The Drum – The Concept of a Marketing ‘Funnel’ is Flawed

The traditional marketing funnel no longer works for the modern customer journey because it makes assumptions about consumer behavior that are great in theory but not reflective of reality. In this article on The Drum, Coegi’s Elise Stieferman explains why it is time to drop one of advertising’s favorite concepts and  how your brand can strategically drive performance in the modern  marketplace.  

The Drum – Prepare for the ‘Trough of AI Disillusionment’: H2 2023 Predictions

As we hit the second half of 2023, The Drum brings you a tasting menu of Drum Network leaders‘ predictions, from AI disillusionment to… fungal growth and changes for pet owners. Elise Stieferman, director of marketing and business strategy, Coegi predicts that “AI will be H2 2023’s Dr Jekyll and Mr Hyde: both hero and villain for marketers, depending on expertise and perspective.” Learn more from Elise and other industry leaders in this article on The Drum.

Do’s and Don’ts of Using Generative AI for Creative

Few innovations have so thoroughly dominated cultural obsession quite like Generative AI. In the past year, hundreds of companies have emerged offering their take on what this technology can do — in the marketing world this is either an easy button for more sales or the end of the world as we know it, depending on who you ask. And while in the programmatic buying world working alongside a machine-learning copilot is nothing new, many branding teams are currently having to build their playbooks largely from scratch when it comes to using AI for creative. 

With this in mind, we’ve identified broad tips for experimenting with the latest tech without accidentally landing your brand in hot water.

Do: Use generative tools to more tangibly express loose ideas

For those of us who can’t even draw convincing stick figures, generative AI can assist in communicating the general ideas in our heads. They can be useful for quickly storyboarding concepts, giving examples of dynamic or sequential messaging during high-level conversations, and communicating the style type a brand would be looking for from its internal teams or creative partners. In this way, generative AI can be used similarly to temp scores in film editing: a temporary placeholder meant to communicate the general tone-and-feel of the scene to help guide composers’ original work. 

Don’t: Launch a campaign with images pulled directly from AI products

Though we may be amazed with tools like DALL-E or other art generators, using generated art for profit is a tricky legal gray area. The current consensus is that AI-generated work is public domain, but this is being actively challenged. As companies like Getty Images and DeviantArt have asserted in lawsuits, these AI tools have been trained on others’ intellectual property — therefore calling into question who AI-artwork really “belongs” to. A collective of artists frustrated by use of their copyrighted works as training materials are trying to bait Disney into a lawsuit by prompting tools into generating versions of Mickey Mouse. 

AI for creative

With these legal questions in mind, if you’re still eager to leverage AI for improved efficiency, consider the straightforward yet cost-effective offers from Google Performance Max, Meta Advantage+, and TikTok SPC. While their templated style isn’t likely to “wow” an art director, the performance perks are worth testing while we wait for these tools to roll out additional creative personalization offerings and for regulations to be standardized.   

Do: Experiment with AI-driven efficiency in the creative process

While generative AI tools aren’t sophisticated enough to produce something original for your brand, they can ease the burden of the more repetitive or menial tasks like resizing or touch-ups. As Adobe beta users have already learned, Photoshop’s Generative Fill feature should also tremendously aid artists in the process of generating backgrounds, removing objects, or refreshing existing branded content into something that will work in different environments. Features like Generative Fill also carry less risk than free generative tools as they were trained on Adobe’s own Stock photos, resting the fears of their new branded content looking remarkably like a Pixar cartoon. 

Don’t: Frame use of generative AI as your “big idea”

Many brands were likely inspired by the earned media coverage of Mint Mobile’s Chat-GPT Ad, which heavily leaned into the novelty of generative AI and echoed the general public’s excitement at its potential. While it may seem odd to imply something from less than six months ago is already passé, media coverage has since shifted away from novelty and toward critique and the moment has truly passed. Brands grabbing attention now are the ones complementing generative AI’s output with a strong human voice, like Burger King’s tongue-in-cheek response to McDonald’s “Answered by Chat-GPT” creative. 

AI for creative

Do: Implement guidelines for generative AI usage

The adage of “with great power comes great responsibility” most certainly applies to Generative AI as it can do a great deal of harm if left to its own devices. Before rolling out AI tools in your organization, it’s critical to first establish what purpose AI will serve and how it will be utilized. The question is not what AI is able to do, but what AI should do to improve efficiencies in your organization. Once use cases are established, work internally to create a set of guidelines and policies on how to responsibly engage with AI tools so that they are used for the right reasons.  

Don’t: Allow generative AI to operate on autopilot

It’s important to keep in mind that the core function of Generative AI is to produce an output in response to a user’s query, even if the tool doesn’t have the necessary or accurate information to do so. Generative AI tools are largely trained by the data that users feed it, which can be problematic as some data inputs are inaccurate, outdated, or biased. ChatGPT, even after a January 2023 platform update aimed to improve accuracy, still only has accurate data available up until September 2021. Bias results from training data as well. Tidio, a customer service software company, conducted a series of experiments to test the level of bias in AI. They asked one AI tool, StableDiffusion, to generate pictures of a doctor, and it wasn’t until the third try that the tool eventually produced an image of a female doctor. As a result, it’s critical to do your due diligence and both fact check and gut check the output of your AI query to ensure that it does not support the spreading of misinformation or hurtful biases. Treat Generative AI as your co-pilot that works in tandem with human logic and reasoning when producing assets.

Generative AI presents many opportunities to streamline efficiency and spark ideas, but there is still much to unfold as the technology continues to be developed and regulated. The do’s and don’ts above aim to be a general guide to follow, but each organization should proactively discuss how to best experiment with this growing technology — there is a lot of uncertainty, but you don’t want to be left behind as new industry norms  develop. For now, using these tools to better define your big ideas, drive better communication across teams, and improve the efficiency of more monotonous tasks is a great place to start. 

This space is moving quickly, so keep a pulse on the latest rollouts to understand what tools are available for your consideration. 

To learn more about AI, listen to this episode of The Loop Marketing Podcast:

The Drum – Attribution Matters: Navigating an ‘Uncomfortably Complex Topic’

Attributing results to particular channels or campaigns is arguably digital marketing’s most dogged problem, with an array of approaches mutating as platforms change. Coegi’s SVP of Marketing and Innovation advises that “it’s not just GA4 that will upend your attribution models. The latest iOS17 update will reportedly strip link trackers from being passed through message, mail, and private browsing. It’s yet another action chipping away at the scale and effectiveness of last-click attribution and website analytics.” Learn more from Ryan and other experts here.

Google Analytics 4: The Transition is Here

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.

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