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AI Readiness in Digital Marketing

AI Readiness in Digital Marketing
AI Readiness in Digital Marketing
AI Readiness in Digital Marketing

We get it – Artificial Intelligence (AI) is the “new shiny object.” But, what are some practical applications of AI for marketing purposes? The IAB webinar on December 14th, presented by AdTheorent, Xaxis, and Time Inc., illustrated a number of use cases of AI for marketing purposes from both the agency and the publisher’s perspectives. You can view the webinar recording here.

With the goal of continuing the conversation, Asiya Vorontsova, Senior Director of Business Intelligence & Yield at Telaria, shares her thoughts below on three areas on which marketers should focus to leverage AI to create and maintain a competitive advantage.

As volume of digital content and user data continues to grow year over year, marketers are asking themselves the same question: how can all this new information help us make smarter decisions and improve ROI (Return on Investment) of advertising campaigns?

While automated processes, such as programmatic ad buying, help speed up the transaction between an advertiser and a publisher, many decisions are still controlled by human evaluation, even if supported by sophisticated statistical tools. This human involvement delays decision making, which inevitably results in opportunity costs. Such costs are only going to increase as the amount of data continues to grow and further challenge human capacity to respond in precise and timely manner. Integration of artificial intelligence (AI) methods into the automated (or programmatic) ecosystem will help reduce, and in many cases entirely eliminate, the opportunity cost associated with human decision making and will empower all participants to make optimization decisions faster while further maximizing their ROI.

In the context of this discussion, AI represents the capacity of a non-human entity to perform actions that simulate human behavior and improve over time based on goal-seeking rather than pre-programmed responses to particular inputs.

If one chooses to adopt AI in digital marketing, it might follow a similar data-driven optimization approach as is typically employed by human analysts. It is often characterized by the presence of a feedback loop, bringing together campaign performance data from various sources and applying strategy to incrementally optimize campaign delivery.

AI readiness in Digital Marketing

Marketers looking to incorporate AI capabilities into their processes should focus on the following three components:

  1. Data Collection and Retention.
    The output can only be as good as the input. In the course of a marketing campaign lifecycle, various types of metrics that are relevant to assessing campaign’s efficiency can be collected and stored, ranging from reach and cost to audience demographics and content placement. Incorporating them into campaign’s performance optimization decisions is challenged by a lack of common definitions, formats, and units of measurement across data providers or simply limitations in their availability for external consumption. This is where the work led by IAB and IAB Tech Lab on establishing industry standards, such as developing the OpenRTB protocol for programmatic advertising transactions or defining industry KPI (Key Performance Indicator) measurements, helps harmonize and normalize data sources to elevate the quality of the resulting data-driven decisions.
  2. Algorithmic Strategy and Execution.
    Various algorithmic strategies might be effective in the particular circumstances of certain campaigns. They share a modern runtime environment that enables their execution, while maintaining its separation from the logic of each individual campaign optimization strategy. Both, the underlying technological capability of algorithm execution as well as the algorithms themselves, require substantial investment and are a key differentiator for market participants. Each investment might take the form of building the capability in-house, by establishing an internal team, or of partnering with third-party providers.
  3. Strategy Evaluation and Optimization.
    While there are already tools that can make some optimizations in real time, many decisions often take days to evaluate before strategy changes are deployed. Streamlining result evaluation has the potential to reduce time to market on campaign changes, thus eliminating wasteful spend and achieving higher KPIs.

To the extent that marketing teams embrace the above enablers, they will be positioned to deliver on the efficiency promise of AI for their organizations. Those who succeed will drive above-market ROI for their business.

 

View Webinar Recording