In my last blog, I mentioned that I have been thinking about the perfect Digital Place-Based planning and buying platform, and discussed the major functions that the tool would have to possess. I began the discussion by talking about Network Education, a pre-planning functionality.
In this post, I will discuss the Planning function of the tool.
First, to review, the tool would have (7) major functions:
1. Network Education
7. Historical Program and Trend Tracking
PART 2: PLANNING
This is probably the most challenging phase of the process, with so many moving parts and such vast amounts of information to organize in order to prepare the strongest programs. The tool would not only be able to digest and organize information, it would also help strategize programs. Additionally, one of the tenets of this tool is that vendor information would be pre-loaded into the system. There would be real-time, automated, regular updates of vendor inventory, offerings, and specifications. The tool would virtually eliminate the cumbersome RFP process, streamlining planning into an organized, instantaneous program-building endeavor.
The tool would easily be able to target nationally, by region, by state, county, zip code, neighborhood and proximity/radius to individual locations at the press of a button or a couple mouse clicks. All of this information would be stored within the system and accessible almost instantly. In this first phase, the tool would show the media buyer all available DOOH/DPB options within the requested geography. Spotted maps will be generated instantly.
Demographic data will be robust and readily accessible. Male/Female ratios, age groups broken out as specifically as possible, along with ethnic/racial breakouts, income levels and education would further target networks that the media buyer is seeking. These demographic data points could also be adjusted: There could be a toggle that the user could adjust either direction to target certain demos. (i.e. if he/she is seeking 60% male/40% female he sets the switch accordingly).
c. Census Data:
Census data down to Zip Code, County, DMA, and even neighborhood/district. It would include population counts, population density, ethnic/racial counts and percentages of each within the selected geography. For example, the buyer could select only zip codes or counties that have a greater than 30% density of Hispanic inhabitants.
d. Psychographics & Audience Research:
Here is where the tool would be a game-changer. Each network has its own, unique audience. If each network’s research existed in a real-time way within the tool, these audiences could be matched up instantly. The buyer could ask the tool to include only networks that over-index for a required subject. For example: What is this network’s Index against “Playing Golf” or “Buying $1000+ watches.” Instant answers to such questions would greatly streamline the planning process.
e. Increased Functionality:
The tool would also have the data and be able to include networks’ added functions such as NFC capabilities, Mobile Interaction, Social Media integration, AR, 3D, AVI and all of the other capabilities that networks will soon possess.
f. Venue-by-Venue Targeting:
Yet another way in which this tool could turbo-charge the process (and the industry) would be venue-by-venue targeting. This does not simply refer to geographic targeting venue-by-venue. This would mean that each individual location of every network in existence would be researched, audited and quantified individually for all of the above factors (i.e. race, ethnicity, income, gender, geography). Let me explain: Suppose there is a Fitness Center network, and across the entire network the HHI is $100k+. But what if you are only looking at a certain gym chain in a certain market? Or what if you are only looking at one specific gym? What is the makeup of that specific audience? What are its particular demographics, psychographics? Another example of venue-by-venue targeting would be in bars/nightlife venues: What if you wanted to target “college” bars exclusively. This program would entail more than simple proximity to college campuses—it would have the less tangible factor of being specifically for college students. Such granular info would be incredibly powerful. Taking this idea one step further, not only would each individual venue be researched and assessed according to audience, but they could also be judged according to the venue’s message-conveying ability: Placement of screen, impression effectiveness, recall for that location would be information that would make a strategic buyer salivate.
g. TRP’s and GRP’s.
If applicable, the network information that is built over the course of the planning stage would accurately reflect ratings points, so that the media could be directly compared to television in terms of reach and cost.
Stay tuned for my next entry: Buying.