The late senator Daniel Patrick Moynihan from New York once said, “Everyone is entitled to his own opinion, but not his own facts.” This great aphorism also extends to businesses seeking to use data more effectively.
The digital world has created so much data as consumers “click” through their lives that we are now talking about it in terms of both “big data” and “small data.” (See the ClickZ column “Big Data and Small Data: Where the Twain Meets.”) Data introduces immense complexity if only because of its sheer volume, but the underlying issue can be put simply. We need to get better at using data for evidence-based marketing decisions, rather than opinion-based decisions, which may, in some cases, be not much more than hunches.
An acronym cited recently at the Predictive Analytics Innovation Summit in San Diego, California, speaks volumes about this dynamic: HiPPO stands for “Highest-Paid Opinion in the Room.” HiPPO decisions are common when data is not understood or available. Think about how a marketing campaign gets put together. Let’s say it’s an email campaign slated for select customer segments with certain buying behaviors across specific geographies. But what went into the decisions to build the campaign? Were they largely intuitive? Was it built based on last year’s campaign performance? If data was used, what quality was it? And if it involved analytics tools and disciplines, how effective was the analysis? Was the impact of social media on paid media performance considered? Did the marketing team have the ability to monitor performance and optimize marketing mix during the course of the campaign? And, how was campaign effectiveness assessed?
These are not idle questions. In this data path, we have a number of disciplines and methodologies required to gather, combine, and analyze data, and then deliver it in formats to effectively support decisions by marketing leaders and their teams. We don’t expect business people to run IT systems or data scientists to create marketing campaigns and produce creative materials. But we can and should begin to look at the connections and interfaces among these key disciplines – and explore how best to transform the complex into simple for broader accessibility and usability.
I recently watched the opening of a truly dazzling celebration of science hosted by astrophysicist Neil deGrasse Tyson in the TV series Cosmos: A Spacetime Odyssey. No need to be a scientist to watch and enjoy! The series has been created to communicate complex and highly sophisticated science in simple, dynamic ways to people of diverse educations, not unlike its widely successful predecessor hosted by Dr. Carl Sagan of Cornell, my beloved alma mater, in the 1980s.
I understand a high-budget television series is not entirely analogous to our situation in the marketing department. But the success of Cosmos does underscore the vast possibilities that open up when material developed by people with sophisticated technical and analytical skills is communicated effectively.
Where are the important points of communication in what we might call the data “lifecycle”? Data analysis involves action by three distinct professional disciplines. And for the most part, they are not disciplines that speak easily to each other. We might even say that there is, at times, a “failure to communicate.”
- The IT team: The technical folks who track, gather, and distribute raw data
- Data scientists and data analysts: The highly trained people who apply sophisticated statistical and analytical methods and tools to make sense of data
- Marketing leaders and their teams: The decision makers with business and creative skills, who in the end are responsible for driving revenue
Data that eventually lands in a marketing team’s dashboard may start out as raw streams captured by the IT team. Data scientists and analysts need to know what this data supplied by IT really means. That’s the first interface. In addition, marketing teams and data scientists and analysts need to carefully define what they want from the data. Data often fails to produce insights for decisions because of how requests and projects get translated between data analysts and marketing teams. That’s the second interface.
What’s clear in the history of technology is that when we give attention to the interface, we find ways to simplify complex processes and information. Think of the smartphone. Few of us know how a smartphone really functions, but even children use them because of the years of R&D committed to building the interface between the underlying technology and that person holding the phone.
In the realm of data analysis, I believe we need to give careful attention to the interfaces among the key groups and disciplines that are involved in managing, analyzing, and using data. I’m talking about much more than just simple visualization of data, of course. It’s in the analytics technology, and the processes and approaches connecting people in these very different professional disciplines.
What’s at stake is bringing people together across these bridging interfaces to deliver the best possible results. That requires attention and is certainly not easy. But I know for sure that negotiating these interfaces will turn out to be one of the key ways we enable data-driven decision support for marketers everywhere.
Marketers create personas to better understand their target audience and what it looks like. If marketers can understand potential buyer behaviors, and where they spend their time online, then content can be targeted more effectively.
What’s behind a successful data-driven marketing strategy?
One of the major challenges in the martech industry is getting the attention of prospects in a world where they are bombarded by content and emails on all sides.
Facebook is addressing one of the biggest missing pieces of its chatbot offering: analytics.