How Unstructured Data Is Redefining Display Advertising Part 1

Display Advertising“Premium data” is a term that is tossed around by both networks and publishers frequently. This type of first rate data is employed to create higher fees and demand for advertising on various website properties.

In its most basic definition, premium data is often described as the webpages that tend to generate the largest volumes of traffic. Sometimes the term is applied to instances where an advertiser has the ability to customize their available ad space to suit their particular needs. There are many other benefits associated with premium data, but it is generally defined using these simple attributes.

In the last several years, the traditional concept of acquiring demographic data has been changed by new technologies and advancements. This new data, when combined with real time bidding, is going to enable a marketer to pick and choose banner placement and its associated value based upon real time data about the targeted demographic. An example of using this technology would be retargeting.

For example, let’s say that you visited a website. You placed four items in your cart for purchase, but you left before the sale was completed. 3-4 hours later, you may be reading an old LiveJournal entry from high school or browsing through a random IMDB page and you see several ads for the items you did not purchase earlier. This is retargeting, and in that particular moment, you seeing this ad is invaluable for the snubbed retailer. It is your actions, rather than the webpage itself, that is defined as premium.

Increasing Amount of Data, Increasing Amount of Potential

While ensuring that individual privacy remains intact, consumer browsing habits are better known today than ever before. There are a number of different data elements obtain for the purpose of effective display marketing, including browsing device, operating system, browser, recently keywords, etc.

With marketers having such a wealth of knowledge on their hands, common sense would assume that display campaigns, targeted at new demographics, would perform as well, if not better, retargeted campaigns directed as current visitors. The wealth of known knowledge would support the decision to purchase an ad and to influence its content as needed.

Moreover, publishers could potentially benefit from the knowledge that the individuals who browsing through their website maintain increased worth as their history prior to visiting increases the value of that consumer when on their website. Much to their detriment, most audience-centric, new display campaigns are generated through networks that fail to take full advantage of this audience data.

What’s Gone Wrong?

The honest truth is that the majority of companies created within the last ten years have not been properly equipped or prepared to deal with the era of big data. Legacy technology cannot accept pure, raw data. Instead, it must manipulate variable audience attributes into a type of format that lowers the value and overall effectiveness of the data many times over. This technique is often referred to as custom audience segments.

To use an example, let’s say you have some raw data…some cookies that have told you the exact keyword’s someone is looking for and what types of websites they visited. These individual elements are employed to categorize the individual user into a custom audience segment. The appropriate campaign is launched, and the standard optimization techniques are deployed. The problem arises in that performance data, bid decisions, etc. are made based off of post impression data.

Tomorrow, we’ll discuss the missing element and how unstructured data has become a game changer.

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