Bidstream data—cookie-based data shared by publishers during ad calls—is increasingly becoming the buzziest component of our programmatic landscape. Advertisers eye this data trove as the latest opportunity to improve efficiency and increase adtech transparency.
Advertisers and demand-side platforms (DSPs) want to leverage this data and more to inform smarter bid price decisions, leading more companies to partner with their own data and algorithm providers.
Traditionally, ad call cookie data is limited, including only a handful of online data points like site visits, device type, and location data derived from IP address. While DSPs use this information to bid on impressions and determine price, the data’s original intent is to provide enough information for the publisher to deliver an ad call to the right location with the proper creative format. So why is ad call data leading the charge on bid price decisions when there is much more insightful, complementary consumer data in the market available to optimize bid decisions?
It’s largely because of a troublesome misconception I’ve recently seen where some assume price optimization is already built into auctions via the DSP and market forces. While supply and demand encourage a market of competitive pricing, they do nothing to determine the best price for a specific advertiser and campaign.
When advertisers leverage their own data partner, there’s increased control over the data that’s leveraged, how it’s analyzed and how it’s activated. These additional people-based data points, when customized to the advertiser and objective, could help improve decision-making capabilities and campaign outcomes.
How it Was
Conventionally, advertisers index bidstream data against audience segments. For example, an athletic apparel advertiser may know Chrome users over-index for its target persona of men, age 18 to 25. Thus, the advertiser might increase bids for Chrome users. While it appears that the advertiser uses (and pays for) data-driven decisions and complex adtech pipes to deliver relevant content, the final bid price decision is based on broad demographic data that assumes all men in that age group are equally likely to buy the advertiser’s apparel.
This type of unsophisticated and outdated approach, paid for at a premium price, is inefficient, opaque and unacceptable.
Through data and technology partners, advertisers and DSPs can consider more data and its value to the campaign. At TruSignal, we’ve seen success by leveraging artificial intelligence (AI) and predictive modeling with offline consumer data to analyze first-party data to help determine purchase likelihood.
A DSP can incorporate these predictive data points into the bidding algorithm, or, use conversion indexes as multipliers to adjust base bids. This lowers the bid price for people unlikely to purchase and bids more for likely buyers. The results we’ve seen include performance lift and lowered cost per acquisition (CPA), driving campaign efficiency.
The Data and Modeling
Advertisers often leverage offline profile data and AI-powered modeling algorithms for targeting and other marketing uses, valuing the benefits of scale, coverage, predictive value and accuracy. Bid price optimization is a new application. In the same way “dynamic creative” technology tailors an ad for one person, bid price optimization tailors a bid for an individual, relative to a specific advertiser campaign and KPI.
Bidding algorithms are only as good as the data that informs them, which is why advertisers may hesitate to pay markups for a handful of unverified data points. In our experience, with offline data and predictive modeling, bids can be based on data linked to verified, PII-based, anonymized profiles.
Some DSPs claim to partner with offline data providers, but advertisers must question these integrations to ensure they leverage comprehensive data sets tied to verified profiles.
Transparency and Control
Advertisers can’t control data that’s passed from a publisher. However, ideally, DSPs can build transparency and control between additional data providers and advertisers by sharing details about how bid decisions are made.
I’ve recently seen an influx in advertisers and DSPs seeking out trustworthy data partners to increase data availability in the bidstream to drive more accurate demand-side bid decisions. Unlike the assumed optimization controlled by market forces, the control afforded by data and technology partners drives transparency into data and technology costs that can be used to measure tangible results. While there are associated data costs, the results of using more of the right data drive sufficient efficiencies to result in a return on investment, in my experience.
Without traditional limitations of ad call data, leading advertisers are successfully leveraging offline data and technology to consider more information, increase sales and decrease CPAs. With a transparent, people-based partner, advertisers can understand how the data they pay for is truly effective in making real-time bid decisions about consumers.
Originally published 06/03/2019 in MediaPost.com