These days, it’s all about artificial intelligence — especially when it comes to the advertising ecosystem. Yet, as AI becomes more and more integral to advertising, the ecosystem needs to adjust accordingly to keep up with technological advancements. Specifically, several best practices need to be implemented in order for an AI-driven ecosystem to truly thrive.
Before I dive into that, let’s backtrack to how exactly AI is affecting the advertising ecosystem. In my last blog, I wrote about two highly beneficial applications of AI in advertising: predictive and prescriptive analytics. From choosing the right media mix allocation, to optimizing audience strategy by channel, AI can help marketers dramatically improve their strategies, saving them valuable time and money.
In order for AI to function as effectively as possible in these ways, it is paramount to follow some best practices for an AI-driven ecosystem. Below are the key best practices we at Cuebiq have identified to make a prolific and ethical AI-driven advertising ecosystem a reality.
Creating a Healthy Big Data Ecosystem
For AI to be effective, there needs to be a healthy underlying data ecosystem. Even though each brand will have its own data stack, based on their needs and resources available, all will need high-quality, privacy-compliant data at scale.
You may be wondering, what makes data “high-quality”? First, the data cannot be biased — it needs to be representative of all groups of people, not limited to a certain pool. To avoid bias, the data must have scale and breadth. What’s more, the data must be collected in a privacy-compliant manner, ensuring transparency, consent, and control for the end user, and accountability for the company managing the data.
As follows, defining best practices for the algorithms behind AI will be critical. This goes hand in hand with establishing guidelines for which data should be taken into account to fuel the AI, both ethically and effectively.
Shifting From an Audience Taxonomy Mindset to a Data-Driven, Prescriptive Mindset
Once a healthy big data ecosystem is established, there needs to be a tectonic shift toward a data-driven, prescriptive mindset. What does that mean? Well, beyond fueling the programmatic ecosystem, AI can also enable a shift to longer-term KPIs and consumer lifetime value. Through predictive analytics for example, algorithms can determine which consumer segments will become high lifetime value consumers, which is more accurate than choosing audiences “a priori.”
As an example, by leveraging machine learning, Cuebiq discovered that high lifetime value consumers of a leading retail brand don’t visit locations as often as might be expected. How could that brand use this insight? Once they know that these consumers don’t visit as often, they can then use offline intelligence to analyze if the consumers go somewhere else between visits. They might learn, say, that these consumers come to their store for big-ticket items but visit another retailer for smaller purchases. With this information, they can then identify specific tactics to drive this segment to their store more often. This is just one of many ways a brand can turn insights from offline intelligence into actions that improve their marketing strategies.
AI-powered insights like this will enable the shift from what I refer to as the current “audience taxonomy mindset” to a real-time, data-driven prescriptive mindset. The beauty of a data-driven, prescriptive mindset for segmentation and targeting is that the AI constantly updates its analysis according to audience behaviors and refines its predictions.
In order to achieve this paradigm shift from the “audience taxonomy mindset” to longer-term KPIs and consumer lifetime value potential, a framework to guide advertisers and publishers would be helpful.
Collaborating With the Walled Gardens
Additionally, for an AI-driven advertising ecosystem to be truly effective, brands will need to have a complete picture and unified measurement for all of their efforts, including the walled gardens. A unified metric for campaign performance will give advertisers the ability to measure consistently and effectively across all platforms, bringing the power of AI-driven recommendations to their full potential.
To realize this, an open dialogue with the walled gardens will be crucial. By collaborating with the walled gardens, those in the rest of the advertising community will be able to unlock the full potential of comprehensive and unified measurement.
If we can successfully implement these best practices, we will be on a win-win path for both end consumers and the advertising industry, empowering advertisers to engage in more relevant conversations and serve better ad experiences, while preserving consumers’ desires and their right to privacy and transparency.
You can learn more about Cuebiq’s commitment to privacy here.