When working on a complex maths equation with multiple parts, if you use inaccurate data in the initial calculations, you end with an answer that is completely incorrect. Each part of the equation is dependent on getting the first part correct and then carrying the logic through all subsequent parts. Writing exclusively for ExchangeWire, David de Jong, CEO and co-founder, Screen6, explains that if you start with bad data, the answer will be wrong no matter how perfectly you performed the formulas throughout.
The same logic applies to the technology and calculus that support the online advertising value chain. Conversion attribution, frequency capping, and profiling technologies all depend on the accuracy of identity data. If you input ‘waste’, you will naturally output ‘waste’ as well. In advertising technology, ‘waste in’ is incorrectly assigning and understanding individuals and devices and, therefore, never truly understanding or reporting the true reach and effectiveness of an advertising campaign.
Let’s examine a typical advertiser who has deployed multiple technologies across the value chain. Most campaigns include a DSP, an ad server that profiles statistics of reach and frequency, a retargeting technology, creative optimisation technology, and conversion attribution modelling. These platforms and technologies may have developed the perfect individual solutions to solve their one part of the advertising process, but they ultimately all have to work together. The problem today is that, although each can operate perfectly on their own, if the input data on the individual is wrong, the entire process and lifecycle will be affected by the quality of the data.
How wrong depends on the data. When considering the overall effectiveness of the ad tech value chain, one should first think about the three main components of a campaign (planning, execution, and reporting) and how they can be impacted by bad data that does not accurately represent the individuals or true targets. If advertisers take the time to ensure careful planning, execution, and reporting, they will need to input accurate audience and target data leading to a much more successful campaign measured in either increased awareness or conversions.
In 2017, many marketers are still using siloed media plans that contain TV, mobile, and desktop display. TV is being used as a reach builder, while desktop display might be more performance based. You have no way of understanding if they have the same behaviour, so you wind up treating each as a separate entity. Consumers certainly don’t consume media and content in silos. The typical consumer could be sitting on the couch, watching TV, using multiple social media apps on their smartphone, and reading an online article on a laptop in their lap.
If you are planning a campaign you need to consider how you’re reaching your target audience across platforms. Where does your intended audience spend their time online – both mobile and desktop – and what are they watching on TV? Where is the overlap in those media and content platforms and how does it influence your campaign? It ultimately boils down to the data you are using in planning.
To execute and target the ad audiences identified in the planning stage, most marketers today use different vendors to deliver advertising. But you run the risk of vendors not working in sync with each other. One vendor might be delivering retargeted ads across the web based on a target’s actions (website visit, search, etc.). Another vendor might be doing A/B testing and optimising digital display ads. But, if the vendors aren’t working in conjunction, and sharing the same view of the consumer, a tremendous amount of optimisation is lost.
In an ideal ad tech ecosystem, vendor systems would work seamlessly. Retargeted ads from one vendor would be optimised continuously via another ad tech vendor’s optimisation technology. This cycle of optimisation would help drive increased ad effectiveness and relevance.
Reporting & Analytics
A big concern for media players at present is the realness of the ‘people’ who view and engage with digital content. Over-reporting reach of ad views, viewability standards, bots, and advertising on content that is not safe, are all problems. One of the most important pieces of identification is reach: how many people have seen or been exposed to a campaign. Accurate reach data underlies the ultimate success of ad metrics. How many ad dollars were spent? What segments of consumers were targeted? What was the reach of the ad campaign? And, ultimately, what were the conversions, awareness, and how much did we pay per lead or awareness lift?
With better reporting, marketers move closer to determining where every ad dollar was spent and what worked effectively. In addition, with better reporting, marketers and advertisers can better predict the success of future campaigns.
All of ad tech is an interconnected process, as is the relationship between planning, execution, and reporting. The effectiveness of each individual part can be greatly impacted by what kind of identity data you are using. If you don’t feed accurate data into the platform, no matter how invested you are, the outcome will still be incorrect. The ultimate goal in advertising and marketing is to deliver a relevant ad to a consumer with purchase intent or delivering ad creative to a potential customer to raise awareness of a brand, product, or service.
A key factor in achieving that goal for marketers and advertisers is to know what, when, where, and to whom their ad was served. The bedrock in achieving advertising/marketing success is the advertising planning and audience identity and targeting. If an advertiser can hone in on their key audience, and is able to target that audience across media and advertising platforms and silos, they have a much greater chance of achieving marketing success.