With all of the latest advances in digital media, we are still in the infancy stage when it comes to data intelligence and informative decision making. Consider what is currently happening in the display advertising space. Data and media are continuing to diverge as two separate commodities. On one hand there are data exchanges making cookie files or user information available for purchase. On the other hand, there are media exchanges that make media available for purchase letting businesses use primary or secondary cookie files from other sources to make the ad decision.
This new divergence between data and media, coupled with the notion that bigger is better when it comes to data, makes for an interesting dynamic in today’s industry. Companies are looking to build their own, large cookie pools. In some cases, these are companies with no real technology or mathematical expertise. The natural assumption is that by having large data sets, it would allow for informative ad decision making and that assumption is absolutely warranted. But again, we are only in the infancy stage when it comes to data intelligence and information decision making so it is important to understand all layers involved while pursuing the “data” path.
There is an entire meta-layer between data and media, and that layer is around delivery and optimization. It’s the ability to marry delivery, performance, and backend metrics with data collection and custom audiences. Without this connection, data is meaningless. Imagine for a second you spent $5,000 on the latest and greatest Flat Screen High Definition TV, $600 on the latest Blue Ray DVD drive, but decided to use old audio/video composite cables instead of investing the extra $150 on some good HDMI cables. Your investments in the television and the Blue Ray drive aren’t even close to maximized unless an additional investment is made in the “connection.”
This is unquestionably one of the most important, yet most overlooked aspects of today’s ecosystem. Data is only as valuable as the intelligence or “connection” behind it. In the coming months, the companies that are positioned to efficiently collect and segment data and, more importantly, are able to tie that data in a meaningful way to media through enhanced delivery and optimization techniques, will see an increase in sales, margins, and ROI. By valuing audiences and media separately, there is also a new arbitrage opportunity available to those that truly understand the three aspects of data, media, and delivery.
At the end of the day, companies will need to make smart investments on technology vendors and third party solutions in order to help achieve their goals. As the economy continues to shake out, as costs and expenses continue to be cut, and as resources continue to be reallocated, it is all that more critical for companies to make sound investments that contribute to increased efficiency and productivity. In order to do this, companies will continue to turn towards “data” in order to make informative decisions, to both business and literal ad serving.
(Disclosure: The post can also be found at Lotame Learnings. Lotame is my current employer)
Data is King. If you don’t believe me, consider this:
Rental car companies and insurers are refusing service to people with poor credit scores because data mining tells them that credit scores correlate with a higher likelihood of having an accident.
Nowadays when a flight is canceled, airlines will skip over their frequent fliers and give the next open seat to the mine-identified customer whose continued business is most at risk. Instead of following a first-come, first-serve rule, companies will condition their behavior on literally dozens of consumer-specific factors.
The “No Child Left Behind” Act, which requires schools to adopt teaching methods supported by rigorous data analysis, is causing teachers to spend up to 45 percent of class time training kids to pass standardized tests. Super Crunching is even shifting some teachers toward class lessons where every word is scripted and statistically vetted.
In the world of Online Advertising, marketers, web publishers and technologists alike are beginning to realize that data is hugely valuable. In the past, advertisements were served to websites based on the content of the website. Today, advertisements are served to websites based on data that is informing the ad server to show that advertisement. As a result of this paradigm shift, the digital media community is beginning to realize that media and data are separate commodities. Where you see your advertisement is very different than how or why that advertisement is there in the first place.
Being able to leverage the power of this online data is great — but what does it really mean? Let’s take a look at the type of data that is available for digital marketers.
There are two types of data we should look at.
Singular Data Points
Multi-Variable Data Points
A “Singular Data Point” is a piece of data that is very cut and dry. It is a binary 1 or 0, “yes” or “no.” For example, if I recently went to an e-commerce travel site, configured a flight for 2 people from NYC to LA, and clicked “view price”, then you could make the argument that I am very interested in flying in the near future. The instant I click the “view price” button, a single data point can be obtained classifying me as “someone interested in domestic travel.” This may hold a ton of value for marketers at the bottom of the advertising funnel (see diagram below) who are looking to convert on a very specific type of user for a very specific product, and has been, still is, and will remain extremely valuable. Some examples of “Singular Data Points” include: Purchase Intent or In-Market Data, Age & Gender and Household Income.
These are data points that explicitly and intuitively describe certain consumer attributes. They are more or less declared characteristics or definitions of a consumer.
“Multi-Variable Data Points” however, tell a very different story. Imagine for example that someone, who happens to be female, 24 years old, and makes $40k/year, went to a social network, checked her inbox, wrote on a friends profile page about last night’s TV show, left that site to read and comment on an article about the New York Giants, uploaded a video about comedy, checked the price of a flight from NYC to LA, and finally, viewed another friend’s photo album who happened to have just returned from a trip to LA. What does this social data say about that user? Nothing? Is it completely arbitrary and meaningless? In fact, it’s exactly quite the opposite. Being able to leverage “dozens of consumer-specific factors” in real time gives a marketer the capability of executing and deploying various tactics such as:
Influencer outreach strategies
Creative optimization for advertising units
Deeper visibility into brand engagement opportunities
Custom optimization for various and wide ranging back end performance metrics
Statistical data analysis and research
Comprehensive search strategies
Informative and cost effective media buys
Moreover, this social data presents huge opportunities for marketers looking to reach consumers at the top of the advertising funnel, but still has applications toward the bottom of the funnel as well.
Today, there is much debate around the value of social data vs. purchase intent data. How and when it should be used? How much should each cost? How should it be sold?
If we look at social data and purchase intent data as it might be applied in the advertising funnel, it would probably look something like this:
Now let’s consider social data and purchase intent data in terms of value vs. time. If we consider our previous example, we can make the argument that purchase intent data is hugely valuable for a short period of time. If I am looking to book a flight within 2 weeks, that does not necessarily mean I would like to travel a year from now. For a travel related company, that 2 week data point is again, hugely valuable.
Now if we consider our social data example, by demonstrating undeclared and implicit behavior over an extended period of time, we can make the argument that this data has tremendous value indefinitely. If we were to graph social data vs. purchase intent data on a value vs. time graph in today’s environment, the graph might look something like this.
But as companies become even more sophisticated, develop better applications of technologies and data mining, then someone’s day to day behavior might prove even more valuable in the future. This is certainly true when it comes to “creative optimization.” It is the idea that a creative ad unit showed to a consumer will be directly linked to their “social data” and that the consumer GENERATES OR INFLUENCES some form of content, message, conversation, or engagement. “Consumer Creative” – or “Social Creative” is the next leap of value to marketers, and as we evolve in the creative evolution of social media/data, the value vs. time graph will probably look something like this.
At the end of the day, having the ability to make educated decisions using comprehensive data sets is what will differentiate businesses from their competitors. It will give the forward looking organizations a way to stay relevant, efficient, and strong, while other companies continue to use outdated and inefficient methodologies.
And as these technologies evolve, become more sophisticated, and create incremental value for marketers, web publishers and consumers, the “data” companies will have a responsibility to uphold the privacy rights of all parties involved. These safety and privacy measures will become, and are already, inherent features of the technologies, because with the power of data comes the responsibility to collect and use the data in ways that provide appropriate protections for user privacy. Responsible industry members will continue to develop practices and policies that can work for marketers, publishers and consumers in this arena (we are one of those companies).
Nevertheless, if companies and marketers want to make the best decisions possible for their clients, they should consult the data for the answers and throw the guess work away. In our world of digital media, find your exact target audience and save money on wasted impressions or eyeballs. It’s all about the data, as well as harnessing the power of consumer driven information.
“My name is Jamie Varon and I’m living in Danville, CA (if you haven’t heard of it, no worries) right now, hoping to move into San Francisco soonish, rather than laterish. The sole purpose of this site is simple: I want to get hired at Twitter and the only way to stand out in this competitive job market is to do something unique.”
I’ve heard many stories lately from people my age that have either lost their job, are about to lose their job, or haven’t found a job in the first place.
Something tells me that simply submitting resumes online and wearing a suit to interviews (if your lucky) isn’t going to cut it anymore. Time to think way outside the box and do something that demonstrates your expertise in your field. In the case of the recent graduates, many of us haven’t had enough work experience to substantiate a worth while track record.
So what to do?
Things I might do if I wanted to be in…
Finance: Invest a small amount of money and demonstrate that you have the ability to earn good returns. Percentages not dollars. See Stocktwits
Real Estate: Find a building you think is worth buying. Asses its value. Record its cash flow. Demonstrate why and how you know this is a good deal in a mini business plan.
Sales People: How much time do you spend with your clients discussing numbers, data or research?
Analytics & Quant People: How much time do you spend interfacing with clients?
The line between being a sales person and being an analytics/quant person is fading and fading fast.
Technology has enabled anyone and everyone with access to huge amounts of information at any given point of time and more importantly, has allowed them to access this information in real time. As a sales person, this makes differentiation that much more important especially in these tough economic times. How are you different from your competitors? What data can you not only show me, but what does that data mean?
In these situations, the tendency has typically been to rope in analytical individuals that are best suited to answer these questions. These are the individuals that are experts on the data and interpretation relevant to clients and their organizations.
Even so, can these individuals convey the data in the capacity needed to maintain, build and grow relationships? Furthermore, can the “relationship” people convey the data needed to grow business and establish meaningful credibility with a client?
I recently had conversations with a close friend of mine who works in the finance industry. His description of the sales-quant/analytics relationship relates directly to my experiences in the digital media industry. Numbers and sales are becoming one and it is becoming increasingly important to have skill sets in each discipline.
This begs the question…Is it better to be an expert in sales with the ability to understand data and analysis? Or, is it better to be an expert in data analysis, interpretation, and reporting with the ability to convey an overall, hierarching theme?
Both are critical and both are necessary in today’s environment. Today, you must become a salanalytic (Sales + Analytics = Salanalytics) but if you had to choose one skill set, which one would it be?