Adobe Target Reviews

I have used Adobe Target for different purposes. The most basic use case is A/B testing. We run random tests on the website. Additionally, I use Adobe Target to run various campaigns. For example, on an e-commerce site, I help the client run a flash sale. Suppose the client wants the flash sale to be visible only to users accessing the website from Europe. I build and develop the campaign to ensure that only users from Europe see the banner for the sale. If users from the rest of the world access the site, they won't see the banner. This is one of the normal use cases I handle for different customers.

Another use case is running recommendations. Similar to what you see on Amazon, where it shows "people who bought this also bought that," Adobe Target can implement these recommendations. I worked on this for a client. When a user visits their website and views or buys products, they start seeing related products at the bottom, like "you might also like these products." For example, if you visit a mobile phone product, you will see similar electronic products. This is another use case I handle with Adobe Target for different clients.

A/B Testing and Navigation Optimization

In terms of A/B testing, we handle basic use cases and complex ones. For instance, I recently worked on a complex use case for a client named Helly Hansen. They wanted to test a new navigation method on mobile devices. Instead of opening different screens, they wanted customers to see all sub-navigation links on the same screen using an accordion. Clicking a link would show all related sub-navigation links within the same pop-up, without navigating to different screens. We just went live with this test. This is an example of the complex A/B tests I handle using Adobe Target.

Additional Tools and Personalization

While Adobe Target is a primary tool I use, I also work with other personalization tools like Dynamic Yield and Optimizely. These tools complement Adobe Target in delivering personalized experiences to users.

There’s nothing specific I like best because recently, I’ve started using Optimizely, and I prefer it. Optimizely is also a tool in the same realm, where you run different tests for customers. I like Optimizely better than Adobe Target because Adobe Target was one of the first tools on the market for A/B testing, but now I don't think Adobe is making many changes to the tool.

When I look at other tools in the market for personalization, they are bringing new features. For example, on a single-page application (SPA), where the page doesn't refresh when you click a link, recent websites handle this well. However, Adobe Target, being an older tool, hasn't done much to address this. Other tools, like Optimizely, have special functionalities for handling SPAs effectively.

Handling Single-Page Applications:
Adobe Target handles SPAs, but not in a very efficient way. They require users or website owners to develop something on their end to trigger a call for every view they want to run an experiment or test on. This approach is not very user-friendly. Tools like Optimizely have better solutions for this issue.

Personal Experience and Usage:
So, there isn’t a particular feature in Adobe Target that stands out to me. It was the first personalization tool I used extensively, but now I find other tools more advanced and user-friendly.

Ease of Use:

In terms of the tool itself, Adobe Target is easy to use. Everything is readily available and intuitive, making it easy to understand how to use it for basic tests and other functions.

Adobe Target is a cloud-based tool that is part of the Adobe Experience Cloud. It is used in segments where we have to use personalization on the fly. I can use the solution to perform A/B testing or make recommendations for website development.

I work in a media company that has technology in data centers. We have industry requirements where we need to put on some campaigns, do some marketing, or prepare some tools that are deployed on the website. There is always scope for improvement because our clients operate in a vast geographical region.

There could be a scenario where some campaign or the visual appeal of a website works best in one region but may not be suitable for the other region. We form such a hypothesis, and Adobe Target is the right platform to test this hypothesis. The tool helps us better understand what kind of visualization or experience works best in a region or with a specific audience.

Adobe Target gives valid data to test such hypotheses. The solution has some critical tools with which you can do A/B testing, multivariate testing, recommendations, and other types of testing. When you reach a confidence threshold score of around 95%, it gives valid data that you are good to go with this particular experience in this region. This is a baseline scenario that Adobe Target helps in.

Adobe Target is mostly used to personalize user experiences on websites. It helps businesses deliver targeted content and offers to different audience segments based on their behavior and preferences. This tool is widely used across e-commerce, telecommunications, and finance domains to achieve specific conversion goals tailored to each website's needs. It allows for retargeting users who have previously interacted with the site and conducting A/B tests to compare different versions of content or offers to optimize website traffic and conversions.

Additionally, Adobe Target offers features like multi-variation testing, automated personalization, and recommendations, which are particularly beneficial for e-commerce websites. Integration with other Adobe tools like AEM and Adobe Audience Manager further enhances its capabilities in audience segmentation and analytics for retargeting strategies.

This is a significant concern due to the absence of a lower environment for testing activities. This can lead to issues if changes are pushed to production by mistake. Additionally, the effectiveness of Adobe Target is heavily dependent on the Target library; if the library files are blocked, no changes will be visible on the website or mobile app.

We have some bugs. For example, suppose I'm creating the activity because of all the activities we create. In that case, we have to get the data from user interaction and understand how this activity was performed. In some rare scenarios, we couldn't capture the data after the activity was published in production because of some issues on Adobe Target's end.

It doesn't provide the proper data in Adobe Analytics. After that, we have to contact the Adobe customer support team and work with them to get that data. A few times, the particular libraries' location is also not properly detected. For example, if I were in London, it would take some other location, like Scotland or some other place. In a few of the implementations, I noticed that the library also is not taking the proper location.

Basically, I have used it for the personalization of content and forms. I have been personalizing for different regions, like the US, UK, and others. The forms need to be different, or at least the segment or fragment of the forms, depending on the region. 

So, I created a kind of personalization by switching up the form based on the location and certain other conditions. I also did content personalization based on the logged-in user, modifying the product solution using Magic to pull up data and push it onto Adobe Target.

I found the recommendation section to be the most valuable. That has been my use case majorly, primarily for product recommendations based on categories we created. Additionally, there was experience targeting, usually used to personalize content for specific users. That goes hand-in-hand with the account.

Another thing is a fortiori, where I was analyzing the experience targeting results and seeing which was the winner. It helped me understand the data better.

AI initiatives: 

I could use it in one. There is a new feature in Magento 2 as an Adobe Commerce, where it was using this feature called Configuration (I forget the name of it). It uses the Sensei AI feature in the backend to give customized recommendations for products. I work on a current project that uses this, but it's a separate team. They are using Adobe Target within the front-end framework, and the data coming into Magento is coming from an AI-driven feature using Sensei Analytics.

Learning Curve: 

Adobe Target is quite an easy tool. However, since there's a lot of involvement of other Adobe products, you have to be aware of that. When we use Adobe Target, it's not a single product. There is also Adobe Launch or Adobe IO, which we should be executing on. 

So, a bit of technical knowledge will be required to understand how to create your experiences. But mostly a front-end knowledge of the framework is sufficient enough to start to work on this.

I mainly use the product for A/B testing and personalization. We base our tests on customer journeys, sometimes getting insights from other teams about customer segments. The business comes up with test scenarios, and we develop and compare different experiences, gathering feedback and making adjustments until they are satisfied. Once approved, we move it to production, closely monitoring the results and customer interactions.

The platform's most valuable feature is deploying code live instantly and retracting it just as quickly. The user interface is good and allows connections to various analytics and reporting tools, such as Adobe Analytics and Google Analytics. Adobe Target has many built-in features that simplify implementation without extensive coding.

We use the solution mostly for A/B testing and personalization.

The most valuable features of the solution are automated personalization, AI, and machine learning algorithms.

Adobe Target has a user-friendly interface, which makes it easy to use. Plus, there's a supportive community and comprehensive documentation available. These features make it accessible to anyone and facilitate seamless integration with different systems. It's like a one-stop solution, making Adobe Target impressive and efficient.    

For modeling purposes, we use a different platform as Adobe Target doesn't offer custom machine learning model building. This separation of tasks among different systems makes sense. I don't see any significant disadvantages, as Adobe Target is a leading tool in the market and offers a great set of features compared to other tools.

The main purpose of the target is to personalize and optimize user behavior. If we consider that you are visiting some page today, let us assume that you are interested in Nike shoes or something. What will happen is that since you have visited the site containing the Nike shoes, a personalized banner or a personalized offer, particularly for a specific user based on some criteria, will be made visible to those users who have visited the home page and the Nike shoe page.

The most valuable feature of the solution is the personalization area. The other valuable feature of the solution is the AB test. If we are planning to have a new UI, you can push that new UI to a limited number of folks in the initial phase, and then eventually, you can replicate the older version and bring your new version. In the whole process, you can evaluate whether your new UI is working or not or if you need to stay with the existing UI. You can make some analysis based on your old UI. You can do the analysis since data is collected in Adobe Analytics. We can analyze whether the user is clicking or not and whether he or she is converting or creating a lead, and based on that, we can do an A/B test as well, and then we can have some automated personalization. For multiple personalizations, we have to configure them in the back end, and then automatically, it will start flowing to your browser or the application randomly, so it is not, like, fifty-fifty or seventy-thirty and is not fixed. It will randomly show you Abc, for example. Ten times 'a' will appear, ten times 'b' will appear, and ten times 'c' will appear. If you eventually click on 'a' multiple times over a period of time, you will get 'a' as an experience or your default experience. It will mitigate your low-performing personalizations or experience over a period of time, and it will make it like a high-performing default experience. Then, we have the product recommendations or content recommendations. The recommendation is something that if you have viewed a specific product, like Nike shoes, then what will happen is that we can retarget that particular user with some offers on that specific product. We can also have default algorithms that talk about how people who viewed this also viewed that. We can look at the most viewed products or top products. We have these algorithms built into Adobe Target, which we just need to use and retarget the users.