Redefining and Leveraging the Social Web
The industry is adopting fast to the new digital landscape. For the first time in living history the pharma industry is collectively spending more on DTC than professional marketing (51% to 49% according to the most recent Ogilvy report). The allocation to digital in marketing budgets is soaring. Digital is a broad definition and there are many aspects to a digital initiative. I’m going to focus on digital intelligence and how we can use it to deliver context to consumers.
There are two main sources of discovery available to us as consumers – search and social. In many ways these twin engines of discovery have converged. Do a Google search and you are delivered relevant search results to include Tweets. Drill a touch deeper in the results and you will find website comments, discussions, blogs, forums etc. The converse is also very true. Do a search on Twitter or Facebook and you find many results that lead you to a website. The online experience is a social one. I call this as the Social Web.
However, for many marketers the social web changed dramatically this year. In April Facebook deactivated access to its public posts (those available from profiles not marked as private). Some companies were extended on this data feed until the end of October but generally speaking the data was cut off worldwide. (You haven’t missed much as the volume of data has dwindled to a trickle). Facebook replaced the public post data with aggregate topic level data. While more topic data is available in this new tool it is almost impossible to determine its contextual relevance. You just can’t get to that data on Facebook any more. Done and done.
Despite the loss of a popular data feed its important to understand how much more data is available to be leveraged across the social web. Think of all the comments fields that you now find on websites. There’s so much data out there that is frankly richer and more diverse. The big challenges are finding the relevant data, quantifying its relative contextual value and the value of the destination.
In 2015, Liquid Grids extended its capabilities beyond intelligence to predictive multichannel targeting. To do this the company has indexed the most relevant destinations across the worldwide web where consumers are interacting on health topics. For each result the company’s technology is deriving all available statistics about each destination: content keywords and phrases, posts and comment strings, visitor rates, costs per click, competitive website information etc., and then calculating propensity scores for each destination. The result is a rank ordering of the world wide web, including the social networks, for health interactions.
Liquid Grids has also developed a break through in the application of computational linguistics to the content it finds across the web. The technology inspects the content to surface the unique context of consumer dialogue. In this example post “I have a very painful migraine and need to be in a dark room for the rest of the day” the ‘dark room’ is identified as the context linked to the migraine diagnosis and pain symptom.
This combination of highly precise disease dialogue context and predictive targeting parameters is used to develop the best ad content, creative and placement strategies across premium search, display and programmatic buying.
This extension of context intelligence into ad tech targeting is a substantial innovation to improve the way the pharmaceutical industry can ensure its advertising and messaging reaches the right audiences, with the right information, in the context of their interactions on the web and their disease status.