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Moving Beyond The Data Hype With SAP XM

When we started thinking about SAP XM as the next generation end-to-end media network– a technology beyond programmatic, one of the critical aspects for us to consider was the question of data - type, quantity, quality, significance etc. We were exposed to the various Data Management Platforms out there already doing a great job, such as  advertiser DMPs, publisher DMPs, independent or even hybrid DMPs.

It was mind blowing!

Challenges of Customers And Partners

I wondered, when ‘techies’ like us find it difficult, how would customers handle it? Hence, we decided to approach the need for managing data on our platform without any preconceived definitions in our minds and to completely focus on what main challenges our customers and partners were facing:

  1. “I know we have a lot of data, but I don’t know what it can do for me. ”
  2. “I want to reach my consumers at the right place, at the right time.”
  3. “I want to be closer to my customers.”
  4. “ I already am working with a DMP – Why should I change that?”
  5. “Is my data safe?”
  6. “How can data help me improve my advertising RoI?"

Getting Focused And Creating A Plan

The above challenges provided us with some basic constraints. SAP XM needs to handle data generation across all channels i.e. cookie based, ID based, segments, collections etc. and needs to consider multi-dimensional data sets – not to speak about 1st, 2nd and 3rd party data. Furthermore, it is important that SAP XM is recognized as a secure and trusted platform from customers. Another necessary feature to consider was that the platform should not only act as extension of the customers’ enterprise landscape, but also allow for customers to onboard their existing relationships onto the platform in order to have freedom of choice. To fulfill consumers’ expections about sending the right message to the right consumer at the right time, it was clear that SAP XM had to leverage the machine learning based capabilities to find relevant and intelligent matches – all based on the best in class in-memory technology of SAP HANA.

Our intention has always been to achieve the mythical ’segmentation of one’ – the ability to reach that single individual with the information that person needs at the exact moment he or she needs it. Yes, we also want to make use of traditional use cases, e.g. intelligent targeting, retargeting, look-alikes, cross channel tracking, but also achieve greater and more innovative goals. We thought about integrating sentiment analysis and offering our customers the possibility to create personalized content, which allows them to be really close to their consumers. There was also the thought of intelligent simulations, so customers could “test flight” campaigns before they went productive. Another interesting feature would be to fill in the gaps of our customers’ data, enriching it and therefore allowing more intelligent systems. As a consequence, it was clear to use the power of machine learning to predict persona swings in order to increase the relevance our customers’ messages. Finally, it was our goal to track life transitions to not only build advertising but trusted relationships.

Data, Data and more Data!

We started off with classifying different data dimensions we have access to – on well known standards – into 1st, 2nd and 3rd party data. Let’s take the opportunity and define each of the above in our context.

1st Party Data (held on SAP XM) is the data generated by users working directly with our platform. One could classify the various data sets into the following categories:

  • Registered accounts (advertisers, publishers, individuals)
  • Registered users through website or the portal (individuals)
  • Ad requests from specific users – dependent on Data Privacy guidelines (individuals)
  • Bid requests from SSPs
  • Advertising-specific events such as views, clicks, win/lose notices, conversions, etc.
  • Logs (context of individuals)

For SAP XM, 2nd Party Data is somebody else’s 1st party data utilized by us, which is an inherent advantage, because SAP has lots of customer data within its systems and if customers agree we could utilize this intelligence pursuant to the constraints, defined by the customers. Some of the systems which could provide this data are:

  • CEC systems:
    • Profile: contextual information across multi-dimensions, multi-account; account specific information (individuals)
    • Marketing: is it still onPremise? If yes, then customer specific information (advertisers)
    • Commerce: user buying patterns from specific eShops / accounts (retailers/advertisers, publishers, consumers)
  • SAP backbone: account information, customer purchasing history etc. (advertisers, publishers)
  • SAP Cloud Solutions & onPremise Solutions run by the advertisers or publishers (S/4HANA, SAP CRM, SAP C4C, SAP CAR, etc.)
  • Networked offerings (advertisers, publishers)

3rd Party Data are curated data sets which are available as packaged offerings to buy or sell. This data is provided by data aggregators who focus on mining offline and online user stores and creating relationships, using various theoretical and practical algorithms. Data aggregators correlate the information on various matrices to create widely different and distinct patterns from the same set of information. Usual examples would be demographic data, contextual or behavioral data and much more.


Typically, we would utilize 3rd party data to supplement our 1st and 2nd party data sets to offer better value to our customers. However, if you would prioritize the 3 broad categories, 3rd party data would bring the least amount of differentiation. At the same time, it’s probably the most well-known data set in the context of advertising, so even though it’s a commodity it needs to be supported by SAP XM already in the 1st iteration.

One of the biggest USPs of Google, FB and other Walled-Gardens is the fact that they have closed user ecosystems. A goldmine of individual information directly interacting with them. Huge amount of 1st party data!

SAP XM has the unique situation of potentially also having access to similar amounts of 1st  and 2nd party data. However, it may be a rather unfair advantage that a lot of the data we have access to is business or transactional data. The open approach allows to connect various data dimensions, in order to create correlations and patterns, and to make use of the data intelligence. We have already established some interesting connections. The initial results of the intelligence we are able to derive, in the context of rich & holistic profiles, look very promising.

But obviously there are still additional challenges. Some identifiers change over time (more or less often) and for some identifiers we will have multiple valid values (e.g. home-IP, business-IP). The quality of data needs to be as high as possible, so we are facing the question how to ensure that. Another open question is which is the best algorithm to be used to create perfect matches? Furthermore, the user needs and behavior change extremely fast. It is important to not get constrained by traversal complexity. Finally, it is crucial to move beyond cookies and even traditional IDs or to even support adhoc type of IDs.

One thing we are absolutely clear about is our open approach that allows us to work with multiple partners, adding to the value we bring to our customers.

The initial work is done. Stay tuned about how we get along and continue to improve digital advertising.

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