Ad Tech 101: Tackling the data challenge

Pippa Chambers
By Pippa Chambers | Sponsored

This is a free educational print out from the AdNews October issue. To get it hot off the press download a digital version of AdNews or subscribe to the premium print edition here.

The rapidly growing demand for high-quality data offerings among brand advertisers shows no sign of slowing down. The advantages of layering audience data into the digital media buy is also proven to cut waste, boost brand awareness and conversions. However, advertisers can be baffled about delving into data-led marketing because of the complexity brought by this rapid convergence of media, data, and creativity.

Advertising has always been about understanding and reaching the right audience and increasing its effectiveness. This is usually down to selling more products and increasing the customer base. While traditional media did this effectively by exploiting mass media, with advertising technology it is now possible to use vast amounts of data to improve the targeting, efficiency and conversions.

Keeping on track

  • Data is necessary but not sufficient. Knowing who to target is important, but it is not enough. It is also crucial to know when (purchase funnel management), where (context and placement), how often (frequency) and how much to pay (auction strategy). Separating the data from the machine learning and bidding makes the dynamic optimisation required in today’s real time advertising world impossible.
  • Data volume matters. A massive amount of it is needed to make targeting in real-time advertising work. It is easy to gloss over the mind-boggling statistics associated with display advertising, but here’s a helpful rule of thumb: If you are not leveraging petabytes of data to make each targeting decision, you are missing the true power of real-time advertising. 
  • Data freshness matters. Less data means dated insights and a greater sensitivity to cookie deletion. Serving the right ad at the right time based on the latest insight makes a considerably big difference to sales. 
  • Targeting models are only as good as the amount and quality of data against which they train. Many vendors can provide lookalike modelling but they are not all created equal. The amount of training differentiates the world-class from the mediocre.

In a nutshell

Andrew Double, Quantcast ANZ managing director, says it’s essential to remember that not all data is equal and it’s vital to work with companies that have invested in their data and technology and are constantly improving.

With the test and learn factor being key, Double says Mediasmith, the independent agency based in the US, ran an experiment last year to understand how data partners can deliver efficiently to their advertiser’s target audience. The WSJ covered the story and the findings showed that of the 11 vendors tested, four provided no real uplift - in fact when they tested the accuracy of the data, some were just as bad as flipping a coin.

“Audience targeting accuracy varies among channels and audiences and it is now possible to validate this and measure against scale,” Double says.

“In reality the ANZ market is just at the beginning of testing technology and hopefully this will happen with more rigor and velocity.”

Click the image below for the PDF to print out or simply save and file the page:

ad tech Nov issue pdf


Want to know more about Quantcast? See here.

For more 101s check out:
Ad Tech 101: The Marketing Cloud

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