Category: AI

Ethical AI and Data Privacy: The Cornerstone of Modern Marketing

Posted on by Coegi

Tenured marketing leaders have witnessed firsthand the transformative power of AI in our industry. AI has revolutionized how brands understand and engage with their customers. From predictive analytics to personalized content creation, the possibilities are endless. However, with great power comes great responsibility. It’s critical to proactively integrate ethical AI practices and data privacy compliance into the core of your marketing operations to ensure you are maximizing the technology while minimizing the risk involved.

Our Three-Pillar Approach: Privacy, Security, and Transparency

At our agency, instituting ethical AI practices and compliance with data privacy regulations is integral to all our AI-driven initiatives. We recognize that as technology advances, so must our commitment to responsible use. Our approach is built on three key pillars: Privacy, Security, and Transparency.

Privacy: The Foundation of Trust

We begin by assessing and classifying all data and documents according to their sensitivity level. This allows us to implement the Principle of Least Privilege, ensuring that staff members only have access to the data necessary for their specific roles and responsibilities. By limiting access, we minimize the risk of unauthorized data exposure or misuse.

This granular approach to data management not only protects our customers but also streamlines our operations. It allows us to maintain the delicate balance between leveraging data for insights and respecting individual privacy.

Security: Extending Best Practices to AI

We’ve taken the robust IT principles and SOC 2 compliance standards typically applied to traditional data systems and extended them to our work with generative AI. This includes:

  • Authorized Access Requirements: We have stringent protocols in place to determine who can access AI systems and for what purposes, reducing the risk of misuse.
  • User Authentication: Multi-factor authentication and regular credential updates are mandatory to verify the identity of users interacting with our AI systems.
  • Data Loss Prevention & Encryption Standards: We employ robust encryption for data both at rest and in transit. Our data loss prevention strategies ensure that sensitive information cannot be accidentally or maliciously exported from our systems.

By applying these enterprise-grade security measures to our AI operations, we’re creating a secure environment that fosters innovation while protecting sensitive information.

Transparency: Building Trust Through Openness

We believe in being open about our use of AI. This means communicating to clients and stakeholders about where and how AI is used in our processes. We also ensure that AI-generated content is distinguishable, maintaining the authenticity of human creativity where it matters most.

This transparency not only builds trust with our customers but also helps us stay accountable to our ethical standards.

Evolving with the Landscape

Our ethical AI framework is not static; it evolves with the technology and regulatory landscape. We regularly review and update our policies to align with the latest developments in data protection laws such as GDPR, CCPA, and others relevant to our international operations.

Beyond policies and frameworks, we invest in ongoing training for our team to ensure everyone understands the importance of data privacy and the ethical implications of AI. This cultivates a culture where responsible AI use is not just a policy, but a shared value.

The Future of Ethical AI in Marketing

As we look to the future, it’s clear that the successful marketers will be those who can harness the power of AI while maintaining the highest ethical standards. By prioritizing privacy, security, and transparency, we’re not just complying with regulations – we’re building a foundation of trust that will drive long-term success in the AI-driven marketing landscape.

The path forward requires constant vigilance, adaptation, and a commitment to ethical practices. But for those willing to invest in responsible AI use, the rewards – in terms of customer trust, brand reputation, and innovative capabilities – are well worth the effort.

3 Ways to Improve Marketing Campaigns Using AI

Posted on by Jacob Amann

Are you using artificial intelligence for your marketing? 

If not, you’re likely spending unnecessary time and effort launching and optimizing advertising campaigns.

Which creative assets are best for this audience? How much should I bid for a particular ad placement? Who is my target audience? 

AI can help you answer all of these questions, minimizing guesswork and assumptions. 

How does AI boost marketing campaign performance?

To distill it down, AI uses algorithms to sort through data using a set of rules to complete automated tasks. To function optimally, the algorithm needs a goal to optimize towards. So it’s up to humans to tell the machine what that goal is and if the campaign is succeeding or not. These guardrails are necessary to ensure actions aren’t taken that create cost reductions, but are actually detrimental to marketing goals. 

With this strategic foundation in place, artificial intelligence can significantly improve campaign performance, save time, and increase efficiency.

Here are the top three ways you can use AI to improve marketing campaigns:

1) Campaign Optimization

The most prolific use for AI in marketing campaigns is for media buying automation and optimization. 

Automatic bidding allows media buyers to place appropriate bids for each ad placement in the open market. This ensures you are reaching your target spend levels and pacing evenly across campaign durations. 

Artificial intelligence tools can also optimize pre and post bid to improve campaign performance and learnings: 

  • Pre-Bid Optimization: Analyze site visitation patterns and social media following before a campaign launches for faster learnings and more accurate targeting.
  • Post-Bid Optimization: Pull insights from millions of data points to understand what worked and what didn’t. These learnings provide a roadmap for improving future campaigns and identify tweaks throughout the campaign. 

These automations allow you to focus on meaningful data and strategic thinking. Use them to repurpose your time to more strategic tasks while platforms manage the day to day operations. Look for anomalies and trends in the data, but let the AI do the heavy lifting. Then you can take a more inquisitive approach to determining the “why” behind the data points.

2) Audience Targeting 

The second way you can use AI to improve marketing campaigns is by expanding and refining audience targeting. Using AI, you are able to expand your first party and/or pixel data to find more people who look like your existing customers. Whether it be similar media consumption behaviors, demographics, or other behavioral attributes, this technology can find people most likely to convert. 

In a post-cookie world, AI-assisted targeting will be particularly important. Many brands will have less complete customer profiles due to less availability of consumer data. There will be an increasing need for AI to rapidly test various targeting tactics to find your best audiences in a way that’s both measurable and efficient. 

3) Creative Optimization

Finally, creative optimization is another impactful way to leverage artificial intelligence. Consumers are demanding more personalized, exciting moments from brands. To drive action, ads need to be hyper relevant to your niche audiences’ motivations, but also break through the clutter of other “personalized” ads. 

Dynamic creative optimization is an AI feature that delivers the optimal combinations of creative imagery and headlines to individuals in real-time. This takes the guesswork out of the creative process while improving ad personalization. By letting computers sit on top of the data, you can gain valuable insights into what creatives drive the most impact among core audiences. 

Here are a few tips to make the process of exploring AI go a little smoother:

Bonus AI Marketing Tips

  • Build a strategic foundation: AI saves time and resources, but there’s no point in capturing data if you don’t know your key business objectives.
  • Test and learn: The digital world changes fast. Use data in real-time to make decisions and see if things are working – and if they’re not, to make smart pivots.  
  • Remember the human element: We can’t forget feelings, relationships and human interaction. Make sure human insights from your team and your audience are still part of planning and optimization processes. 

If you’re a marketer, start getting more familiar with AI. Don’t expect to get it all right at first. Ask questions and talk with experts in this space. Start learning and move forward from there.

To work with Coegi and leverage our Data & Technology experts for your brand, contact us today. 

AI Optimization – The Paperclip Theory

Posted on by Jacob Amann

Paperclip Maximizing: Machine Learning And The Problem Of Instrumental Convergence

What do paperclips have to do with digital marketing and machine learning? Admittedly, pretty much nothing. The ‘paperclip maximizer’ thought experiment comes from Nick Bostrom at Oxford University. In essence, it looks at the idea that if you tell a machine to optimize to a specific goal it will do so at all costs.

If you told a machine to maximize the number of paperclips it produces the machine would eventually start destroying things like computers, refrigerators, or really anything made of metal to make more paper clips once other sources of metal run out. This concept has been coined as instrumental convergence. 

Paperclip vs Pay-Per-Click

If you transform the idea of paperclips into the idea of paying per click this becomes very relevant to digital marketing. Nowadays, almost every platform touts some version of AI or machine learning to revolutionize campaign performance, which is a boon to everyone.

By releasing control to machines, media buyers can focus on more strategic tasks such as identifying deeper insights for reports and understanding clients’ goals while campaigns continuously improve themselves. They do this by finding and optimizing for what works while avoiding the things that are not driving performance for the brand. 

See this in action on Our Work page. 

Defining Performance

What exactly is ‘performance?’ The easy answer is whatever your KPI may be. It could be clicks, it could be video views, it could be any trackable metric. However, this is already a simplified goal. If you are running a traffic campaign, the marketing goal should not just be to “get more clicks”.

The goal should be something designed to move the needle for the business – brand affinity growth, sales lift, etc.  For instance, driving qualified users from a target audience to an advertiser’s website and increasing brand favorability is a strong goal. This is something bigger than a single metric. A KPI can be a stepping stone and an important indicator of success, but it is not the final objective.

A truly successful campaign will not simply be the campaign that drove the most clicks at the cheapest price point. Success lies in the campaigns that drive true performance towards core business objectives.

Looking for a partner to harness AI technology and drive marketing performance? Reach out to Coegi for a discovery call today.

What Machines Lack

Context. Context is something that a robot has not yet mastered.

As a marketer, I know that increasing clicks, directionally, should push me closer to my goal. The machine knows this too, but this is all the machine knows. It will endlessly optimize to a single goal.

Maximizing the number of clicks given a fixed amount of budget. This can lead to unintended consequences – think back to the paperclip example.

A machine might say only run display banners and forget about high impact formats such as video and CTV. The machine might push 100% of impressions into in-app environments. It would say never buy another out of home ad again. The machine would never know to build brand awareness, because there is no optimization point it can use. 

Machines Need Guidance

We understand the role of the media buyer is not going away, but it is morphing. There is a new symbiotic relationship between buyer and machine which empowers them to maximize your brand as a whole. Successful media buyers do not need to spend 80 hours per week finding every winning media combination.

Most of these tasks can be done through harnessing technology and freeing up time to look at the media plan from a higher level.  These benefits of using AI go directly to our clients in the form of more time dedicated to listening to client needs, smarter digital media plans, and ultimately higher performing campaigns. 

How To Incorporate Smart AI In Your Media Buying

  • Release media optimization controls to AI machines and spend time on strategic campaign elements. 
  • Define performance success beyond the metrics by establishing meaningful campaign goals 
  • Use context to avoid instrumental convergence and potentially harmful optimizations from unattended machines.

Click the button to view our full YouTube playlist on AI for Marketers.

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