Sunday, March 31, 2019

My Experience at The Adobe Summit 2019

I attended Adobe Summit earlier this week held at the Venetian hotel in Las Vegas and walked ~72,000 steps in 6 days (Saturday to Thursday). In these six days, I only went out of the hotel twice which in a way is shocking but that's because the Venetian hotel is a city in itself and it also goes to show how busy I was. This was my second Adobe Summit and my first as an Adobe employee but the important thing to call out is that attending Summit as an Adobe employee is completely opposite than that of attending as a client or partner. Simply put, you don't get to attend Summit (as a consultant) unless you're presenting or doing a lab which I personally think makes a ton of sense and I want to especially thank the Adobe leadership team who gave me an opportunity.

I did a lot of things at Summit and want to summarize all I did but the one I'm most proud of is the speaking session with Garrett Friedrichsen who's my client from Mastercard with whom we presented to over 200 people. Let's take a look at some of the things I did:

Teaching Assistant in pre-conference trainings- The pre-conference trainings happen right before Summit kicks off so I landed in Las Vegas on Saturday instead of Monday as the actual conference began on Tuesday. My initial TA duty was supposed to be on Audience Manager fundamentals but I ended up swapping to cover basics of Launch by Adobe at the very last minute. Even though it was very tiring due a commitment of eight full hours, it was still a great experience for me as I proactively helped the instructor and for one of the lesson, took over on his request. The most satisfying thing for me was being able to help the students and answering ad-hoc questions about their implementation. The other good thing about this was that I got a feel for how to face an audience of over 50 people.

Adobe Booth duty- This was one of the highlights for me as this is where I got to catch up with my rockstar co-workers and the leadership team in person. In addition to this, I answered ad-hoc questions from clients and partners primarily on Analytics and Audience Manager.

Ad-hoc Customer meeting- This happened as one of the account teams reached out to me and asked me to meet their client (not mine) to discuss my experience working with clients who incorporate agile methodologies in their analytics implementation. The conversation went well as the customer and I ended up addressing a lot of general challenges and potential solutions around agile, implementation challenges and enablement of resources within an organization. We have some next steps and the account team will work on executing those with the client.

Teaching Assistant in Labs (Video Heartbeat and Adobe Analytics)- Personally, I didn't add much value here as my main role was to work with other TAs to help attendees/students with their preexisting lab exercises and computers. All in all, it was a good experience to see how other instructors go about with their lessons.

Co-Speaking Session on Adobe Audience Manager- This was easily the most challenging and satisfying part of Summit for me. I wouldn't lie but I was quite nervous before the presentation as this was the biggest group of people (235 people attended) I had ever presented in front of (previous was probably 20). My session (Audience Driven Campaigns in Financial Services, Featuring Mastercard) started with an icebreaker but I did speak fast at times which is something I'll work on. As I got to presenting my core slides, my flow got much better as I knew the most about the subject. Overall, the presentation went well as the silver lining was that people came up to both Garrett and I to congratulate us but most importantly asked questions and engaged with us after the session. Having presented for the first time to a big audience, I would like to share some tips in case you want to do it as well:

Rehearse but not so much that you end up cramming the content
Get to the conference room 15-20 minutes early to prep and get set up
Have an ice breaker at the start (mine was to wish the Japanese audience Konnichiwa and also wish my co-speaker Happy birthday)
Try to include a relevant analogy for your presentation but be 100% sure that it's relevant and not overdone. We didn't include one but will probably do so next time
Speak slowly and take deep breathes in between
Stay hydrated (very important)
Gauge the audience reaction and mood to adjust if necessary. Sometimes, the audience in the room already knows a lot of what you're saying so it may make sense to go deep but there's a fine line here so be careful.
Make enough time available for questions and engage with the attendees after your session is over
Don't get intimidated and overawed by the situation. It's important to remember that the attendees are people just like you and they're attending to learn from you so relax and enjoy the moment
Finally, push yourself out of your comfort zone and take the plunge to speak. Trust me, if do it once you would want to do it again and again!

Got a demo of the Adobe Experience Platform- I feel the Experience platform is a game changer for Adobe and it will elevate Adobe to a whole new level. The real-time platform allows us to ingest any data (including PII), control privacy, query data, visualize data, leverage machine learning and AI at scale to name a few. Here's a link to it and I'll hopefully write about it as I learn more.

Killers Concert and Wrap up- Adobe summit wrapped up and did justice to the phrase "Work hard and play harder". Honestly, it was hard to enjoy myself before my speaking engagement but after I was done, it was the best feeling not just because it was over but because I felt the hard work paid off which is why I didn't miss any opportunity to enjoy myself at the concert.

All in all, it was a wonderful opportunity and I hope to come back in some capacity next year and continue to add value for the company. I've added some pictures from the conference.

The amazingly beautiful Venetian hotel

An awesome surprise for all AAM speakers from our track manager, Rakhi Patel

Dinner with client

In one of the labs

My presentation

Working our magic at the booth

Awesome Adobe booth crew and my coworkers

The Killers performing on stage!

This happened outside right after the concert where the DJ played some awesome music. It was completely unexpected and a ton of fun!

Sunday, March 10, 2019

Mobile App Marketing with Adobe Audience Manager

80% of smartphone users are more likely purchase from companies who have a mobile app (Google, 2018) and up to 70% of all Internet activity happens on a mobile device (Ciodive, Nov 2017). These are some of the many statistics that remind us about the importance and relevance of consumer behavior on mobile apps. This also means that companies who have mobile apps need to invest in marketing their apps. This post will cover how can customers leverage Adobe Audience Manager to share their audiences with Demand Side Platforms that deal with mobile publisher networks for two use cases which are Retargeting and Suppression.

Mobile App Device IDs and Advertising

The first thing I want to cover is the lack of a UUID (cookie ID) on mobile apps. Mobile Apps have their own device ids that are primarily used for advertising purposes. Apple iOS has IDFA (identifier for advertisers) and Google has the (Google ID) and this is the ID that gets sent to Audience Manager to integration purposes.

Mobile Advertising is primarily of two types:

In-App and Push Messaging: This is messaging and "advertising" within your own app to bring back users to your app once they've downloaded the application. The Adobe documentation covers this in a lot more detail.
External App Messaging: This type of advertising happens on other apps where you see ads of apps that you either have or don't have (see example below). This is what I will cover in this blog post.

Source: mobileads.com

Mobile App Device ID Sync

I recently wrote about the newly launched Adobe Experience Platform SDK which shows you how to deploy it using Launch by Adobe. I suggest that you deploy the new Adobe SDK on your App if you're starting new. For this post, I'll focus on SDK4.0 given that it's still the most popular SDK being used by our clients.

Assuming that you have the Adobe SDK already setup on either your iOS or Android Apps and Audience Manager deployed, we need to make sure you send the respective device advertising ID for iOS and Android to the SDK which enables advertising on your apps for Audience Manager. The methods are outlined here:

iOS: setAdvertisingIdentifier (More information here)
Android: submitAdvertisingIdentifierTask (More information here)

Once you deploy the device ID sync, you should expect to see the following output in a packet sniffer for your iOS app as an example:

http://dpm.demdex.net/id?d_ver=2&d_orgid=F7AC877056DFF0E87F000101@AdobeOrg&d_mid=68452733562309787373599334388622514953&d_blob=cIBAx_aQzFEHcPoEv0GwcQ&dcs_region=3&d_cid_ic=DSID_20915%01591B1212-F56B-4B8F-B026-A2C8D90323F1%011

Please note that 20915 a unique identifier for iOS and needs to be used in case you want to upload any records for specific device IDs to AAM.

Validate Mobile Apps Signals in Audience Manager

The first step is to validate incoming mobile app signals in AAM which you can do by looking for 'c_contextData.a.Launches' to confirm if a mobile app signal is coming into AAM. This attribute should show up only if you have a mobile app forwarding data to AAM.

Create Mobile App Traits in Audience Manager

Once mobile app signals are identified, we need to create a mobile app specific data source by selecting 'Device Advertising ID' as the type. Both iOS and Android app specific traits will be created based on this setting.

The next step is to create traits tied to our specific use cases of Retargeting and Suppression.

Retargeting Trait

The following example is to setup a trait for retargeting users who added to cart but did not purchase. The numbers in the trait builder are evaluated as strings so we have to be careful with the logic. The other thing to note is that you will have to send in specific context data variables which map to various events in the checkout flow.

Exclusion Trait (Suppression using Segments)

Suppressing an audience simply means that we want to suppress certain audiences who've either purchased a product or in my case, I'm showing an example of users who have launched the app who will be suppressed using an AAM segment. One other thing to note is the you can create these traits in all lowercase.

Map to Segments and Activate

Once retargeting and exclusion traits have been created, map these to segments. This particular example excludes users by leveraging instant cross device suppression using the "AND NOT" logic. You can also suppress the audience at the DSP level but my recommendation is to exclude it in AAM.

Finally, map segments to AAM destinations for activation. A few examples of Demand Side Platforms that work for mobile apps are AppNexus and DataXu. Criteo is another company that specializes in mobile app retargeting.

So that's it! I hope that gave you some insight on how to execute media campaigns on mobile apps with the sole goal of maximizing your return on ad spend.

Sunday, February 3, 2019

DTM to Launch Migration Tool Overview

There's a lot of buzz currently about upgrading from Dynamic Tag Manager to Launch by Adobe and there's a ton of good materials out there to help us plan and execute the migration successfully. Right off the bat, the three part series from 33Sticks comes to mind which I highly recommend you to read before this. My post will cover Adobe's migration tool which in my opinion provides a rather seamless method for upgrading to Launch. I'll share my experience with it and supplement the information that the Launch product team has already provided. I'm also not trying to provide a complete roadmap to migrate as there's already great content for it but I'm simply giving my opinion on this handy utility. Let's get started!

Overview

The migration tool is basically accessed by clicking the 'Upgrade to Launch' (highlighted in Red) button in DTM as shown below.

When you click this button, a popup will appear that shows you two check boxes and an 'Upgrade' button. The first checkbox allows you to use the same DTM embed code with Launch post migration. The second checkbox disables the existing property in DTM.

Once you click 'Upgrade', you'll see a brief message stating that the property is being migrated. Once that goes away, you'll see a link which will take you directly to the newly migrated Launch property. The new property in Launch contains the timestamp in its name as shown.

An important point to note is that only items that have been published in DTM will get migrated and anything that's unapproved in DTM will not get migrated to Launch. Here's a link from Adobe's documentation that covers what all will be migrated (Adobe Analytics, ECID, rules and data elements).

In case you want to migrate any additional rules that weren't previously migrated during the first round, you can migrate the property from DTM again. In my case, I forgot to publish a few rules in DTM that weren't migrated before so I ended up migrating the property again which added a second property in Launch with a different timestamp. Personally, I would recommend that you migrate just once and publish everything in DTM beforehand.

As far as the (migrated) rules are concerned, Launch creates a new rule to fire off an Analytics beacon (see below) in addition to the ones that get migrated.

Now that we've seen what the migration tool does, I want to share how I would approach a migration with this tool.

My Migration Approach

Before I begin, I want to share that I've upgraded a few clients using this tool as well as migrated clients by creating a brand new property in Launch. There's no hard and fast rule around which method to choose as both options will get you the desired results but if you have a very complicated Analytics set, the migration tool might be a better solution. Let's go through what my approach would be with the migration tool.

Treat this migration as an opportunity to do a cleanup of unnecessary data elements and rules and validate your data layer and tags in general.
Create a thorough launch schedule/plan to QA your staging environment and sure all tags are firing as expected. There's a lot of good material out there on how to build use cases and leverage QA tools & extensions to perform validation but that's a separate post for some other day but give this step the utmost importance.
Your launch plan needs to include a timeline of how long your tags will be tested in staging and when can they be pushed to production. I would typically give it about a month (depending on the complexity of your site) from when you begin testing in staging to when you can deploy Launch in production.
Make sure to publish all rules, data elements and tools in DTM that you want to migrate to Launch. As mentioned earlier, only Adobe Analytics and Experience Cloud ID Service tools will be migrated so other solutions such as Target or Ad Cloud will have to be created separately in Launch.
Click the 'Upgrade to Launch' button and do not select any of the checkboxes in the popup. The reason why I wouldn't select the DTM embed code option is because it's recommended to deploy Launch asynchronously which doesn't require the legacy pageBottom() call. As this call will have to be removed, there's no real advantage in keeping the DTM tag as you'll still need a release cycle to make this change. The other checkbox to disable the DTM property shouldn't be checked as you may need to go into DTM later.
Once the new (migrated) Launch property has been created, click into the property and do a quick sanity check to make sure all rules, data elements and the Analytics/ECID extensions have been copied over correctly. At this time, add any extensions in Launch that didn't get copied over from DTM such as Target. I've covered AAM separately in the last section.
Replace your existing DTM code hosted in your staging environment with the Launch embed code to begin your tag validation. Click the 'Environments' tab and copy the development script as shown below.

Test in staging and closely follow your migration plan. After meeting all your validation criteria, publish your new Launch property in production.

Audience Manager SSF Migration

If you had Audience Manager deployed via server side forwarding (SSF) in DTM, the AAM module will get copied over to Launch and the module will be present in the Analytics extension. My recommendation is for you leverage the in-built SSF integration for AAM inside the Analytics extension. Let's take a look.

Click 'Open Editor' in the Analytics extension.

Comment out the AAM module code in the library and save changes.

Choose 'Manage the library for me' option to let Launch host your Analytics library as well as automatically upgrade you AppMeasurement library. Also 'globalize' your Analytics object so that you can leverage the object elsewhere in Launch and in the browser console.

Finally, check the checkbox as shown below to enable the automatic AA->AAM SSF integration in Launch.

So that's a wrap. I hope you found this walkthrough helpful in case you decide to go with the migration tool but whatever approach you choose, it's imperative to have a sound migration plan and validation strategy.

Saturday, January 12, 2019

Miscellaneous Facts About Adobe Audience Manager

Happy New Year 2019 to all and Thanks for reading this post. Adobe Audience Manager just like any other data management has its fair share of characteristics that are not commonly known. Unless you've actually spent some working time on AAM, it's unlikely to find these facts readily available in our documentation. In this post, I've aimed to document some of these in a single post. So, let's get started!

1. The third party Audience Manager demdex cookie is the same across all sites (customers) that have AAM deployed. The reason why it's the same as it allows cross domain tracking of AAM customers and also forms the basis to share data across all AAM customers (2nd party data share). So, if you delete the demdex cookie, it gets refreshed for all AAM customer sites.

2. Metrics in Audience Manager are based off Cookies/Visitors tied to the UUID (device/browser) or demdex. Customer often ask about replicating Adobe Analytics segments based on Visits in AAM which isn't possible to replicate 1:1 so it's important to understand this nuance.

3. Server side Forwarded Adobe Analytics data to Audience Manager is billed separately in Audience Manager. Customers often assume that this data is forwarded for free which isn't the case as the same applies to data captured via the client side DIL data collection method in AAM which is billed separately.

4. Audience Manager variables starting with 'c_' are customer defined keys, 'd_' are platform level keys and h_ are typically browser header keys. Let's review of few of these.

o Following are some keys to filter for the URL and/or referrer.

§ d_referer (not traffic referrer) allows you to filter for the page URL

§ If you have Adobe Analytics forwarding enabled, you can also use c_pageURL.

§ If you want to filter for the traffic referrer and have Analytics data forwarded, you can use c_referrer.

o If you have a scenario where you capture the same page name across multiple sites (with different Adobe Analytics report suite IDs) and have SSF enabled, you need to use the d_dst_filter field. I've included a screenshot which shows how to filter it using a trait. See how the customer variable starts with 'c_' and platform variable starts with 'd_'.

o If you want to capture data tied to city or country based off automatic IP address which is collected in AAM, you can use the following Geo platform keys:

§ d_city

§ d_country

5. Given that Adobe Analytics data is now forwarded to AAM via report suites, the Audience Management Module is not required to forward data BUT should ALWAYS be implemented if you want yours users to qualify for segment as without it, you cannot leverage AAM for A/B testing and personalization. The other advantage of the module is that it creates a first party cookie for AAM (covered in #7).

6. One other confusion that clients have is around the data collection frequency and availability of rule-based traits in AAM. Rule based traits start collecting data in real time since creation even though the data may be take up to 24 hours to show up in the AAM UI. So, if you have a scenario where you want users to qualify for a trait in real-time for personalization, you can rest assured. To validate if you qualified for a trait, you can use Visitor Profile Viewer in AAM and other methods defined here.

7. UUID demdex cookie (covered in #1) which is usually a third party cookie can be captured as a first party cookie called 'aam_uuid' within the Audience Management module (#5). Once you set the UUID as a first party cookie, you can easily create a data element in Launch by Adobe (shown below) and map it to an Analytics variable.

8. Customer IDs synced using natural match upon authentication need to be hashed and should not contain any ids that are even borderline PII. A good method to anonymize your user ids is to leverage the SHA256. The other important thing to keep in mind is that you should leverage the same type of user ID across multiple sites and map them to the same AAM data source so that there's no ID collision.

9. Data onboarded via 3rd party data providers such as Acxiom or data onboarders such as LiveRamp upload data to AAM using a unique 'ic' (integration code) which is numeric (example below). In a previous post, I covered how to upload data using key value pairs but an 'ic' can also be used to upload your CRM data.

10. 3rd party ID syncs enabled by AAM consultants shares the AAM UUID with Demand Side Platforms where they store this ID in a match table so that they can target users on their own network using this ID. Adobe also receives a corresponding ID from that system as well so that each system's cookies can be synced with each other. This sync is used to calculate the match rate which is why it's recommended to ask your consultant to turn on these id syncs to get a higher match rate between AAM and your DSP. However, these cookies can be turned off by setting the disableThirdPartyCookies variable to true in the ID Service library.

Below is what these id syncs look like in the network tab of your browser (Adobe Ad Cloud) populated the very first time a user visits your site. Also, note that subscribing to a data provider in the Adobe Marketplace automatically turns on an id sync as well.

I hope you found this post helpful and feel free to share any other misc facts that you learnt about Audience Manager.

Sunday, December 9, 2018

Fix Variable Stickiness on Click Events in DTM & Launch by Adobe

This post covers a fix for a very common issue where page load variables tend to 'stick' or 'persist' on the subsequent click event. I'm leveraging the clearVars() function and covering exit link tracking in this case but the same logic can be applied to other types click events too. We will cover this fix keeping in mind both DTM and Launch by Adobe so without any further delay, let's jump right in!

Overview of the Issue

Before we dive into the issue, we have to take a look at the HTML structure of our page. For the purposes of this post, I'm using my demo site as an example. In the screenshot below, we can see that all of our internal site links start with ".." and our external link pointing to http://www.adobe.com starts with "http://". We can use this distinction as a condition in our Tag Manager.

Here's an example of a page view call (from my test site) where we have a custom Page View event (event6) and an eVar (eVar3) to track the Page Name.

On click of an exit link, we see that event6 and eVar3 get carried over on the next call.

So, let's discuss how to fix this in both DTM and Launch by Adobe.

DTM Fix

Given that DTM is still a populate Tag Manager that will eventually be sunsetted, it makes sense to cover how to resolve this issue in this TMS.

The first step is to disable automatic exit link tracking in DTM. We do that by unchecking the "Track outbound links" checkbox in the Adobe Analytics tool.

The next step is to create an Event Based Rule tied to the anchor ('a') element with the event handler of 'click'. The reason we do that any link on the site can be an exit link so it makes sense to simply look for the presence of an anchor link.

The next step is to disable any tracking under of the Adobe Analytics section.

The final step is to add some custom code to fire our own exit link tied to the exit link starting with "http" (covers both http: and https:) where we only include eVar3 (Page Name) as part of our exit link call along with the exit link URL passed to the s.tl() call. Note that I'm using s.clearVars() to remove the extra event6 from my call.

Once published, we can see that on click of the exit link, we're only seeing eVar3 being passed but no longer seeing custom event6 being populated in the image request.

Launch By Adobe Fix

Now, let's take a look at how to tackle the exact same issue in Launch by Adobe. Given that Launch has an in-built action for clearing variables, we will leverage that instead of passing it in the custom code.

The first step is to disable exit link tracking from Launch by Adobe by unchecking the 'Track outbound links' checkbox in the Adobe Analytics extension.

Next, I've created a new rule and am using the 'mouseover' event (which I really like as I'm able to test links by right clicking instead of clicking to monitor image requests). For Actions, I'm using 'Adobe Analytics - Clear Variables' and then just selecting 'Adobe Analytics - Set Variables' to put in my custom code in the JavaScript Editor. Note that I've not chosen the 'Send Beacon' action as we're doing our own custom event handler.

In the Code Editor, I've used the exact same code as DTM except for the clearVars() function as I'm leveraging Launch to do that.

After publishing the code, we can see the exact same image request as we saw in case of DTM, where we're only capturing eVar3 along with the exit link URL.

So, that's it! Hope you enjoyed this post and can make use of what I've done (or something better) to solve this common issue.

Saturday, December 1, 2018

Reasons For Data Variance Between Analytics Systems

I'm almost certain that at some point in your career, you've been asked to troubleshoot an age old problem of comparing numbers between different analytics systems. I have been asked about this (several times!) so I'm writing this post to share what in my experience, are some of the reasons for this difference and also some potential ways to close these gaps. I will talk about a recent experience I had with a client and talk about some of the issues we faced and a potential solution the team is evaluating.

Based on a few comments I got about it to not pursue this type of analysis and only look at trends, I want to clarify that we did that and the trends did not line up in this case. We all know that no two systems are going to be the same but in such a scenario, you do need a way to validate your orders to establish the web analytics tool as the ultimate source of truth. That can only be done by going through this painful exercise at least once which happened to be this time when I was first looped into this project.

The Challenge and Context

My client reported a 25% variance between eCommerce orders reported on the website and their backend system where the website was lower. The issue started after they redesigned the UI (Single Page App) and updated the backend platform. The difference was caught about 2 weeks after the new UI went live. We had used DTM to slightly revamp the implementation to add additional attributes and asked the platform development team to fire a Direct Call Rule (same as before) on the confirmation page which was already in place in the previous version.

The client wanted us to come up with a solution to 'backfill' these historical missing orders in Adobe Analytics and also apply the right kind of marketing attribution to them which was always going to be challenging as time had already passed. The basic issue is that once a Visitor ID has had been to the site and visited a bunch of pages and certain eVars were set, backfilling data for that user would ‘break’ the pathing flow, the visitor profile and any downstream conversion metrics would also be messed up. This Adobe article on timestamps explains the issue better than I did.

What is an Acceptable Variance between Systems?

Honestly, the answer to that question depends on the client, system and the metric we're looking at. If we're looking at deduplicated metrics such as Visits, Visitors or even Orders, then anything below 5% is acceptable in my experience. For duplicated metrics such as Page Views or Clicks, I've seen clients accept anything below 10-15% if we're comparing one client side system (Adobe Analytics) with a backend CRM system (my use case).

The other thing to note is that if we're comparing Adobe Analytics to Google Analytics or any other web analytics system, then a lower variance should be expected. Keep in mind that both client side solutions should fire as close to each other as possible on the page to expect a low variance.

Analysis and Approach to Pinpoint the Variance

During the course of the investigation, we looked into the following factors that could've contributed to the variance.

But before we begin, make sure you download records from both systems that should be from the same time frame, time zone and need to have the same dimensions and filters as much as possible. We know that no two systems will be apples to apples but let's make sure the comparison files are as close to each other in all respects. Let's now take a look at some of the factors we looked at keeping the orders metric in mind in comparing a web analytics solution such as Adobe Analytics vs. a backend CRM system.

If you're not tracking all the payment methods in one system vs. the other system, you will see some discrepancy in the overall orders.
There may be conflicting JavaScript code deployed on the confirmation pages (privacy, plug-ins etc.) that will not be compatible with scripts used by other payment methods or Analytics tags. This was actually one of the causes of the difference.
Legacy Browsers may not run JavaScript tags so that is another cause of the variance.
There may be internal IP filters or bot filters that are applied at the web analytic tool level that won't be present in the CRM system so that is another reason.
Given that our Direct Call rule was firing at least 8-10 other marketing tags including Google Analytics, we thought that in some cases, Adobe Analytics didn't fire because of that so try to limit the number of tags on key pages such as confirmation.
Most important is the order of execution of the Adobe Analytics tag and if it's able to successfully grab all elements present in the data layer. In our case, we noticed that Adobe Analytics was not able to grab elements from the data layer 100% of the time.

What was the Root Cause of the Variance?

The main reason for the variance was that the website did not fire the Direct Call Rule in DTM required to track the orders 100% of the time in Adobe whereas the CRM system always captured the order if it was a successful submission. We were able to nail this down when we looked at the orders in Google Analytics which ALSO reported a 25% variance between the CRM system.

Potential Solutions to Backfill Missing Traffic

Partial- Data Insertion API

Data Insertion API is a very well known and common methodology to send data to Adobe Analytics server side. In this case, we tried to leverage this method to backfill historical orders and it worked but the other requirement around marketing channel attribution could not be met by this method. If your client also wants to fix the marketing channel attribution problem, this method is not going to work.

Regardless, I still want to quickly cover how we went about testing some orders using Postman to send a POST request to Adobe Analytics. The following screenshot shows a sample XML request to send historical order to Analytics. Note that we need to pass an epoch based timestamp which is a required field to backfill historical or any data.

Complete- Data Reprocessing

I don't have much experience with this but based on what I've heard, a custom consulting engagement with Adobe's engineering team to reprocess the data can fix the attribution issue. This is still being evaluated and I will provide an update when we're done with the project.

How Can We Avoid Issues Like This?

The only way to avoid such issues in the future is to thoroughly test this in a development environment before anything goes to production. A similar kind of rigor is needed to test key metrics in a test environment similar to how it is in a production environment especially during major redesigns so that we nip it in the bud at the onset. The other thing that you can do on an ongoing basis is create anomaly detection dashboards in Adobe Analytics or other tools that are designed to catch any noticeable drops or inflations. I know there are other ways to avoid issues but that is a separate blog post in itself.

My hope with the post is that I'm able to help you find some reasons around why traffic is different between systems on how to be better prepared while undertaking such a task. I wrote about something similar way back in 2007 when I started my career that might be relevant. Have you run into a similar issue for your clients?

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