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

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

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

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

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.

Data Storytelling: How to Act on Analytics

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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