Alain-PortmannAlain Portmann is co founder of Web Liquid.

Last year I headed over to my local cinema and bought a ticket for the matinee showing of Moneyball, the story of Oakland A’s general manager Billy Beane who challenged the status quo of baseball by applying a data driven approach to talent recruitment, player rotation and management. While not a follower of baseball, I was intrigued by the impact that systematic data analysis had – and continues to have – on both America’s favourite pastime and the world of Digital Advertising I joined in 1996.

moneyballThe value of the Moneyball story as it applies to my day-to-day job isn’t simply about the use of data analysis. The real lesson is about continually informing the decision making process we undertake to generate return from marketing investments.

While the challenge of informing investment decisions has always been a part of advertising – from George Gallup introducing research techniques in the 1930s to the development of yield management tools for online publishers – it has acquired a real sense of purpose and scale through such methods as Real Time Bidding (RTB).

Big Data comes hand in hand with a Big Story

Billy Beane’s players and staff did not adopt his statistical methods until he took the time to tell each of them a story around the numbers. The A’s front office not only recorded and analyzed every pitch thrown to Oakland A’s hitters, but related every plate appearance to a hand of blackjack; the tone of it changed with each dealt card (pitch).

The same applies with online media and the footprint of data we deal with every day. When numbers acquire the significance of language, they are able to do all things which language can do: become persuasive and tell a story.

I personally have found hypothesis planning to be quite effective – framing a commercial challenge on the basis of a series of story based assumptions that are continually tested and validated through data.

Real Time Bidding has magnified the challenge of good storytelling in our industry, be it through the growth of Data Management Platforms or aggressive data monetisation strategies by publishers. The most important challenge, though, comes from the ability to transform, interpret and visualize data to make better decisions.

The relative value of experience.

moneyball2At the heart of the Moneyball story is the premise that experience is over-rated. Put simply, the collective wisdom of baseball insiders (including players, managers, coaches, scouts, and the front office) over recent decades was subjective and often flawed. I dare say the same applies in our industry.

In the current ecosystem, we no longer can afford to use our ten, fifteen, twenty years of experience as the single point of differentiation.

The democratization of information, the demand for interoperability and the changes in structures, resourcing, skills and culture, forces us to continually learn and challenge ourselves – not through our past experience but with our ability to anticipate behaviour.

Anticipating behaviour is hard as a baseball manager or a marketer – human beings do not always act on or make rational decisions. However, behavioural economics can provide some valuable clues and insight. And while behavioural economics demands a separate posting (given the significant breadth and scope), it shows by and large that every decision people make (be it as a baseball player or a consumer) is massively affected close to the moment of decision by the context in which they decide.

The value of information sources

moneyball1Moneyball chronicles not only the initial indifference of baseball insiders to statistical analysis, but also the progressive availability of baseball statistics. For decades, baseball statistics were collected through rudimentary score sheets and box scores controlled by a handful of organizations such as the Elias Sports Bureau.

The creation of STATS Inc., which aimed to “set down the primary events that occurred in a baseball game as completely as possible” started to change things. Faced with low adoption by the organizations that should have embraced this new body of knowledge, STATS Inc. gave up on trying to sell the data directly to teams and began selling it to fans.

The evolution of baseball statistics mirrors the evolution of audience and inventory data in online media. In the early days, audience and inventory data was owned and controlled by individual publishers and research organizations that were more likely to approach clients directly. Fuelled by RTB, the availability and volume of audience and inventory data today has grown exponentially.

Yet issues still remain: while we have access to more data, questions about who owns the data are yet to be addressed; audience data is not easily transferable between Data Management Platforms and Dynamic Creative Optimization platforms; a large portion of audience data U.S based (a concern when dealing with global clients); no standardisation of data (classification and taxonomy) across multiple data sets, and there is limited social media audience data at hand. Increased access to quality audience and inventory data provides us with two fundamental benefits – the ability to generate new data and inform decision making about investments.

The “power of no”

Billy Beane and the Oakland A’s created a competitive advantage through better decision making – statistical analysis allowed them to exercise the “power of no” over the market by cherry picking undervalued talent based on their own set of criteria.

RTB provides us the same “power of no” – by allowing us to purchase on an impression basis, and completely bypass on impressions that do not meet our own specific criteria and goals.

Buying at the impression level also means de-averaged pricing, allowing buyers to set individualized price parameters for each impression based on expected return. The ability to refine our buying decisions – taking full advantage of the impression “long tail” – is only the start of the process. The real challenge is building scale and sustained return on investment across time.

Focusing on the metrics that uncover hidden value

moneyball3The principles adopted by Billy Bean in Moneyball were first developed by Bill James, the author of The Baseball Abstracts, a book that unknowingly became the scriptures of the Sabermetric movement that has shaped the landscape of player evaluation in baseball. James questioned the value of using highest batting average to judge the success of a team, player or manager, so he set about developing an alternative metric that would recognize the single most important objective of an offense – the number of runs a given player was able to generate or contribute to.

Even more valuable than the equation itself was the realization that most baseball organizations did not place enough value on the actions that led to a run such as walks and extra base hits.

The same dynamic applies to the way in which most online media investment objectives are measured and managed. When discussing and planning return on investment goals, for example, most organizations ignore the importance of the conversion path in achieving that return. While considerable progress has been made in segmenting the value of prospecting vs. re-targeting investments, attribution planning and analysis seemed to be relegated to analytics reporting as opposed to marketing plans.

The challenge with attribution planning is that there is no single best model or “one size fits all” formula: it requires a bespoke model that fits the dynamics of each individual organization and the behaviour of its customers. Return on investment is the sum of individual actions, and those actions must be identified, attributed and more importantly continually optimized to uncover value.

Article Source:

Portada Staff

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