Archive | September 2014

Adapting Data into Digital Media

“Adaptation… means watching for the next wave that is coming, figuring out what shape it will take, and positioning the company to take advantage of it. Adaptation is what drives increasing-returns businesses, not optimization.” by W. Brian Arthur, Increasing Returns and the Two Worlds of Business

In essence, this is what we have had to do as an industry as we have grown leaps and bounds in volume, saturation, profitability and capability. Our access and ability to process data has transformed the way that we conduct business, particularly in digital as our media buying has gone online, systems are automated, transactions are algorithmically driven. Data has become part of our every day language and a tool for people to make better, more informed decisions.

In the 1990s, online banner ads were introduced to be bought and sold based on a CPM, the same cost structure that is used in TV advertising. The difference is that we can track whether someone actually saw it and if they clicked on it. This introduced additional metrics — CPC and CPA — that now still exist in other forms of digital media. Fast forward 20 years, other metrics commonly include: attribution, click path, engagement/loyalty, rank, popularity, device type, shareability, value, etc. These metrics have evolved and become more complex over the years as our ability to generate, collect, process and navigate data has advanced. And the people who generate, collect, process and navigate the data have also evolved in the industry as they are multi-lingual, left and right brain, sales and technology savvy, data-lovers and most of all… storytellers.

The demand for data has never been as great and wide-spread as it is vast, across devices, platforms, media plans, content, geographies.

Until recently, data was not expected to be real-time and dynamic; it never needed to be presentable in raw form for general consumption. The handling of large data sets has traditionally been the responsibility of specialists trained in math-related disciplines (applied mathematics, statistics, engineering, and economics) with very minimal exposure, if any, to marketing functions. These specialists were typically on risk management, business intelligence, strategic planning, DBA, or CIO teams where the data was consumed primarily by a few highly trained individuals. Seldom were these individuals successful in translating the data into something actionable on the business side as they weren’t necessarily armed with the business knowledge or context and vision. This translation layer is precisely what is currently in motion, from the technical teams to client-facing roles.

Here are some recent titles that have emerged as a result of this shift:

  • Data Scientists
  • Business Insights
  • Marketing Analytics
  • Data Strategist

Companies want best of breed for both parts of the brain – this is unrealistic and rare to find unless you are Google, Facebook or Apple and can throw silly amounts of money and have a variety of interesting, large-scale projects at individuals to keep them happy get them to stick around.

In the last several years, marketing and other business teams have drastically increased the demand for data in order to make better business decisions, drive business strategy to improve profitability, optimize campaigns, analyze ROI and customer behavior, audience targeting, to create profiles and more relevant experiences to increase loyalty.

The organizational structure of most organizations are still divided into business/sales teams on one side and technology/data on the other. The data strategists and current-day storytellers will often contain elements of both sides — the analytical brain that understands how to sell solutions and ideas, the creative mind that strives towards operational excellence, the client advocate that can identify best practices.

Business teams are not ideally situated to swim through the data even if they are creating much of the demand. Some common challenges that business teams face:

  • there is a talent gap; a sales/marketing team is not likely a top pick for high quality data talent
  • sales/marketing professionals don’t have the time to sift through the data even if they have the skill set
  • the data is usually gated; deeper and broader access to data is reserved for specialized teams
  • the data is usually not architected for sales/marketing consumption
  • requirements for data use evolve as frequently as sales/marketing campaigns; this is also typically what causes friction between data/tech and sales/marketing teams
  • data scientists hired into a business team are often not properly managed, given the wrong incentives and difficult to retain

Technology teams have been completely overwhelmed and stretched