Brittany Wray: Get Ready for a Cookieless Future with AI Analytics | Podcast 31

Brittany Wray: Get Ready for a Cookieless Future with AI Analytics | Podcast 31

Interview by Nikola Djuric
Published: January 26, 2024

The 31st episode of the DesignRush Podcast brings a conversation between our senior editor Vianca Meyer and Brittany Wray, an experienced media professional specializing in AdTech operations and Data Analytics.

Tune in to the full episode that's now available on YouTube and Spotify to discover:

  • How AI-driven predictive analytics can enhance audience targeting and optimize your campaigns
  • Why omnichannel solutions are crucial for effective advertising strategies
  • When will contextual advertising and computer vision take over the advertising world
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Who Is Brittany Wray?

Brittany is the Senior Director of Platform Solutions at AudienceX, bringing a decade of experience managing AdTech Operations for numerous business clients. She leverages her extensive knowledge of demand-side platform training skills to support and educate AudienceX's sellers and clients. She graduated with a Bachelor’s degree in Marketing from Pennsylvania State University.

Here's the first part of Vianca's conversation with Brittany, edited for clarity.

Vianca: How would you explain AI-driven predictive analytics in simple terms?

Brittany: Analytics is fundamentally about collecting data to identify patterns for decision-making.

Predictive analytics extends this by modeling data to foresee future outcomes. Adding AI involves automating this process with machine learning and other techniques.

Together, they form the backbone of AI-driven predictive analytics.

At Audience X, we leverage predictive analytics to enhance our clients' campaigns. This approach allows us to better understand client audiences, anticipate their actions, and refine advertising strategies.

We use our AudienceX intelligence suite, or AXi, a collection of AI-powered, privacy-safe data science tools. AXi comprises three main components:

  1. The Predictor tool, which creates audiences using AI and machine learning
  2. The Optimizer, which customizes campaign decisions for targeting the right audience at the optimal time
  3. The Explorer, which provides audience and media reporting through predictive analysis

These tools collectively use AI and machine learning to drive successful outcomes for our clients.

The primary advantage for our clients is getting more value for their investment. The AXi suite is designed to tailor tools and campaigns to each unique initiative our clients undertake.

Flexibility is a key feature, allowing individual or combined activation of different components of the suite. This enables us to customize our approach precisely, aligning with the specific requirements and objectives of each campaign and initiative our advertisers are executing.

Can you explain the concept of integrated AdTech and how AudienceX takes advantage of this technology?

Integrated ad tech refers to the use of various advertising technologies to create a unified system for managing and optimizing digital advertising.

It's like a Swiss army knife with different tools, each serving a specific purpose but all part of a cohesive whole. These tools include demand-side platforms for programmatic display, native video, search and social platforms, data partners, and supply vendors.

At AudienceX, integrated AdTech is our essence.

We are integrated with major enterprise-level DSPs, search and social platforms, select supply vendors and data providers, and an industry-specific CRM and order management system, among others.

A specific example is our Explorer tool, an omnichannel tool that harmonizes data across multiple platforms and channels, presenting a unified view of campaign performance against targeted audiences.

What market trends have you noticed in the digital advertising industry recently?

The most notable trend across all industries, including digital advertising, is the use of generative AI.

Generative AI plays a crucial role at AudienceX, although it's just one aspect of our broader AI utilization. We have a dedicated team that sets the foundation for the responsible use of generative AI, considering the various rules and regulations. This technology aids in guiding ad content and product descriptions.

However, we don't solely rely on the output of generative AI. Our teams take this AI-generated content, modify it, and tailor it to fit each brand's unique voice and requirements.

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Can you give us an overview of AudienceX's omnichannel solutions and why they're so effective advertising strategy?

Omnichannel is about reaching your audience across various interconnected channels.

At AudienceX, we ensure our clients can connect with their target audience through multiple platforms like social media, webpages, mobile apps, and even physical billboards.

We integrate omnichannel solutions by having expert media buyers across different channels, a creative team crafting consistent messaging, and our Explorer tool, which offers cross-channel performance analysis and media mix modeling.

This approach helps in allocating spend efficiently and understanding the impact of one channel on another.

The primary benefit is consistent messaging across different channels, which is crucial for brand recall and engagement. This consistent exposure can guide a potential customer through the marketing funnel, from awareness to consideration and ultimately conversion.

For example, display ads can introduce your brand, native ads can push consideration, and search ads can seal the conversion. Omnichannel marketing is about strategically guiding customers step by step through this process.

The effectiveness of omnichannel marketing can be attributed to its ability to reinforce brand recall and capitalize on a person's mindset at different times. For instance, a person might see an ad during work but not engage with it due to the context. Later, the same message seen on a different channel, like social media during leisure time, might prompt action.

This strategy plays on the human tendency to remember repeated stimuli, especially when presented in varied contexts that align with the individual's readiness to engage.

AudienceX is all about programmatic advertising. Can you tell us more about that?

Programmatic advertising is essentially about using technology and data to automate ad buying in real-time.

Unlike traditional TV ad buying where schedules are built and purchases are made upfront, programmatic advertising utilizes technologies that make split-second decisions to display ads to the right audience at the right time.

It's a more dynamic and responsive way of advertising, contrasting with the upfront, scheduled approach of traditional methods where you don't see results until after the campaign. It boils down to a combination of sophisticated algorithms and precise targeting. Different forms of targeting, like third-party behavioral or contextual targeting, play a role.

For instance, using our predictor audiences built from a large, consented behavioral base and first-party data, we target those who don’t just look alike but act like converters.

The algorithms process millions of data points beyond human comprehension, optimizing campaigns based on numerous attributes like channel, device, browser, site, and audience. These algorithms can adapt strategies based on time, ensuring that the ads are shown when they are most effective, which is a capability beyond the limitations of human analysis.

How is AI-driven predictive analytics reshaping programmatic advertising?

Traditional audience targeting often struggles with inconsistent or unreliable data, difficulty integrating with first-party data, imprecise audiences leading to wasted ad spend, and the phase-out of third-party cookies.

AI algorithms can analyze massive data to identify customer behavior patterns. Our predictor tool, for instance, uses machine learning to address these pain points.

What's unique about AI-driven segments is their ability to optimize during a campaign, constantly adjusting the audience based on real-time data, which traditional methods don't offer.

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