the library() function. In my case, I'm using the following packages:

• library(ggplot2)
• library(lubridate)
• library(ggwordcloud)
• library(maps)
• library(dplyr) 

Finally, before we take a look at the visuals, one other thing to note is that I did some basic data manipulation in R to convert the Month and Year using the lubridate library by using the ymd() and mdy()  functions. There are a lot other things that I can cover under basics but that's outside the scope of this post.

Visualizing Blog Traffic Trends

In this section, I've inserted the graphs created in R Studio which were saved as images. I've used the "ggplot2" package which is a very popular R library to visualize data. I'll admit that my blog does not get a ton of traffic but that is not my intent as I'm not into any competition to artificially inflate my traffic. My intent is to share what I know with others and document things for myself for future reference. Let's take a look at some of the trends.

Show Visits and Page Views for the Last 3 Years (Line)

In this line graph, I've visualized Visits (called Sessions in Google Analytics) and Page views for the last 3 years. 

• If you notice closely and look at the label I manually added, traffic started gradually increasing once I started writing again in early 2018. 
• The biggest spike happened Thanks to my last post about Real-Time CDP which I wrote last month. This shows how much my readers want to consume content about the latest and greatest technology from Adobe and especially if it's around Adobe Experience Platform.

I mentioned earlier that it's very common for analysts to modify the source data before bringing in the data in R which is what I did to generalize the page names by removing the month and year using the mid() function in Excel.




As promised, here's the code sample to generate this visual. Please note that I created a separate data frame called 'dfl' which contained Month, Visits and Page views. Also, note that the file format is .rmd which is a format used to visualize R commands. 

Top 10 Pages Visited from Jan-2017-Apr-2020 (Flipped Bar Graph)

In this bar graph, I'm showing the top 10 page Visits by Page in the last 3 years. 

• The most popular page is the one I wrote to show the calculation of funnel drop off rate way back when I started blogging. This shows that there's still a sizable audience looking for calculation of basic metrics such as drop off rate as the traffic source of this page is primarily search engine.
• The other popular pages is the homepage which may mean that people get sent directly to my homepage via search. Again, this is inclusive of the last 3 years worth of data so more analysis is needed to understand this fully which is outside the scope of this post.

Below is the code I wrote.

Top 10 Pages by Visits and Bounce Rate from Jan-2017-Apr-2020 (Bubble Chart)

Top Traffic Sources from Jan-2017-April-2020 (Stacked Area Chart)

In this chart, I visualize the top traffic source sending traffic to my blog for the last 3 years.

• Organic search has traditionally been the top traffic source for my blog but the interesting thing is that a lot of visitors come directly to my blog by typing the URL which is very surprising to me unless they bookmark it.
• Traffic via Social channels started appearing since I started sharing my blog posts on LinkedIn and Twitter from early 2018 which explains the trend.

Below is the code snippet.

Age and Gender Data Captured since late 2018 (Pie Chart and Bar Graph)

Now, this might be a bit surprising for some Adobe Analytics users to understand how Google Analytics captures demographic data. This is done by enabling the Demographics and Interests reports  in Google Analytics which uses data collected from IDFA and Google advertising cookies to help in retargeting. Again, none of this data shown here is even borderline PII so Google has taken into consideration all privacy regulations. It might be a good addition for Adobe Analytics if it can receive similar data from the Adobe Ad Cloud platform.




There's not much to say here as these graphs are self-explanatory but I manually added the percentage (total is ~5500 Visitors) to show the breakdown of Gender in the Pie chart. The code snippet is show below.

Word Cloud showing Internal Search Terms since early 2019

• Given that I've written extensively about Adobe Audience Manager, it's not surprising that a lot of the search terms are tied to AAM.
• This also tells me what else I can write about based on what people are searching for.

Finally, this visual created using the "maps" and "viridisLite" (already available in R) packages shows which country is the most popular in terms of the total number of Visitors. As shown below, United States is the most popular followed up by India which are the two countries I'm associated with so I'm not surprised :)



Below is the code snippet for this.

Adobe Experience Platform Data Science Workspace

So that was it! Hope with this post, I piqued your curiosity about R and its data visualization capabilities.

Adobe I/O is a one-stop shop for developers, giving them everything they need to extend the capabilities of the Adobe tech ecosystem. I coauthored a post where we took a look under the hood at our I/O architecture, components, and all the dev tools and services available on I/O Runtime (Adobe's serverless platform), plus the different Adobe Experience Cloud use cases that I/O makes possible. Here's a link to Adobe's Medium Tech blog post I coauthored and below is the visual of the architecture.

Companies are continuously innovating and coming up with new tools and technologies to enhance the experience of its customers. Optimizing and personalizing the experience of customers at every touchpoint needs to be the holy grail for all companies. I gave an overview of a Data Management Platform a few years ago and have written a few article about Adobe Audience Manager since then. 

In this post, I'll write about Real-Time Customer Data Platform which is the activation engine and a service built on Adobe Experience Platform and how it solves the ever evolving challenge of a consistent and personalized user experience. As you may be aware, Adobe Experience Platform is built with an API-first approach which makes integrating with other platforms easier and it also allows us to replicate what we can do in the UI via APIs as well.

It's always helpful to start simple so I'll give a brief overview of what a Customer Data Platform is. There's a lot of documentation which compares a CDP to a DMP so I won't go into that in detail but will just focus on what a CDP is.

According to Wikipedia, "A customer data platform is a type of packaged software which creates a persistent, unified customer database that is accessible to other systems. Data is pulled from multiple sources, cleaned and combined to create a single customer profile. This structured data is then made available to other marketing systems".

I define it as a system which consolidates known and anonymous customer information from multiple data sources to serve 1:1 real-time personalized experiences across multiple touch points and marketing channels in a privacy aware manner. Below is a visual overview of a CDP (taken from a 3rd party site). The diagram looks very similar to a DMP but a CDP differs with a DMP in the following ways:

• The activation of both known (PII) and anonymous user data is possible in a CDP
• Profiles have a much longer data retention period in a CDP
• A complete customer profile (historical, demographic) is maintained in a CDP

Core Elements of Real-Time CDP

• XDM Schema & Datasets: The XDM Schema describes the logical model of all data coming into Adobe Experience Platform and contains information about the identities used to build the id graph. Essentially, any data that will be activated by Real-Time CDP has to be mapped to the XDM schema format. Datasets are tied to a schema and collect data either sent either via streaming or batch format from various sources (E.g. Audience Manager, Analytics, 3rd party sources) into AEP. 

• Real-time Customer Profile: The Real-time Customer Profile is a combined view of all customer identities collected across multiple channels. I recently wrote about the device graph which creates a holistic view of the customer across multiple touch points. Real-Time CDP taps into the device/id graph to create a unified view of both the known and unknown customer profile on the fly depending on the use case.

• Real-Time Web Data Streaming: The introduction of the AEP Web SDK has changed the dynamics of how Adobe data collection will happen moving forward. This essentially allows clients to send data directly to AEP in real-time as well as to Analytics, Audience Manager and Target from our website or mobile app. A lot of new content is being created on this which I'll share soon.

• Real-Time Activation of Profiles: Real-Time CDP allows us to send segments and PII profiles to external destinations quickly keeping in mind the rapid pace at how customers can interact with brands across multiple channels. 

• Built With Privacy By Design: All Adobe products including Real-Time CDP is built keeping privacy in mind. In case of the AEP UI, customers can use Data Usage Labeling and Enforcement (DULE) labels to mark mark identities or restrict how data will be activated based on GDPR and other privacy laws. The official Adobe documentation provides more details on this.

A lot more information can be found in the official Adobe documentation in addition to what I've covered.

Adobe Solution Integration with Real-Time CDP

Real-Time CDP UI Overview

I'll now provide a high level overview of the Real-Time CDP UI but please be aware that these are likely to change as product is continuously making changes to optimize the user experience and add more enhancements.

Profiles

The identity page contains identity namespaces which contain unique information about a person. As shown below, an identity namespace can be Email, address Phone number, ECID etc. This is also embedded within the Adobe Experience Platform UI.

Sources

Destinations

The Destinations->Catalog tab allows us to activate the audiences and share them with ESPs, marketing automation and advertising platforms. This is the core feature of Real-Time CDP which you won't see present in the regular AEP UI. There are two ways to send data via destinations:

• Profile Export which allows us to export PII data to ESPs and other platforms. 
• Segment Export (anonymous) which allows us to export profiles which qualified for a segment to Demand Side Platforms and is similar to how Audience Manager exports data to DSPs. 

This UI as shown below shows us the standard connections but also lets us connect to Launch which allows us to forward raw signals captured in Launch using extensions to any 3rd party partner.



The following is a visual (representational) system view which shows how many profiles are connected to sources and are sent to a destination.



Finally, I want to emphasize that I've not heard anything about Adobe Audience Manager being impacted by Real-Time CDP as we still need AAM for 2nd and 3rd party data activation. Also, Audience Manager has over 100 destinations whereas Real-Time CDP only has a few (currently) with connections to CRM, cloud providers and email systems but the list will only grow. I don't know about the overall product vision around the total numbers destinations and AAM in general but I'm sure our product team will clarify that.

So, that was it! I'm sure I've left out other finer details about Real-Time CDP but this tool is continuing to evolve and will only get better as its adoption increases.

We are surrounded by all kinds of (regular and smart) devices. It's either a Laptop, Tablet, Smart Phone, Smart Watch, Smart TV, Smart Vacuum, Smart Plug, Nest Thermostat, Wifi Camera or Chromecast to name a few. However, not all of these devices are truly connected due to many factors with one being that not all devices may belong to one single company in a household but the most obvious one is the lack of a true user "Identity". The other issue we face is that it's really difficult to track users and attribute an action across all these devices consistently. Google does this well with Google Home where it's able to sync my Nest thermostat with Chromecast using my Gmail ID. The other company is obviously Adobe ;)

By 2030, Americans will own as many as 15 connected devices. While we're not fully there yet, the introduction of new devices every year might expedite that trend. In 2016, Adobe's coined the phrase "Devices don't buy products, people do" which is still very relevant in 2020 and will be in the future. So how does Adobe establish the link between multiple devices to one person? In this post, I'll provide an overview of the various ways by which Adobe is able to link multiple devices to a person or household.

Concept of a Device Graph

Private Device Graph

The Private Graph is another way for a company to link their customer devices in a device graph but visible and accessible only for their own organization and not any other company. 

Using the previous example, if we just look at the travel website alone we can treat it as a participant of the private graph as the data will simply be available within the context of that company. Private Device graph primarily makes use of the deterministic link as it works best if encrypted user ids are captured. Probabilistic matching will also be available to Private Graph sometime this year. 

Below are some use cases of the Private Device Graph:

• If you want to reconcile user identities across multiple devices into one.
• If you want to perform frequency capping across devices thereby optimizing ad spend.
• If you want to establish a common identity across online and offline channels.

In order to participate for the Private Graph, the customer must be on Analytics Ultimate or have Audience Manager or have Target Premium and also be an Adobe Experience Platform customer. If you qualify for this as a client, subscribing to this should be a no-brainer for you given that no privacy policy updates need to take place. However, Device Co-op does give you access to a much larger amount of users which you wouldn't get with a Private Graph so you'll have to weigh your options on which one to choose.

Device Graph and Adobe Experience Cloud Solutions

Adobe Analytics

The other service you can leverage (I've not used it yet) is Cross Device Analytics (CDA) which allows you to analyze cross device behavior in Analysis Workspace using Adobe Analytics data (needs a cross device report suite). This is not a default service and all the details and eligibility requirements are included in this Adobe Spark page.

Audience Manager

Web Browser Cookies have long been the lifeblood of the digital marketing ecosystem. A cookie is a tiny text file used by a web browser that typically captures non-intrusive information about a browser (indirectly a user) such as logged in state, user preferences and anonymized or encrypted ids etc. A cookie allows companies to measure browsing patterns (pages visited, products bought), remembering products added to a shopping cart or simply personalizing user experiences that match previous patterns and behaviors. 

As of December 2019, Google Chrome was the most popular web browser with a market share of 69%. Google recently released their newest Chrome browser version 80 which introduced clear guidelines around how cookies need to be set moving forward keeping in mind privacy regulations such as GDPR and CCPA. As an example, Safari now completely blocks 3rd party cookies from being set. Given that Google has advertising platforms which primarily leverage 3rd party cookies, there are still a few years left before Google completely phases out 3rd party cookies by 2022 which means we don't have a lot of time left. So, what does it mean for companies like Adobe which also rely on cookies for measuring user behavior using 1st party cookies and activating anonymous profiles on publishers using 3rd party cookies in the interim?

In this post, I will review changes made by the Adobe Identity Service team to address cookie setting requirements in Google Chrome 80.  I will refer to this article written by the Adobe Identity service team and validate changes made by Adobe primarily around the Experience Cloud ID Service cookies. So let's dive in!

What is all the Fuss About?

In the next two sections, I'll do a quick pre/post comparison between Chrome 79 and Chrome 80 and review the Experience Cloud demdex cookie set in both versions.

The World Before Google Chrome 80

The World After Google Chrome 80

What is the Recommendation for 1st Party Cookies on Safari?