Author: Jacob Amann

How to Strategically and Compliantly Use First Party Data for Regulated Industries

Posted on by Jacob Amann

In healthcare marketing, particularly under strict regulations like HIPAA, the use of first-party data (1PD) demands a nuanced, strategic approach. Leveraging 1PD responsibly and effectively can unlock significant value while maintaining compliance. Healthcare marketing must prioritize privacy at every turn, with specific measures to protect individual rights:

  • Explicit Consent: Always secure explicit consent for using any Personally Identifiable Information (PII) in marketing communications. This is especially vital in highly regulated industries like healthcare, where patient trust is paramount.
  • Data Anonymization: Anonymizing or tokenizing PHI (Protected Health Information) before utilizing it for any marketing purpose is crucial. This ensures that even in data-driven decision-making, no individual’s privacy is compromised.

So where do you start?

Here’s how Coegi recommends maximizing your use of 1PD to strike:

1. Non-PHI Data Utilization

The foundation of responsible 1PD usage lies in focusing on non-PHI data, ensuring patient privacy is never compromised:

  • Social Followers and Demographics: Voluntary, non-sensitive data such as social media engagement and publicly available demographic insights can serve as a starting point for audience segmentation. This data can be analyzed to create preliminary target personas, ensuring that marketing is tailored to audience interests without venturing into sensitive areas.

2. Lookalike Modeling with Tokenization

To unlock deeper insights from first-party data without compromising privacy, tokenization must be applied before any modeling begins:

  • Tokenization First: Before conducting any analysis, customer data is tokenized, ensuring personal identities are anonymized. This enables the organization to securely harness insights without exposing PHI.
  • Modeling High-Value Clients: Post-tokenization, focus on identifying high-value clients based on their Lifetime Value (LTV) and other non-PII metrics. By refining marketing strategies using these models, businesses can allocate resources more effectively toward customers likely to drive higher returns.
  • Cohort Targeting: A cleanroom environment provides a secure way to apply cohort-based targeting while maintaining compliance. This enables marketing teams to group customers with similar characteristics and behavior patterns for more personalized messaging.

3. Index-Based Matching for Lookalike Audiences

Lookalike modeling is essential for audience expansion, and index-based matching offers a compliant, privacy-conscious method:

  • Interest and Propensity Data: By using index-based matching, organizations can match interests and customer propensities without direct data matching. This allows for effective targeting while protecting personal data.
  • Building Lookalike Audiences: Insights from tokenized data, combined with index-based matching, enable the creation of lookalike audiences, allowing businesses to broaden their reach to potential new customers with similar characteristics to high-value clients—all while adhering to strict privacy regulations.

4. Aggregated Campaign Optimization and Analytics

Optimizing marketing campaigns through data analytics is key, but it’s critical to ensure compliance throughout the process:

  • Automated Targeting: Machine learning can help automate and continually refine targeting strategies by analyzing past campaign performance. By doing so, organizations can keep their messaging relevant and impactful, based on real-time insights from previous campaigns.
  • Conversion Rate Analytics: Aggregated, anonymized data should be used to assess and optimize conversion rates, keeping campaigns both effective and compliant.

Conclusion

By strategically using first-party data in compliance with privacy regulations, healthcare organizations can maximize the impact of their marketing efforts. The approach of focusing on non-PHI data, using tokenization, and applying lookalike modeling ensures the organization maintains patient trust and regulatory adherence, while driving more targeted and effective marketing campaigns.

Want to learn more about how to maximize your first party data? Contact us to schedule a discovery call. 

Unleashing the Power of Data Velocity

Posted on by Jacob Amann

Imagine you have a state-of-the-art garden hose connected to an endless supply of water. Sounds fantastic, right? Here’s the problem: unless you turn the handle and direct that water where it’s needed, your garden won’t grow. 

Data in today’s digital marketing landscape is just like that water – abundant and full of potential, but only valuable when we put it to work.

In today’s marketing world, data velocity is that crucial turn of the handle. It’s not enough to have a database reservoir filled with information, sitting idly; you must build insights from this information with speed and efficiency. Both are critically important in being able to channel that data to nurture your customer relationships, grow operational efficiencies, identify and act on market trends, and see your business reap its benefits. 

The Data Dilemma: From Stagnant to Strategic 

Just as a garden without access to the water won’t grow, data that remains untouched in a database, no matter how vast, cannot influence decision-making, improve processes, or provide return on investment. To harness the true value of your data, it’s important to: 

Activate Your Data:

Breathe life into raw information by transforming it into actionable insights. It’s in this transformation – whether through business intelligence, machine learning models, or insightful reporting – that your data’s true value emerges, empowering your team to make informed, impactful decisions.

  • Implement robust ETL (Extract, Transform, Load) processes to clean and structure your data.
  • Leverage advanced analytics tools to uncover hidden patterns and predictive trends that shape strategy.
  • Focus on the insights that directly influence your marketing strategies. Whether it’s identifying customer segments, optimizing ad spend, or tracking campaign performance, prioritize data that can be directly applied to your decision-making process.

Prioritize Relevance:

Not all data is created equal – the value of data is inherently tied to its context and use case. Focus on the data points that truly matter for your specific goals.

  • Master the art of data wrangling as a critical process. Clean, structure, and enrich your raw data to transform it into a goldmine of insights by combining disparate data sets, filling in missing values, or normalizing data for consistency.
  • Incorporate third-party data strategically. External data sets can provide valuable context, but crucial to recognize that not all third-party data sets are universally valuable. Some datasets you can reference range from demographic data to search and social trend data to weather data. For instance, weather data might be incredibly valuable for a retail business that needs to anticipate changes in consumer purchasing behaviors in terms of likelihood to visit store locations or which products that may be of greater interest. Yet, for a technology company, weather may hold little value compared to demographic data. Which leads to the next point…
  • Don’t fall into the trap of data hoarding for the sake of information gathering. This can cause unnecessary analysis paralysis that leads to insignificant outcomes. 
  • Streamline Access: A state-of-the-art garden hose can’t deliver clean, pressure-perfect water if it isn’t paired with a perfectly engineered plumbing system. 

The same is true of your data. 

You must produce data feeds that will serve as your digital plumbing, allowing team members to simply “turn the handle” and instantly access the insights they need.

  • Develop a data governance framework that balances security with accessibility. Create role-based access systems to provide team members with the data they need, when they need it.
  • Invest in user-friendly interfaces that make data exploration intuitive for non-technical staff. Platforms like Google Analytics, HubSpot, and various CRM systems offer dashboards and reports that make it easier to interpret data and apply it to your marketing efforts.
  • Encourage curiosity to uncover new data-driven marketing opportunities. Regularly review your data feeds to see if there are any new trends or patterns that could benefit your campaigns.

The Velocity Advantage: Why Speed Matters in Marketing

Timing is everything. Data velocity in marketing empowers you to:

Respond to Market Shifts in Real-Time

  • Monitor social media sentiment and search trending topics to adjust messaging on the fly
  • Use real-time bidding data to optimize ad placements and budgets instantly
  • Quickly pivot campaign strategies based on emerging competitor actions or market disruptions

Personalize Customer Experience

  • Leverage real-time browsing behavior to serve dynamic website content
  • Implement AI-driven recommendation engines that learn and adapt with each customer interaction
  • Use location data and purchase history to deliver hyper-targeted mobile notifications and offers

Optimize Budget Allocation

  • Implement automated budget reallocation based on real-time performance metrics
  • Conduct rapid A/B tests to quickly identify winning creative and copy.
  • Use predictive analytics to forecast campaign performance and adjust spend proactively.

Empowering Marketers: Identify Ways to Help Cross-Department Teams Tap Into Data

As a marketer, you don’t need to be a technical expert to put data to work for your campaigns. What’s important is understanding how to leverage the data that’s been prepared and made accessible to you. 

 Here’s how to get started:

Seek Out Your Power Players:

Work closely with your data and analytics team to identify which data sets are most relevant to your marketing goals. Explain your campaign objectives clearly so they can help you pinpoint the data that will provide the most actionable insights.Together, you can:

  • Conduct a comprehensive audit of your current data sources and analytics capabilities
  • Align key performance indicators (KPIs) with overall business objectives
  • Develop a prioritized list of metrics that directly influence marketing decisions

Invest in Agile Analytics:

Implement tools that provide real-time insights and easy-to-use dashboards.

  • Evaluate and adopt cloud-based analytics platforms for scalability and real-time processing
  • Implement machine learning algorithms to automate data analysis and predictive modeling
  • Develop custom dashboards that provide at-a-glance insights for different roles within your organization

Foster a Data-Driven Culture:

Encourage your team to make decisions based on insights, not just instincts.

  • Provide regular training sessions on data interpretation and analysis tools.
  • Celebrate data-driven successes to reinforce the value of this approach.
  • Implement a system for sharing insights across departments to break down data silos.

The Bottom Line: Data Velocity Drives Value

In the race for market growth, it’s not just about having data – it’s about how quickly you can turn that data into results. By focusing on strategic data velocity in marketing, you’re not just keeping up with the competition; you’re setting the pace. 

Ready to turn on the full force of your data and watch your marketing flourish? Don’t let your valuable data sit idle – contact us today to start your journey towards data-driven marketing excellence. 

It’s time to turn that handle and let your data flow!

Navigating Privacy Regulations In The Dynamic Pharma Landscape

Posted on by Jacob Amann

From the patchwork of stringent state laws to the nuances of consent in patient data usage, explore the critical elements that organizations must adeptly navigate privacy regulations to ensure ethical and legal adherence in this dynamic pharma landscape.

State of Privacy Regulations in the United States

Data privacy laws, especially those related to healthcare, are subject to frequent changes at both the federal and state levels. At the federal level, the Health Insurance Portability and Accountability Act (HIPAA) is a key regulation governing patient data privacy. However, there may be additional federal laws, state laws, and other enforceable guidelines that impact healthcare marketing. Staying updated with these changes is important not only for following the law but also for maintaining top-level privacy and trust in healthcare.

Divergence at Federal and State Levels

Within the federal landscape, HIPAA serves as a fundamental regulation, offering baseline protections for Protected Health Information (PHI). However, beyond HIPAA, various factors contribute to the evolving regulatory environment. The CARES Act, with its temporary modifications to HIPAA, introduces additional considerations for handling health data during emergencies. The FTC continues to play a crucial role in enforcement, ensuring that entities adhere to privacy standards.

On the state level, the regulatory landscape introduces a patchwork of stricter laws that organizations must consider. States like California, with the California Privacy Rights Act (CPRA), Colorado with the Colorado Privacy Act, and Virginia with the Consumer Data Protection Act, have implemented comprehensive privacy laws. These state laws grant patients various rights over their data, necessitating organizations to establish robust opt-out and data deletion processes to comply with diverse state-level requirements. The existence of these stricter state laws adds complexity for entities operating across multiple jurisdictions, requiring them to adapt their practices to align with varying privacy standards.

Difference in Patient and Provider Marketing

Patient marketing operates under more stringent restrictions due to the involvement of sensitive health data. The use of PHI necessitates careful handling and compliance with privacy regulations. Organizations engaging in patient marketing must establish clear opt-in and opt-out mechanisms, allowing individuals to express their preferences regarding the use of their health information. Transparency about how data is utilized becomes paramount, ensuring that patients are informed about the purposes for which their information is being used. This transparency not only aligns with regulatory requirements but also builds trust with patients, a critical factor in healthcare marketing.

In contrast, marketing efforts directed at healthcare providers may have less stringent regulatory requirements concerning patient data. However, ethical considerations and data security measures remain crucial. While there may be more flexibility in the approach to provider marketing, organizations must uphold ethical standards to maintain trust within the healthcare ecosystem.

Compliance Strategies

Principle of Clear and Informed Consent

The essence of clear and informed consent is embodied in four key attributes:

  • Freely given: No coercion or undue pressure.
  • Specific: Clear explanation of data usage and sharing.
  • Granular: Allow patients to choose what data is used and shared.
  • Revocable: Easy opt-out mechanisms.

Opt-In Methods and Opt-Out Mechanisms

Opt-in and Opt-out methods are pivotal in healthcare marketing, offering an ethical way to engage individuals by obtaining their explicit consent prior to using their information for marketing purposes. 

Opt-in Methods:

  • Require obtaining explicit consent before using information for marketing.
  • Align with clear and informed consent principles.
  • Allow individuals to express willingness to receive promotional materials or participate in initiatives.

Opt-out Mechanisms:

  • Important to protect sensitive health information.
  • Essential for effective consent management.
  • Crucial for adhering to privacy regulations.
  • Important for nurturing trust among stakeholders

Role of Consent Management Platforms (CMPs)

CMPs are valuable tools for pharma brands, enabling them to specify the exact purposes for which patient data will be used, particularly in remarketing efforts. This level of granularity in consent management not only aids in regulatory compliance but also plays a significant role in fostering patient trust.

Managing Third-Party Data Aggregation

While leveraging data is essential for targeted marketing efforts, especially in the pharmaceutical industry, where Personal Health Information (PHI) is involved, it is crucial for pharma brands to exercise caution when considering third-party data aggregation. Sharing PHI requires explicit authorization and adherence to strict data security measures to protect patient privacy. A notable challenge in the realm of third-party data aggregation for pharma brands is the inherent difficulty in auditing external service providers thoroughly. As a general principle, pharma brands should exercise prudence and consider the potential risks associated with incorporating third-party data into their marketing strategies. 

Ultimately, these efforts converge on a singular goal: to uphold the highest standards of patient privacy and trust. As the legal and ethical landscape continues to evolve, staying informed and adaptable is not just a regulatory requirement but a cornerstone of building lasting relationships in the dynamic world of healthcare marketing.

Say No to Lazy Data – How Everyone Can Be Part of the Solution

Posted on by Jacob Amann

What is Lazy Data?

Picture this: a chaotic jumble of inconsistent formats, messy categories, and a general disregard for order. Lazy data isn’t just data that prefers lounging on the couch to hitting the gym; it’s the unruly teenager of your business – wild, rebellious, and refuses to conform to a standard.

This laid-back attitude is the root of the problem – a lack of discipline that prevents your data from realizing its full potential. Lazy data isn’t a solitary problem; it triggers a domino effect. One inconsistent entry leads to confusion in reporting, which, in turn, affects decision-making.

Why Did Data Get Lazy?

In the early days of digital marketing, the ease of connecting systems with just a click led to a somewhat chaotic scenario. Imagine the digital landscape as the Wild West, with data flowing freely and connecting without much oversight. While convenient, this Wild West approach allowed lazy data to thrive, as there were minimal checks and balances. The ability to effortlessly connect systems created a false sense of accuracy, like linking two puzzle pieces together without ensuring they fit. 

Inconsistent data entry, neglecting updates, and failing to recognize the importance of standardized schemas have allowed lazy data to infiltrate our systems. The result? Confusion, inefficiency, and missed opportunities.

As privacy regulations tightened, especially in the wake of concerns surrounding user data, businesses realized they could no longer afford a laissez-faire attitude. The emergence of data clean rooms and the imperative for strict naming convention compliance, essential for enabling tools like marketing mix modeling, forced a reckoning. Lazy data, accustomed to a more carefree existence, suddenly found itself in an environment that demanded structure and order.

While the concept of standardized schemas might initially seem bureaucratic, they are, in fact, the essential foundation for ensuring data usability. Consider it as akin to a musical performance without a conductor; in such a scenario, each instrument plays its own tune, leading to dissonance.

How to Solve  

Data should not be viewed as isolated islands or individual pieces. Instead, envision your organization as a harmonious orchestra led by a master conductor. This fresh perspective brings forth a unified approach, ensuring that every fragment of data performs its role seamlessly.

View Your Business as a System: Don’t think of your business data in silos or individual platforms. Instead, adopt a holistic approach, viewing your organization as an interconnected system – an orchestra led by a master conductor. This perspective helps in creating a seamless flow of information and makes sure that every piece of data plays its part.

Embrace the Standard: Establish a standard data schema across your business. Think of it as giving your data a set of rules to live by – no more rebels without a cause – every piece of data should know its role and play it well.

Audit to Insure: Regular audits are the insurance policy for your data’s health. Ensure that the established standards are being followed and identify any areas that might need a little extra TLC. An audit is like a health checkup for your data – catch potential issues before they become major problems.

Google Analytics 4: The Transition is Here

Posted on by Jacob Amann

Get ready for the imminent sunset of Universal Analytics. Starting July 1st, Universal Analytics will no longer process data. To ensure a smooth transition, it’s crucial to deploy and test Google Analytics 4 tags in place of your existing website analytics infrastructure. We understand that this task can be overwhelming, especially if you’re unsure where to begin. That’s why we’ve created a comprehensive checklist to guide you through the entire process:

Learn About GA4

Start by familiarizing yourself with Google Analytics 4. While it shares some similarities with Universal Analytics, there are significant differences in the user interface, event tracking, and conversions, as well as the modification or removal of certain metrics.

Google Resources: 

Introducing GA4

UA vs GA4

Review Your Tag Strategy

While it is not necessary to make fundamental changes to what you are tracking on your website, it is a good time to consider if what you are tracking in the Universal Analytics setup allows you to make informed business decisions. If not, update your strategy to track important events on your website, with a focus on collecting lead data for future re-messaging opportunities.

Migrate Existing Tags

Once you’ve updated your tag strategy, it’s time to delve into Google Tag Manager (GTM). Google Analytics 4 requires a configuration tag that should fire across all your pages. If Google hasn’t already guided you through creating a new configuration tag, simply create a new tag that fires on all pages. This configuration tag will typically replace your Universal Analytics Page View tag.

Migrating your existing Universal Analytics events to Google Analytics 4 is relatively straightforward. Inside Google Tag Manager, you can reuse existing triggers, but you’ll need to update your tag type to a GA4 Event tag.

The key difference between events in Universal Analytics and Google Analytics 4 is the structure. In Universal Analytics all events had a Category, Action, Label, and Value. In Google Analytics 4, simply name your events and pass additional event parameters as key-value pairs. Your ‘Event Name’ should be descriptive enough to identify an event – leave the nuances to event parameters. For instance, if you had a tag in Universal Analytics that tracked blog post views, it would have likely looked like the following: 

  • Event Action: Blog Event
  • Event Category: Blog Post View
  • Event Label: dynamically populated the title of the blog post 

In Google Analytics 4 this could be simplified to the event name “blog_post_view” with an event parameter of name with a value equal to the title of the blog post.

Compare Data

After your new tags are deployed, you should compare the Google Analytics 4 data to Universal Analytics data or another reference data source. There can be issues during your new tag deployment or overlooked settings that may need to be re-examined. For example, if you were using session de-duplication of goals in Universal Analytics, you may need to instead adjust your Google Analytics 4 firing rules in GTM to prevent duplicates as this feature of Universal Analytics has sunset. Being aware of the nuances in your data is vital.

Educate Teams

Now that your new Google Analytics 4 property is up and running, it’s essential to educate key stakeholders and teams within your organization about the data available in GA4. By providing them with the necessary knowledge and insights, you empower them to make informed decisions and maximize the benefits of the new analytics platform. Here are some steps you can take to educate your teams:

Organize Training Sessions:

Conduct training sessions or workshops to introduce your teams to Google Analytics 4. Cover the basics of the platform, including navigation, key features, and reporting capabilities. Provide hands-on demonstrations to help them understand how to access and interpret the data.

Highlight Differences:

Emphasize the key differences between Universal Analytics and Google Analytics 4. Explain the changes in terminology, event tracking, and reporting structure. Help your teams grasp the nuances and limitations of GA4 compared to the previous version, ensuring they are aware of any adjustments needed in their analysis processes.

Showcase New Features:

Highlight the enhanced features and functionalities of Google Analytics 4 that can benefit different teams within your organization. For example, show marketers how to leverage the advanced audience segmentation and user journey analysis capabilities. Demonstrate to product teams how GA4 can provide insights into user behavior and help optimize the user experience. Tailor the training to the specific needs and interests of each team.

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.

Learn More

Data Storytelling: How to Act on Analytics

Posted on by Jacob Amann

Data storytelling transforms brands. Take an inside look at how Coegi crafts stories with actionable recommendations for our clients by finding the human element in the numbers.

As marketers, we now have access to vast amounts of data. There’s been a major influx of analyst jobs in the last several years as a result.

But are we telling compelling stories with that data and adjusting our strategies based on the insights? If not, what’s the point?

The true value in data lies in how we use key insights to take informed actions for businesses. In other words, with data storytelling.

4 Steps to Set up Data Storytelling in Your Analytics Practice

Gather: Set up a measurement framework to capture metrics that matter most

First, set performance KPIs that ladder up to your business goals. For more information on how to do this, feel free to reference our Marketing Measurement Playbook. Then, prepare a learning agenda to determine the types of information you are looking to understand from your campaign.

Are there hypotheses you want to validate? Assumptions you want to challenge? Audience learnings you want to gather? Use the agenda to help answer these questions.

Learn: Capture and visualize data to pull key insights

Once the campaign is running, you begin to gather data: this is your “what.” Now, it’s up to you and your media partners to uncover the “why.” Look at the underlying narrative running through your data to build a meaningful story arc.

A great way to do this is by visualizing the data in a way. This method of data storytelling allows you to easily identify trends and understand performance relative to goals. Consider layering campaign data with third party data to see a holistic picture and identify outliers or interesting correlations. Look at the data from a macro lens. This helps weave the micro data points into a cohesive story makes sense to both marketers and external team members like the sales team or the executive suite.

We often talk about blending art and science in our marketing strategies – that same concept applies to data analytics. When communicating results to internal stakeholders, qualitative information with direction from quantitative data often speaks volumes for executives. But only if you tell the right story. You want to layer in context, feeling and understanding – the human emotion and behavior will amplify the data you’ve collected. Knowing the audience and tailoring your story to their point of view will help ensure the information resonates.

Brent Dykes, author of ‘Effective Data Storytelling’, says “Your data may hold tremendous amounts of potential value, but not an ounce of value can be created unless insights are uncovered and translated into actions or business outcomes”. This leads us into the next step: application.

Apply: Transform insights into actionable strategies, and repeat.

Data storytelling provides an opportunity to connect the dots between various media spend across channels and show how they work together to reach your customer when and where it mattered. If done right, it will also show areas that didn’t succeed. Those failures can guide new messaging or creative on particular channels, or the adjustment of certain tactics and spend reallocation. Additionally, it should highlight any gaps between customer touch points and eventual conversion or retention. Lay out clear, actionable steps based on analytic insights to transform your digital marketing strategy.

Refine and Repeat

Marketers create an infinite cycle of improvement through this data feedback loop. The digital ecosystem is constantly in flux. New platforms, privacy laws, consumer behavior and more, creating twists and turns in the media landscape. This process is never perfect. But, by using performance marketing data to tell your brand story, you can ensure it is always evolving and being refined. This practice minimizes media waste and allows marketers to make more informed decisions and craft winning strategies.

“Numbers have an important story to tell. They rely on you to give them a clear and convincing voice.” – Stephen Few

If you need help finding the story in your data, Coegi is here to help. Set up a discovery call with our team to explore opportunities for your brand.

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