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I just found this excellent piece from Neil Young on Advertising (starting from 0:49*).

“They don’t know that you know what’s on”

How could anyone describe better what Advertising on the Internet has to deliver these days? People learned that the web can listen. Facebook delivers an unparalleled experience of relevance, way better than anything Google ever could and will do. Amazon sells more and more based on recommendations and feedback from users and so do other shops all over the net.

And on the professional side we see a revolution in efficiency and accountability of advertising because we run advertising based on algorithms and platforms that try to figure out what is on and what people might be interested in – adjusting our models and assumptions from click to click.

Basically all what we do is try to act on Youngs statement. We want to create advertising that takes into account that people know what’s on.

*you should also listen to the song, it’s awesome

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We find ourselves at the beginning of the data economy – at least as far as the Internet and advertising are concerned. An ever-increasing proportion of online display advertising is being controlled by qualified data, target groups are being defined and adjusted by means of measured data and websites are being newly created and optimised based on usage data.

Some of the most successful online companies are data companies – Google surely must take first place, but Facebook, Twitter, Yahoo and so on are also very prominent. In addition, many companies who until now ignored and even naively dumped their valuable data are slowly starting to realise what treasures they possess – and are starting to think about monetisation models. This is the case, for example, among online marketers who are beginning to realise during the crisis that they can generate something online that would otherwise require armies of researchers in conventional media: target group data.

Recently, one managing director of a large media agency said in conversation:

“Our future business model will presumably be in working with data and no longer media purchasing and planning”.

Above all, data are not merely new goods that have come into play, but are generally more like catalysts for existing business models. A marketer can suddenly deliver a suitable target group report to a campaign, which contains detailed information on the internal composition of the target group. A media agency starts to use what it already does every day to derive insights and to form generalised models, thus becoming a strategy consultant to the customer. Simple websites that up to now survived only with great effort and slack marketing contracts are suddenly highly valued data partners, because they can help in another way to provide advertising more efficiently. Creative agencies discover the possibilities of making their customers’ websites react intelligently to visitors by cooperating with data partners.

The string of examples could be extended almost at will. In the future, data will become a kind of fuel for the digital economy. Incidentally, the tremendous momentum that exists here can be observed by trying to analyse Google’s success in detail. While there are certainly a whole range of factors that play a role, including a clever bidding system, a couple of important design decisions and so on, the unbeatable strength of this provider is without doubt essentially attributable to the highly efficient generation and use of data – incidentally, not only on user behaviour and the preferences of Internet users, but in all areas of business (such as in the intelligent organisation of computing power, enabling computer centres to suddenly manage without cooling).

Increasingly, the value of digital data is being recognised, and explicit business models that take account of this are emerging. By these means, bluekai in the USA, for example, was able to establish a fast-growing business as a data marketplace, where partners can feed data into it and others can help themselves from the pool for a fee.

Of course, the consistent observance of data protection also plays a very special role, particularly in Europe. This is, however, easier said than done – because the concept of data protection must be constantly adapted to the latest technological possibilities – data protection must grow in tandem and should ideally be considered directly as a central parameter in providers’ business models. But not only that – the attitudes of users with regard to their data is also changing rapidly. Social networks are nothing else than data marketplaces from user to user – and often already from advertiser to user. Data protection concepts must take these circumstances into account; in other words the fact that users no longer need only to be protected, but are also willing to handle their data actively.

Money is data

In its latest edition, Wired carried the headline “Money wants to be free“. The authors demonstrated that existing, bank-based systems of payment were becoming increasingly obsolete on the Internet and that digital business processes are demanding new ways of transferring money from A to B. This should also work with small and tiny sums, should not fall victim to country or currency borders and should also function as quickly as digital society demands. At the same time, money has long since started to become more virtual, ultimately more digital. Of course, this has been the case in the inter-bank business (a term unheard-of 2 years ago) for a long time. But the end consumer is also starting to grow accustomed to EC cash, credit card payments, PayPal and so on. The digitalisation of money leads naturally to a lowering of transaction costs, but also of money transportation and security firm costs and so on – without the user really realising it, since if providers such as iTunes didn’t have devices to bundle many micro-payments, credit cards would actually remain an utterly inadequate method of payment on the Internet. But something is happening on this front: the power of banks and credit card companies is starting to crumble. Just to name two fascinating examples:

1. flattr

flattr is a service that enables voluntary payments for content on the Internet – the user carries a fixed sum and distributes points over the course of a month to offers and texts that appeal to him or her. At the end, the amount paid is distributed to the connected offers. The exciting thing about this is that it is a hybrid payment system that links conventional money flows to a digital currency. Above all, however, the digital currency is self-contained – a click on flattr can mean 10 cents but also 10 euros. There are no interest charges and no administration fees. It is personal and social. Somewhere between cash and smileys.

2. x.com

This service is somewhat more conventional in its approach, but perhaps more explosive as a result. Behind x.com is PayPal’s developer community. PayPal – comparable to the opening up of Facebook in the framework of the F8 initiative – has opened itself to developers and has published an API, including development tools, etc. This means that developers can develop their own applications with x.com, which contain payment functions and virtual currencies. Wow.

Money transfers of the future: an http-link

What these services – especially flattr – make clear, when you think about it, is the fact that “data is money” and “money is data” really are increasingly converging together. What is the difference between me receiving digital money for an http-link containing information that I am interested in a small car and replying to an offer via an http link to say that I would like to “transfer” money to it, as is the case with flattr?

Correct. There is no difference.

In the new data economy, money is just a certain type of data. Nothing more and nothing less.

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Targeting is applied science. You can fill bookshelves with knowledge about targeting, different approaches, technologies, strategies, statistics, key performance indicators, procedures, best practices, target groups, click through rates, conversion, media loss, case studies, etc., etc., etc.

But, as you already might have experienced: Top managers and decision makers are not interested in all these operative details at all. They just want to be “shown where the money is”! For top management you have to:

1.    Describe your company’s business in a simple model, using only the most important parameters
2.    Describe the qualitative impact of your solution on the parameters of the model
3.    Quantify the impact in relation to the required investment (Return-on-Investment calculation)

And this is how it looks like for Predictive Targeting:

1.    Model of your business

There are three major parameters for the monetization of a website/portfolio with advertising:
-    Available capacity (in Ad-Impressions per month)
-    Average realized fill rate (in % of capacity)
-    Average realized CPM (net!, in €)

It has proven to be practicable to split the whole inventory into three segments for a differentiated view:
-    Premium inventory (high CPM, e.g. homepages)
-    Working inventory (mid-range CPM, e.g. channels)
-    Long tail (low CPM, e.g. un-qualified content)

With these 3×3 values any website/portfolio can easily be described. Either qualitatively in a simple graph where the area below the curve equals the monetization:

Or quantitatively in form of an Excel table (fictive figures):

2.    Impact of Predictive Targeting

Predictive Targeting can have a lot of different positive impacts on your business. But there are two major ones under which most of the others can be subsumed:

I.    Increase of reach, e.g. in terms of …
-    increased fill rates
-    prolonged channels
-    a well monetised long tail
-    etc.

II.    Increase of CPM, not as targeting surcharges but e.g. in terms of …
-    new high-value products
-    increased wallet shares
-    increased eCPM
-    etc.

In the diagram the increase of reach leads to a shift of the graph to the right while increased CPMs shift the graph upwards:

The resulting arrow is what nugg.ad calls the Targeting Vector. It’s everything about predictive targeting packed into one single figure (a real KPI!). The area between the two curves finally is the increase of sales (“the money”) you get from Predictive Targeting!

3.    RoI Calculation

Now the impact needs to be quantified. Based on your individual business aims, benchmarks, experience and an analysis of weaknesses and strengths you make assumptions for the impact Predictive Targeting on each of the parameters (exemplary figures):

The result is a comprehensible benefit calculation at a reasonable effort.With these figures you are in the position to make a well-founded decision whether Predictive Targeting makes sense for your business or not. You can put the benefit in relation to the costs of a Predictive Targeting solution and make a Return-on-Investment calculation.Without such benefit calculation you couldn’t even say whether a solution is cheap or expensive. You would purely depend on gut instinct which is no good basis for budget discussions.

I would like to encourage you to take this kind of helicopter view at your business. I promise, it will be very illuminative for you!

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