Is The Price Right? Allow The Data Used In Programmatic Marketing To Provide The Answer
Predyctive Programmatic Marketing: Let The Offline Data From An Artificial Intelligence Platform Provide You With The Answer
Programmatic audience targeting increasingly has become riskier and these days does not provide as much of an increase in performance the way that it used to. There are many vendors these days that provide competitive (as well as comparable) ways of targeting consumers based on information that is available via third-party exchanges. After many years of innovation, a new maturity level has been reached when it comes to audience targeting. These days marketers are searching for new ways of gaining an edge, which includes using people-based marketing. One chance of finding an edge is through applying the same information that is used for segmenting audiences – which includes the data that is collected offline – outside of audience targeting, in order to determine what is the correct price to pay. This has resulted in bid optimization emerging as a new way to innovate with display advertising, where many of the exact same tools can be used that are utilized with the well-proven audience segmentation world. There isn’t as much discussion about using technology and offline data for determining accurate bidding. Looking across the industry what I see is most of the effort still being devoted toward utilizing offline technology and data for segmenting audiences, while this same offline technology and data continue to be comparatively underutilized in terms of using it to optimize bid prices.
Bid Price Optimization: The New Differentiator
Who wouldn’t want to decrease their cost per acquisition (CPA) while increasing conversions rates, without needing to change their targeting? There are too many people who make the assumption that when it comes to real-time bidding (RTB) that this type of optimization is baked in already to the auction format and that the forces of supply and demand will ensure we pay the best price at all times. That isn’t true. Bid decision-making, in reality, isn’t just based on the market forces of supply and demand but also on algorithms, and these algorithms can only be as good as the information informing them. Typically with audience targeting, we layer in offline data, first-party data, and other sources of proprietary information. However, bidding algorithms do not do this. There is a reason reasonable explanation why this advantage is seized upon by marketers: the methods used to determine RTB prices are very opaque when compared with those that are used with fixed targeting audiences. Fixed targeted audiences have clearly stated costs, but RTB actual pricing is determined usually by black-box algorithms. It gets aggregated into an average CPM after the campaign is over. We don’t usually learn why some impressions are valued more highly than others are or whether or not somebody is pulling their prices from thin air. This results in us failing to understand all the ways that we can optimize bid prices further. Additional transparency could help with addressing the frustration. However, it is more likely that adoption will be driven by business outcomes. There is a good reason to believe this will happen. Programmatic marketing allows the marketer to hit the people whoa re ready to buy your services or products at this very moment.
Imagine online click activity showing a consumer group who are have a high interest in washing machines. They would all be valued equally by current targeting. A Demand-Side Platform (DSP) then could access various cookie data points – like search keywords, time of day, device type, and site visits – in order to determine impression bid prices. Now imagine that there is additional information from offline sources (for example, credit card information) which shows which individuals are more likely to buy. A more informed marker who has access to these offline data points can adjust their based bid, lower their bid prices on individuals who are less likely to buy and repurpose these savings and use them on individuals who are more likely to buy. A new car shopper is another favorite example of mine. Plenty of people who are looking to buy a car will browse websites to search for them. They send verifiable and strong signals of their intent to make a purchase. More generally, their demographic over-indexes with owners of new cars. The problem here is that they aren’t equally valuable, and their value should be reflected in the bidding.
Acquiring More Data In Bidding
More data brought to bear on bidding. When you target the right audience, you will spend more money on individuals who matter more while spending less on those who matter less. This will result in average cost-per-action decreasing and conversions increasing. The same is true for bidding. This is better for those marketers using more data as part of their bid decision process in order to understand who they spend less on and who they should pay more to get.
People Based Marketing
As marketers are very well aware of, conversion indices, offline data, and first-party data are all critical for audience targeting. Something they may not be aware of is that these data points along with others are pertinent as well when it comes to determining how much should be paid for impressions. As third-party exchange data continues to become increasingly commodified, it is much easier to locate the right individual for receiving your marketing message. Over the past 5-10 years, we have reached this point through the programmatic marketing evolution. Now we need to focus on expanding to pay the correct price for the impression. In most cases that can be done by using the data we already have. However, a lack of transparency – along with believing in market dynamics to some degree – has left bid price optimization largely unaddressed. For marketers, focusing on this can provide more accuracy and efficiency.
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