Have you ever received a request to track and detect online shopping cart abandonments in real time? If you have, then you are not alone. This is a typical request we get from our clients and I have seen too many times. The theory is very simple: if we can detect that a user has added something to the basket but has not purchased it, then we need to persuade him to finish the process. However, the reality is more complicated than just that. Let me explain what I usually discuss with my customers and what options do we have.

According to some news, all major Internet players are now focusing on chatbots. I have never used one but it looks like with the progress in artificial intelligence, we will all be using them in the not-so-distant future. Facebook even claims that thousands of developers are creating right now chatbots. If this is true, then we should be ready for them. While I read this news, I thought, how about using Adobe Analytics to track the conversations? Would it make sense to large corporations, which already have Adobe Analytics, to use the same tool as with website and apps? I know some people will contend that my idea is wrong, that chatbots will need a different reporting tool. However, I would then reply that, a few years ago, it was not clear whether Adobe Analytics would be used for apps; now, all my clients, want to integrate Adobe’s SDK in all their apps.

If you are working with a DMP like Adobe Audience Manager, I am sure you have come across the following problem: you want to target your visitors on site, immediately after they log in, using on-boarded data, even on the first visit. This last statement is, precisely, where the problem is. The way AAM processes on-boarded data is as follows: You upload your CRM data to AAM, either to an SFTP location or an S3 bucket Every 12h, AAM reads all on-boarded data and processes it, converting the signals into traits The traits are stored in the core servers A visitor logs in for the first time Since the communication between the browser and AAM is done through the edge servers, these servers have at this moment in time no on-boarded information for that visitor The edge servers where this visitor activity has happened, request the on-boarded traits to the core servers In a batch process, core servers send to the edge server the visitor’s on-boarded information

[UPDATE 07/01/2019] I have changed the operator in the trait expressions. Thanks Glenn! Initially, when we think of segments (or clusters) for ad segmentation, we think of ever-growing groups of cookies. Simple use case like purchasers, visitors to our website, subscribers to a newsletter or owners of a device fit in this model. However, advanced (and not so advanced) use cases do not work well with this model, where we have visitors entering and leaving regularly a segment, so a segment can shrink in size: Retargeting dropped baskets: the moment someone places an order, you do not want to retarget him again Customers of a mobile operator: it is very common nowadays to switch to a different provider frequently Age group: ever day, visitors enter one particular age group or leave it, as people grow older

My job as an Adobe Analytics consultant has involved, very often, bridging the gap between these two worlds. I have seen myself many times as a translator: getting a message from the marketer, translate it into technical terms and communicating it to the developers; and vice-versa. My developer background has helped me a lot in this case. In quite a few cases, I have been requested to join meetings just to make sure that the IT team understood what the marketer wanted. It does not help either the fact that web analytics is not considered as important as it should be.

Let me start with an anecdote I once heard. The marketing department decided that they wanted a new feature in the home page. The IT team received the request and implemented it as per the requirements. Three months later, the business owner of this new feature requested a report on the performance of this new feature to the web analytics team. To the team’s surprise, that was the first time the web analytics team had heard of this feature. Consequently, had not issued any tracking requirements and there was nothing to report on. In other words, three months had been lost.

Before I started working with Adobe Audience Manager, I had a very limited knowledge of the on-line advertising market. In the past, I had managed Google AdWords campaigns, but that was all I knew. Now that I have been working for some time with a few AAM customers, I have realised that the market for on-line campaigns is huge. There are many actors involved: agencies, trading desks, DMPs, DSPs, SSPs… I still have to learn more about this market.

Today’s post is going to be a different form the last few posts, a bit more hands-on. One of the typical questions I get from my AAM customers is “how do I detect a user browsing with an iPhone [model]”. The only solution we have to reliably detect the device is through the User-Agent. Although this should be very simple, in theory, there is one problem: Apple does not want you to detect the iPhone model. Android devices include in the User-Agent the name of the device, or enough information to get it from there. However, Safari browsers include the device type (iPod, iPad or iPhone) and the iOS version, with no hint of the model.

This is going to be a rather short post, but only from my side, as the poster: if you follow everything I am saying, it will be even longer for you to process than any of my previous posts. Let’s start by watching the one of the great TED talks: Sebastian Wernicke: How to use data to make a hit TV show

This is my first attempt to write an opinion article. I had it in my mind for some time, but the sparkle was a question during my talk at the London Analytics Labs. One attendee asked me about the future of on-line advertising if 3rd party cookies and/or ads were blocked from all browsers. So, this is my point of view.

I will be presenting at the Analytics Labs in London next Tuesday 26th January.

[UPDATE] This is an old post, which I keep for historical purposes. DTM is not longer available. We are all aware of the importance of creating secure products. In a previous post, I explained how to set up a workflow for a DTM implementation. One of the consequences of using this workflow is that only a reduced number of users can cause damage to the website via DTM. This is also good from the security perspective, as it reduces the risks of a successful attack. This is probably enough for most companies.

Before getting into the details of the post… Happy New Year to all of you! I hope that 2016 is full of DMPs, DTMs and Analytics 🙂 Now, going back to today’s topic, I want to talk about how to create the products string in DTM using the W3C data layer. One of the reasons why we prefer a tag management solution (TMS) over hard-coded snippets is to write less code. All modern TMSs include features to set analytics variables using a point and click interface, usually through Web. In the case of DTM, you can create a data element that reads a data layer variable; you can then assign it to an eVar or a prop, without writing a single line of code.

In my experience as an Adobe Audience Manager consultant, I have noticed that many clients need a lot of hand-holding at the beginning when working with this DMP. Coming from the Web analytics world, this was a bit of a surprise to me at the beginning. I remember when I started an Adobe Analytics project I worked on 6 months ago, one of the client teams had a spreadsheet with 138 requirements… and that was only one of the teams involved. They knew exactly what they needed from the tool, which made my life easier. However, this is rarely the case in an AAM project.

A while ago, a customer requested a call with me to discuss one issue. Usually, I get more technical questions, but this time, he wanted to have my input regarding something completely different. The developers had realised that they forgot to include a JavaScript library in the website and they could not add it immediately, due to code freeze. They thought of an alternative solution: load it through DTM. My customer, from the marketing department, was not sure whether this was possible or acceptable and, therefore, wanted to know my point of view.

If you have been developing websites for a while, you will know that one of the typical recommendation is to execute as much JavaScript as possible at the bottom of the page. This is nothing new and Yahoo recommended it in 2007. The reason is very simple: JavaScript code tends to add a delay, both when loading the JS file and executing it; so, moving it towards the bottom, you make sure the HTML is loaded and the page is rendered before starting to execute any JavaScript. The user believes the page is loaded a bit sooner than when it is actually fully loaded. DTM knows that very well and this is why you have to add the two pieces of code: one at the top and one at the bottom of the HTML.

A few years ago, one of my customers showed me a tip that I found very interesting: tracking the lifetime value of a customer. The SDKs offer a function to track the visitors lifetime value, but the traditional JavaScript implementation does not have anything similar. So, we will have to create it.

If you have been in an Adobe Analytics implementation, it is highly probable that, at one point or another, you have heard the expression “VISTA rules”. However, many of you might still wonder what those little beasts are. First of all, let’s start with the name. Unless you dig in Google or the help section, you will never have guessed that VISTA stands for “Visitor Identification, Segmentation & Transformation Architecture”. Do not get too impressed with this name, it was just an imaginative way of getting a fancy name.

As all digital marketers know, surveys provide invaluable information from visitors. They allow you to know various types of information from the visitors: the website itself, likelihood of buying, preferred products… The outcome of these surveys can be used to modify certain aspects of the experience or target the visitors with specific messages. All marketers would like every single customer to perform a survey and use that information to create a perfect experience for each visitor, but the reality is far from this ideal. Only very few visitors end up accepting the invitation and this usually happens when there is a potential reward.

Now, looking into the standard, we will get into the different sections that conforms recommended data layer. Let’s review each of them in the following posts.