DMP Low-Hanging Fruits

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.

In order to get some quick wins, we recommend to start with the low-hanging fruits. For your first few campaigns, do not try to create very complex rules for segments. Instead, think about easy rules that only require online behaviour. Here are a few of them:

  • Exclude customers from prospecting campaigns. This is probably the easiest case and the one that makes most sense: stop showing prospecting ads to users that are already customers; you are 99% sure they are not going to convert. This is as simple as creating a segment with customers and send it to the DSPs. Then, using the exclusion capabilities of the DSPs, exclude the visitors in that segments from the prospecting campaign. One of my customers had a drop of about 20% in the cost per acquisition just using this technique. If you use AAM with Adobe Analytics, here you have a few examples of the traits you can use to detect that a visitor is a customer:
    • The log-in event has been fired: (c_events contains “eventX”)
    • If you are capturing the CRM ID in an eVar or prop, then something as simple as (c_evarX/c_propY matchesregex “.+”) should do the trick
    • Any page of the private section (e.g. c_pagename == “my:private:area”)
    • Depending on what you sell, it can be as simple as users who have purchased something: (c_events contains “purchase”)
  • Include only local visitors or exclude non-local visitors. Very similar to the previous case, if you have a business that only sells locally, you do not want to waste any money on banners shown to visitors that come form regions where you are not going to delivery your goods. In AAM, this is very easy with geotargeting with platform-level keys.
  • Retargeting abandoned baskets. For all of those products that provide you with a high margin, you can create segments with users who have abandoned the basket with those products in it. You then create very specific retargeting campaigns using these segments. The segments will formed of two traits: “Add to basket – <product id>” AND NOT “Purchase – <product id>”
    • Add to basket: (c_events contains “scAdd” AND c_products contains <product id>)
    • Purchase: (c_events contains “purchase” AND c_products contains <product id>)
  • Up-sell or cross-sell. Target customers that have recently purchased certain products, customers who your experience (or your Web analytics data) shows that they are very likely to convert again. This is as simple as the previous example: (c_events contains “purchase” AND c_products contains <product id>)
  • Frequency capping. If a user has seen a campaign more than X number of times, stop showing the campaign to him. You need to decide which is the optimal X, but we all know that after a certain amount of times, if the user has not clicked on a banner, it is very unlikely that he will do it in the future. In AAM, you would use the frequency and recency capability of the segment builder.

What do you think about these initial segments? Any other segments you would recommend as low-hanging fruits?


AAM, surveys and look alike modelling

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.

Enter Adobe Audience Manager. One of its capabilities is the look alike modelling. Basically, this feature compares a base population with the rest of the population, finding similarities. You can think of it as an algorithm that gets all the traits from the base population, remove the base trait, and checks the rest of the population for visitors exhibiting the new list of traits. The main goal of this feature is to uncover hidden population segments and this is exactly what we need it for.

Going back to the survey, many on-line survey tools have the capability of processing the answers and provide a score or a classification. With this information,  once the user finishes it (or after selecting a particular answer in a question), we can add a tracking pixel, with a key/value pair different for each score or classification. Creating a set of traits from this tracking pixel is trivial.

The next step is to create one model using one of these traits. I am not going to talk today how to use this functionality; that is material for another post. The algorithm will extract a subset of the population that looks very similar to the people who have conducted the survey, even if they have not conducted this survey. In fact, the algorithm generates a trait, that can be used in a segment.


After this explanation, let’s try to illustrate it in an example.

  • Bank website
  • 20,000,000 registered users
  • A survey is created to analyse investment interest
  • 5,000 people conduct the survey
  • 1,000 people are classified as “interested in investing”
  • A model is created to find similar people to those “interested in investing”
  • The algorithm, with an accuracy of 0.6, finds 1,000,000 visitors potentially “interested in investing”
  • These 1,000,000 visitors are then targeted with a campaign to show the investing products the bank has

In other words, from just a population of 1,000 visitors that we know for sure are interested in investing, we have uncovered a population 1,000 times bigger of potential investors, just by looking at similar traits.

A brief introduction to AAM

The concept of a DMP (Data Management Platform) is not new in the digital marketing arena. However, there are still many marketers who do not know of this type of platform and what it can do for them. I will explain what is a DMP and what is Adobe Audience Manager.

In today’s digital life, visitors give a lot of information about their traits. However, that information tends to be siloed. The two main sources of information, CRMs and web analytics, do not “talk” to each other. Your web analytics tool just gives information about on-line visitors and your CRMs only knows about static data. Ideally, we would like to have all that information combined in real time so that we could provide the visitor with the best experience.

Think about these scenarios:

  • An airline wants to offer credit cards to frequent fliers between London and Paris.
  • A retailer would like to retarget visitors to their website, who have just moved to a different website without purchasing.
  • A media company is interested in selling ads on their own websites and wants to show mainly relevant ads.
  • A car manufacturer has created a series of ads, which should be shown in sequence.

All these scenarios and many more can be fulfilled with a DMP. Without getting into much detail, a DMP uniquely identifies a visitor and tries to get as much information from that visitor, using different sources and marrying the data from each of the sources. One other key property of a DMP is that it should be completely anonymous; in other words, no Personal Identifiable Information (PII) should be managed by the DMP.

Demdex was one of the companies that offered a DMP. Adobe acquired Demdex a few years ago and included the DMP in the Adobe Marketing Cloud. This product is now known as Adobe Audience Manager (AAM). What are some of the differentiating features of AAM?

  • It integrates nicely in the Adobe Marketing Cloud.
  • It can be natively be delivered through DTM.
  • It supports Adobe’s Visitor API. As a consequence, you can use the Master Marketing Profile (MMP) in AAM.
  • It captures natively all Adobe Analytics variables (eVars, props, events…)
  • It can be used in conjunction with Adobe Target to create personalised experiences.

I have been working with AAM now for a while and I can see a lot of potential in it.