BOSTON, Dec. 21, 2015 /PRNewswire/ -- One of the promises of buying digital media programmatically is scale, unlimited users at a high frequency and low cost of media. Today, when running Direct Response or performance based campaigns, retargeting is still the best performing tactic because programmatic is very efficient at finding and converting users who have already shown intent. However, retargeting has its limitations mainly that it doesn't scale beyond users that have visited your webpage.
So, how can you scale your programmatic advertising? Is there a better way to improve ROI beyond retargeting? How do I find more consumers that will convert efficiently?
Marketing Data: History
Since the beginning of marketing science, marketers have built their marketing plans and strategies using research to identify 'personas' of their consumers. The data that goes into building these personas is often one dimensional (developed through surveys and by analysts). This means that the analysis considers one or fewer moments of aggregated data and doesn't analyze the consumers holistically. In the digital advertising world, each of us as consumers, sit in more than 100 industry data segments (i.e., living in Boston, male, purchase intent, etc.) on average. Traditional marketing personas only account for a handful of segments like demographics, Geo, household income, etc.
Marketing Data: Now - Consumer Persona
Through our data science research, we have discovered a way to make marketing personas more accurate, using machine learning and applying data science to available 1st, 2nd and 3rd party data. This is an impossible achievement with human analysts, there are just too many data points for any analyst to process to make a meaningful output in near real time.
How Do I Find New Consumers?
Stop rinsing and recycling your audience! Instead, you want to first identify all of the data associated with your consumers, this means all of the 100 or more segments based on multiple attributes (e.g., surfing behavior, time of day, type content consumption, etc.) Then apply multiple data science models and algorithms to identify a "Persona" that captures these complex user behaviors and places them in a machine generated audience Persona Graph. The audience Persona created will be based on dynamic, real-time consumer behaviors, unlike the static personas or the traditional look-a-like models. Once the users and user behaviors are identified, predictive algorithms can be applied to assess the value of the persona and the value of each user in real time. The end result is the ability to identify potential "new consumers," not recycled users. And knowing the value of the user will allow you to target effectively and efficiently, improving performance.
Humans are really good understanding and remembering pictures. So, logically, advertisers would want to see consumer behaviors associated with their campaigns. Historically, this has been a difficult problem in data science - the ability to visually represent an algorithm output. To solve this problem, we created an easy to understand visualization for consumer behaviors that are important for a campaign or advertiser.
We also looked at probability of conversion. What we found was a machine generated persona captures complex behaviors and responds to data signals in near real time. Thus, increasing performance.
Insight to Action
At Digilant we are transforming the static marketing persona concept into a data science driven 'Consumer Persona' using all the data signals available. This is one step further in the direction towards the promise of programmatic -- extending audience reach and enhancing campaign effectiveness using what we know best, data. See examples on our website: http://www.digilant.com/digilant-consumer-persona-programmatic-data-product-what-is-it/ or talk to a Consumer Persona expert by reaching out to firstname.lastname@example.org.