Friday, August 30, 2024

Google Analytics 4 introduces benchmarking data

Why the shift from ‘conversions’ to ‘key events’ in GA4 is a game-changer

Google rolled out a significant update to Google Analytics 4 (GA4), allowing users to compare their performance with other businesses in their industry.

Why we care: This new feature will provide valuable context for advertisers trying to understand their performance relative to their peers, potentially informing strategic decisions and goal-setting.

How it works.

  • Users can access benchmarking data if their property has the “Modeling contributions & business insights” setting enabled in Admin > Account Settings.
  • Benchmarks are refreshed every 24 hours.
  • The summary displays:
    1. Your trendline.
    2. Median in your peer group.
    3. Range in your peer group (25th to 75th percentile).

Key features.

  • Customizable peer groups: Users can select from various categories to find the most relevant comparison group.
  • Data privacy: Google assures that benchmarking data is encrypted, protected and aggregated to maintain privacy.
  • Wide range of metrics: Covers acquisition, engagement, retention and monetization.

The big picture. This update addresses a long-standing need in the analytics community for comparative data, allowing businesses to gauge their performance more accurately within their industry context.

What’s next. Users are encouraged to check their GA4 accounts for this new feature and explore how it can enhance their analytics insights.



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Peak PPC season is coming: 5 tests to run now

5 tests to run in preparation for peak PPC season

We all love to take a little time away from the grind during the slow season (which for many of us is right now), but smart PPC marketers are also using the time to get in the prime position when the tide is higher. 

  • In retail, that will fire up in October (probably earlier than ever this year). 
  • In B2B, that means prepping for the end-of-year push that comes from needing to hit quotas and engaging brands about to be flush with new yearly budgets.

No matter your vertical or the particular timing, there are five tests I like to run during the slower season to help clients get learnings to leverage when the tide is high. Those are:

1. Channel diversification tests

When choosing a new channel to test, the first things to consider are:

  • How effectively it can help you reach valuable users.
  • Where the right people spend their time.

Depending on your company’s size and goals, you can weigh the pros and cons of niche platforms with great targeting and intent (for SaaS, maybe that’s relevant subreddits or a platform like Capterra) vs. bigger platforms with more reach and less precise targeting (YouTube or connected TV). 

Once you choose your channel(s) to test, you must find some budget. Ideally, your brand or client has a test budget to play with, but if not, consider shifting the budget from the same stage of the purchase journey. 

For instance, if you’re looking to build awareness and you’d like to test Reddit, assess how spend on platforms like GDN is performing to see if you can pull from there without impacting your revenue too dramatically.

Timing-wise, I believe there are no bad times to run tests. Still, remember that your goal should be to learn. 

If demand is relatively low, you’re not running aggressive promotions and direct response is relatively soft, it’s a particularly good time to test up in the funnel. 

When you’re weighing timing, use the tests to get information you can leverage in your peak season.

Dig deeper: Un-silo your PPC campaigns: 4 tactics for more cohesive marketing

2. Landing page and CRO tests

The soft season is also a good time to tweak your existing landing pages or launch new ones to see how they perform.

The ultimate goals are to improve user experience and conversion rates – some of our clients have seen a 15%+ boost from these efforts.

Build your list of landing pages to address based on a combination of impact (engagement volume) and opportunity (low CVR).

You can assess this by looking at your data (spend, traffic and CVR) within your ad platforms or do some cross-channel assessments in GA4. 

First, test the higher-impact variables: 

  • Copy above the fold.
  • A layout that delivers impact at a glance.
  • Adding different types of social proof.
  • Form fields and copy.
  • Different CTAs.

To get the clearest insights when tweaking or revamping a page, duplicate it and run A/B tests between the new and old versions. This approach also prevents performance from dropping if you’re testing riskier changes.

Dig deeper: A/B testing mistakes PPC marketers make and how to fix them

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3. Seasonal creative and offer tests

There’s only so much advanced testing of seasonal creative you can do (Christmas themes might not work in July, for example), but you can test how different specials, promotions and CTAs resonate with your users.

It’s also a good idea to look for different opportunities to run seasonal PPC promotions:

  • For retail, the usual examples would be Mother’s Day, Father’s Day and other relevant secondary holidays.
  • For B2B, the end of the quarter and the need to hit quota can leave room for limited-time promotions to test with your users. 

Many industries can have their own seasonal spikes, too.

Consider the academic calendar if you’re in the education space or the enrollment period if you’re in health and schedule some tests outside of high tide to get learnings to leverage.

Dig deeper: 3 tips for using promotions and discounts in paid search

4. Incrementality testing

One of my favorite testing initiatives for slower times is incrementality testing. 

Basically, test segments of your campaigns to see if they’re actually driving the return you think they are or if users in those segments would be engaging or purchasing without seeing your ads.

These can take the form of holdout tests or geo tests. 

  • For holdout tests, create groups that do not receive your ads and measure their performance against similarly composed groups that are seeing your ads to gauge the difference.
  • For geo tests (a form of holdout tests), identify specific geographic areas to suppress and measure the performance of those geos to those still getting served ads.

Successful learnings from these tests depend on a few key factors: 

  • The right variable (and only one variable, whether that’s geo or age or another factor).
  • Identifying segments to compare that are close enough in composition to produce clean results.
  • Enough data density to make a call on the level of incrementality your spend is driving.

If you find that your campaigns aren’t all that incremental, the next step is to determine where to reallocate the spend for greater impact. 

Often, moving funds away from direct response and up the funnel to build brand awareness and reputation is a long-term play.

If you’re thinking about when to start incrementality testing, the most common reason is that you’re spending more but not seeing a higher return. 

Another reason, though less common, is when your closed-won rate drops in the later deal stages. This suggests there’s a chance to strengthen customer loyalty earlier in the process.

Dig deeper: Incrementality testing in advertising: Who are the winners and losers?

5. Default settings testing

Yes, this is kind of an excuse to remind you to check your default settings (e.g., Google Search Partners, audience expansions in any channel, etc.). 

My rule of thumb is to turn off any settings that will give the advertising platform power to expand your campaigns.

For smaller brands or brands without a sophisticated analytics set-up, it’s best to just turn off these settings and monitor impact (I’m guessing the impact will be improved efficiency). 

Even for brands with more robust measurement systems that tell them that GSP and audience expansions are bringing in revenue, the slow season is a good time to do some on/off testing to measure the effects in their campaigns.

Dig deeper: Improve your Google Ads performance: 3 simple setting changes

Prepare your PPC campaigns for high-demand periods

Human nature makes it hard to knuckle down when the sun is shining, and you’re months away from seeing the traffic that will make or break your year. But your competitors are feeling the same pull to power down their laptops.

Brands that run these tests now and have a system for analyzing and storing the results to deploy when the tide starts rising will have a big edge in crunch time. 

Just remember that when you’re patting yourself on the back in late December, you have your summertime self to thank.



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Mastering AI and marketing: A beginner’s guide

AI and marketing: A beginner’s guide to mastering artificial intelligence

Welcome to the first installment of our article series on artificial intelligence (AI) for marketing beginners. This aims to demystify AI, providing foundational knowledge and practical insights on how AI can help your marketing efforts. 

This introductory article explores AI and why it’s significant and highlights key milestones. I’ll also share actionable steps you can take to start integrating AI into your marketing strategy.

What is AI?

Artificial intelligence or AI, refers to the simulation of human intelligence processes by machines, particularly computer systems. 

These processes include:

  • Learning (the acquisition of information and rules for using the information).
  • Reasoning (using rules to reach approximate or definite conclusions).
  • Self-correction. 

In the context of marketing, AI involves using data and algorithms to predict, analyze and enhance marketing strategies and decisions.

Why is AI significant in marketing?

AI is incredibly important in marketing. It helps marketers connect better with their audience, run more effective campaigns and improve ROI. 

Here are a few key reasons why AI is crucial for modern marketing:

Personalization

AI enables highly personalized marketing experiences by analyzing consumer data and behavior.

This means tailored content, product recommendations and targeted ads that resonate more with individual consumers.

Efficiency

AI automates repetitive tasks such as data analysis, email marketing and social media posting, freeing up valuable time for marketers to focus on strategy and creativity.

Predictive analytics

AI-powered tools can forecast future trends and consumer behavior, allowing marketers to make data-driven decisions and stay ahead of the competition.

Enhanced customer experience

AI-driven chatbots and virtual assistants provide instant, personalized responses to customer inquiries, improving overall customer satisfaction and engagement.

Cost savings

By optimizing ad spend and reducing the need for manual labor, AI helps businesses save money while achieving better results.

The future of AI

Before we delve into the historical milestones of AI, it’s important to understand where the technology is heading. 

The ultimate goal of AI research is to develop general artificial intelligence (GAI), also known as strong AI or artificial general intelligence (AGI).

General AI refers to a hypothetical form of AI that could think and learn like a human, unlike today’s AI systems, which are designed for specific tasks (narrow AI).

Although general AI is an exciting idea, we’re not there yet. The AI tools transforming marketing today are still narrow AI. Each advancement in AI brings us closer to the potential of general AI.

Key milestones in the development of AI

Understanding the historical development of AI gives us a deeper appreciation of its capabilities and future potential. Here are some significant milestones in AI’s evolution:

1950s: The birth of AI

  • The term “artificial intelligence” was coined by John McCarthy in 1956 during the Dartmouth Conference. 
  • This period saw the development of the first AI programs, including the Logic Theorist and the General Problem Solver.

1960s: Early research and development

  • The 1960s marked significant advancements in AI research, with the creation of the first neural networks and the development of ELIZA, an early natural language processing program.

1980s: The rise of expert systems

  • AI research gained momentum with the advent of expert systems, which mimicked human decision-making processes. 
  • These systems were widely used in fields such as medicine and finance.

1990s: Machine learning emerges

  • The focus shifted to machine learning, a subset of AI that involves training algorithms to learn from data. 
  • Notable achievements include IBM’s Deep Blue defeating world chess champion Garry Kasparov in 1997.

2000s: Big data and AI integration

  • The explosion of big data in the 2000s fueled AI’s rapid growth. 
  • AI systems became more sophisticated, with applications in various industries, including marketing, healthcare and transportation.

2010s: Deep learning and AI everywhere

  • The 2010s witnessed the rise of deep learning, a more advanced form of machine learning. 
  • AI-powered technologies such as voice assistants (e.g., Siri, Alexa) and autonomous vehicles became mainstream.

2020s: AI in everyday life

  • Today, AI continues to evolve, with advancements in natural language processing (e.g., GPT-3), computer vision and more. 
  • AI is now an integral part of everyday life and business operations, revolutionizing how we interact with technology.

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5 actionable steps for beginners

Now that you understand the basics of AI and its significance, here are some actionable steps to help you start integrating AI into your marketing efforts:

1. Explore AI tools 

Begin by exploring marketer-friendly AI tools such as:

  • HubSpot: Offers AI-powered marketing automation features.
  • Grammarly: Uses AI to improve your writing.
  • Canva: Has AI features for designing marketing materials.
  • ChatGPT: Can help generate content ideas and drafts.
  • Perplexity: Is an answer engine that aims to provide more contextualized and accurate answers compared to traditional search engines

2. Start with small projects

Implement AI in small, manageable projects. For example, use AI tools to:

  • Personalize email marketing campaigns.
  • Automate social media posting and analytics.
  • Analyze customer data for insights.

3. Use AI for content creation

Leverage AI to enhance your content creation process. Tools like Copy.ai and Jasper can help generate blog post ideas, write drafts and even create social media posts.

A word on AI hallucinations

AI hallucinations refer to instances where generative AI systems generate false or nonsensical information that appears plausible but has no basis in reality. 

This phenomenon is crucial to understand when using AI in marketing, as it can result in inaccurate content, misleading analytics or inappropriate customer interactions.

To mitigate this, always verify AI-generated content, use human oversight and choose reputable AI tools with built-in safeguards.

4. Learn and adapt

AI is constantly evolving. Stay updated with the latest trends and advancements by following reputable sources such as:

  • AI newsletters: Subscribe to newsletters like “The Algorithm” by MIT Technology Review.
  • Online courses: Enroll in AI and marketing courses on platforms like Coursera and Udemy.

5. Join AI communities and educate yourself

Engage with online communities and forums dedicated to AI and marketing. 

Platforms like Reddit and LinkedIn have groups where you can ask questions, share experiences and learn from others. 

I have found Medium to be a great resource for AI education. But if you ever want to play around with Midjourney, they have a ton of content on perfecting your prompts.

Prompt examples

It’s important to understand the structure and elements that make a prompt clear, specific and actionable for AI.

Here are key components and best practices:

Components of a prompt

  • Context: Provide background information or the scenario that sets up the prompt. This helps the AI understand the situation better.
  • Task: Clearly define the specific task you want the AI to perform. This can be a question, an instruction or a request for specific information.
  • Constraints/Instructions: Include any specific guidelines, limitations or instructions that the AI should follow while generating the response.
  • Examples (if necessary): Providing examples can help clarify what you’re looking for and guide the AI towards the desired outcome.

 Midjourney prompt: Image creation

  • “/imagine prompt: A compelling LinkedIn banner image, featuring a diverse team in a brainstorming session, whiteboard filled with ideas. Bright, inspiring colors, viewed from an overhead angle. Background of a modern office with large windows and natural light. Sharp details, dynamic lighting, collaborative ambiance. Created Using: digital photography, realism, high detail, natural lighting, dynamic composition, innovative atmosphere, hd quality –v 6.0”

ChatGPT prompt: Social media content ideas

  • “Our company, [COMPANY NAME], is launching a new product/service in [MONTH/YEAR]. Can you suggest some social media content ideas that will help us build buzz and generate interest among our target audience? Additionally, please create a 30-day content calendar that includes specific posts, themes and strategies to maximize engagement and anticipation leading up to the launch.”

ChatGPT prompt: Email ideation

  • “Can you generate three versions of a comprehensive series of educational emails for [MY COMPANY NAME] in the [INDUSTRY] industry on the topic of [TOPIC]? Each email should provide in-depth insights, actionable tips and practical advice that our subscribers will benefit from. Additionally, please include engaging subject lines, personalized greetings and clear calls to action to enhance engagement. Ensure the content is well-structured, informative and tailored to different segments of our audience, reflecting their varied needs and interests. The series should also align with the latest trends and best practices within the [INDUSTRY] industry.”

Dig deeper: Advanced AI prompt engineering strategies for SEO

Understanding the tech reshaping the marketing landscape

By learning the basics of AI and its significance and implementing these actionable steps, you’re already on the path to becoming a more informed and empowered marketer.

Embrace AI’s possibilities and get ready to elevate your marketing game to new heights.

Dig deeper: How AI will affect the future of search



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Thursday, August 29, 2024

Google Ads adds product Categories tab

Google shopping ads

Google Ads added a new Categories tab under the Products section. It appears to pull from the google_product_category attribute.

Why we care: These ecommerce insights may give you a clearer view of performance metrics, demand trends and actionable recommendations to drive more clicks and sales.

How it works:

  • Performance insights: Understand what’s driving success in your campaigns and where improvements can be made.
  • Demand trends: Stay ahead by spotting shifts in consumer interest at the category level.
  • Actionable recommendations: Get tailored suggestions for boosting your return on ad spend (ROAS), including tips on pausing underperforming categories and optimizing top-performing ones.

What to watch out for. Some advertisers wonder whether Google will correctly categorize products.

First seen. This update was first brought to our attention by Aleksejus Podpruginas on LinkedIn:

Bottom line: These insights could help you fine-tune your strategies and possibly outpace the competition, all from the convenience of the product page.



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What 54 Google Ads experiments taught me about lead gen

What 54 Google Ads experiments taught us about lead gen

For two years, my team ran 54 Google Ads experiments in one lead gen (non-ecommerce) account, testing various features, including:

This article delves into those tests, explaining the rationale behind them, the outcomes achieved and the implications for your own Google Ads accounts.

TL;DR

  • We ran 54 experiments over two years covering a range of features for a lead generation business.
  • Key tests covered bid strategies, match types and ad copy.
  • Exact match keywords almost always perform better than phrase match keywords.
  • Maximize conversion strategy tended to underperform other bid strategies.
  • Target CPA bidding helped us hone in on an optimal CPA level for us. 

What are Google Ads experiments?

Google Ads experiments allow advertisers to test changes within their campaigns before fully implementing them. 

These experiments, conducted at the campaign level, provide a structured framework to easily set up these changes, test for significance and apply the changes to the whole campaign if they are effective.

For more detailed information, the official Google Ads support pages offer additional guidance.

Experiment setup overview

Across two years, we conducted 54 experiments, testing a variety of levers in Google Ads. Below are a few examples of the experiments.

Key test categories 

  • Bidding: Testing different bidding methods against each other. For example, maximize conversions vs. target CPA or target CPA of $90 vs. target CPA of $120. 
  • Ad copy: Pinning certain ad copies down, testing certain copies or new landing pages.
  • Keyword match: Testing exact match keywords vs. phrase match.
Experiment setup overview

Timings

There was no set time for each experiment, but we tried to run each experiment for at least 30 days. 

However, sometimes when the results were clear in less time, we made a decision. Many of the experiments ran for over three months, and most ran for over two months. 

Evaluation

We evaluated all experiments based on conversion rate, conversion volume and cost per acquisition – balancing these metrics to make a decision. (For ad copy tests, we also used CTR.) 

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Experiment results 

1. Exact match vs. phrase match 

Exact match keywords performed better on all criteria when compared to phrase match. They had lower CPAs and higher conversion rates and maintained similar conversion volumes to phrase match keywords.

Insights

  • Although exact match keywords were more expensive, they still almost always had higher conversion rates and lower CPA than phrase match keywords. 
  • While certain phrase match keywords can be useful, when taken as a grouped campaign, it was always better for us to run exact match keywords only. 
  • Phrase match has a place and can be useful for search term discovery and in new campaigns, but if there is enough volume for exact keywords, the latter should always be preferred. 
  • Exact match keywords often perform better than phrase match keywords because Google hides many search terms on phrase match keywords. 
  • In many cases, we found up to 80% of search term impressions and clicks are hidden when compared to keyword impressions and clicks. 
  • You can see this on the search terms report. The percentage hidden on exact match is far lower because the search term possibilities are far lower. 
  • With phrase, you don’t actually know many of the keywords you have bid on and are unable to negative out many search terms, which further lowers the quality of these keywords.

2. Maximize conversion bidding vs. other bidding strategies

In all of the experiments where we ran maximize conversions against a target CPA strategy, the target CPA strategy always outperformed, driving more leads at the same cost. 

When we ran maximize conversions against manual bidding, maximize conversions performed better. 

Insights

  • If you are using manual bidding and you have enough conversion data, then you should move to automated bidding. Either target CPA or maximize conversions would be a good option. 
  • If you don’t have a lot of conversion data, you should wait or test maximize conversion bidding. In general, we found that compared to tCPA, maximize conversion bidding drove bids too high to be effective. It allowed Google to set bids too high, sometimes excessively so.
  • Google’s advice here was that the bids would stabilize over time. However, we ran these tests often for three months and did not see bids come down. 
  • It’s likely most advertisers, like us, would not be able to sustain such high bids for this long. 

3. Target CPA vs. target CPA at different levels 

Essentially, we tested tCPA at different levels (e.g., tCPA 90 vs. 120). Approximately half of our target CPA experiments failed and half succeeded.

Insights

  • The results showed that a target CPA of around 120 produced the best outcomes. 
  • Tests with target CPAs lower than 120, such as 110, did not improve performance. 
  • The 120 target CPA consistently delivered higher conversions at a better cost per conversion.
  • For our account, which is heavily lead-based, tCPA tends to be the best model. 
  • We found the optimal target CPA that maximizes leads within our budget. Your goal with experiments should be to find your own optimal target CPA, which may vary.

Next steps

While our results provide valuable insights, remember that every PPC account and campaign is unique.

Testing these strategies in your specific context will help confirm whether our outcomes apply to your situation.

Our experiments can serve as a starting point for optimizing your lead gen campaigns. By leveraging our findings, you can save time and effort in your own testing process. 

Validate our results with your own data, and if the outcomes align, confidently implement those changes in your account for improved performance.



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6 steps to AI-driven budgeting and forecasting for digital marketing

6 steps to AI-driven budgeting and forecasting for digital marketing

AI is transforming how businesses approach their digital marketing budgeting and forecasting processes.

Companies can develop robust forecasting and budgeting models that focus on data-driven decisions.

This approach enables customized strategies that align with specific business goals and can be adjusted based on organizational needs and channels.

AI is a key driver for transformation.

  • Up to 86% of organizations implementing generative AI report seeing revenue growth of 6% or more in their total annual company revenue, per a Google Cloud report

This article covers how to leverage AI with the right data to come up with forecasting and budgeting prioritization, specifically for digital marketing efforts.

Below are the six steps to craft a model that aligns with your unique business needs. 

budgeting and forecasting process

Step 1: Define business goals, objectives and KPIs

This step is divided into two parts: setting goals and identifying key performance indicators (KPIs).

Clearly articulate business objectives

Specify the overall business objectives, such as increasing revenue, enhancing brand awareness, generating leads or boosting engagement rates.

Identify specific KPIs

Determine the relevant KPIs for each targeted channel, such as views, conversion rates or cost per acquisition (CPA).

Goals-kpis-strategies-alignment
Goals, KPIs, strategies alignment

After aligning on goals and KPIs, analyze historical trends to identify channels and strategies that can contribute toward achieving the goals.

Channel distribution analysis

  • Gather historical data: Collect data on marketing spend, revenue and key performance indicators for each channel.
  • Identify performance levels: Analyze the data to determine which channels are high-performing and which are low-performing.
  • Calculate ROI: Know the return on investment (ROI) and other relevant metrics for each channel.
  • Identify industry and market trends: Examine industry trends, including market demand and supply patterns for the upcoming year and the previous year.
  • Assess consumer behavior and emerging technologies: Identify shifts in consumer behavior and emerging technologies, such as AI, virtual agents and the shift to mobile platforms.
  • Analyze competitor activity: Evaluate competitor performance across different channels.
  • Analyze customer discovery channels: Determine how your customers are finding your business. While new marketing strategies may seem promising, ensure these channels align with your customer’s journey. 
  • Use Google Search Console and Google Analytics: Leverage tools like search console and analytics to understand customer search trends and compare them with industry-wide search changes.
  • Evaluate content formats: Assess whether your business is gaining traction through videos, AI-generated overviews or images and compare these results with industry and competitor benchmarks.

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Step 3: Data and infrastructure 

Evaluate the existing technology stack

  • Assess the technology infrastructure for its ability to centralize data, maintain data quality and ensure data security.

Centralize data

  • Consolidate all data from various channels and touchpoints into a single location, such as a data lake. Test if data can be used to run analysis and reporting.

Data cleaning and pre-processing

  • With all the data collected, the next step is to prepare it for forecasting and budgeting models.
  • Begin by cleaning and organizing the data, focusing on the most relevant data points aligned with business goals and KPIs.
  • Ensure data accuracy and consistency by removing outliers and addressing any inconsistencies.
  • Conduct exploratory data analysis to identify patterns and correlations.

Step 4: Forecasting

Forecasting is key to budgeting because it helps manage risks, seize opportunities, optimize resources and make smart investment decisions. 

The following machine learning and language-based models can be used to generate these forecasts:

ARIMA (Auto Regressive Integrated Moving Average)

  • Combines autoregression and moving average.
  • Flexible for various time series patterns.
  • SARIMA, or seasonal ARIMA, accounts for seasonal fluctuations.

Prophet

  • Developed by Facebook.
  • Decomposes time series data into trend, seasonality and holiday effects.
  • Works best with time series with strong seasonal effects and multiple seasons of historical data.

Chronos (language-based model)

  • Developed by Amazon.
  • A family of pretrained time series forecasting models based on language model architectures.
  • A time series is transformed into a sequence of tokens via scaling and quantization and a language model is trained on these tokens using the cross-entropy loss.
  • Once trained, probabilistic forecasts are obtained by sampling multiple future trajectories given the historical context.

Consider using Claude 3.5 Sonnet by Anthropic to easily generate Python code for implementing the forecasting models.

Step 5: Budgeting

Determining the optimum channel allocation

  • Determine the most suitable budget allocation method based on business objectives, such as percentage of revenue or a fixed amount per channel.
  • Consider factors like channel maturity, potential ROI and customer and market trends.
  • Use statistical techniques such as Linear Regression to generate a market mix model that optimizes the budget allocation across channels to meet your business goal.

Regular monitoring and optimization

  • Continuously track channel performance against budget and KPIs.
  • Identify underperforming channels and reallocate budget accordingly.
  • Optimize campaigns based on real-time data and insights.

Step 6: Use cases

Finally, create specific use cases for each step of your marketing plan. For example:

  • “As the chief marketing officer of an upscale hotel, I want to increase online revenue by 20% year over year. To help achieve this goal, recommend the best budget allocation across digital channels.”

Solution steps

Define business goals and KPIs

  • Goal – Increase revenue by 20% overall 
  • KPIs – Revenue

Channel distribution, ROI, revenue and conversions 

  • Gather historical revenue and conversion data from Google Analytics across all channels. 
  • Collect spend data for all channels.
  • Calculate ROI for each channel. 

Data and infrastructure

  • All data should be available in a centralized storage such as a data lake.
  • It is easier to access clean and centralized data for training the model.
  • Install required python libraries such as pandas, numpy or scipy.
  • Perform exploratory data analysis to identify trends and seasonal patterns by running python libraries and statistical analysis  

Forecasting and budgeting

  • Use forecasting models such as SARIMA to forecast the revenue from each channel based on the spend. The model will account for seasonality trends in the data
  • Use statistical optimization techniques to find the best budget allocation across channels.

Working model output 

Current average spend across the top channels:

current-spend

After executing all the steps given above, here’s the recommended allocation by the budgeting model:

budgeting-model

Individual channel allocation

Once you have the budget allocation for each channel, the next step is to break it down further and identify specific sources or platforms within each channel. 

For example:

  • Within the organic search channel, you might consider sources like Google Business. 
  • For paid search, platforms like Google Ads and Facebook. 

This helps determine the precise budget needed for each source.

For our use case, focus on the organic search channel. Run the budgeting model for all sources within this channel to determine each source’s allocation.

After executing all the steps, here’s the recommended budget allocation for organic search sources:

organic-recommended-budgets

Strategies and solutions to maximize the full-funnel digital experience 

Now based on the recommended allocation, deploy the strategies to optimize GBP Listings and Google Search.

AI in digital marketing: Smarter budgeting and forecasting

In the AI era, budgeting and forecasting can be done in real time if data from various customer touchpoints and channels is centralized and readily available throughout the customer journey. 

By leveraging AI, you can optimize marketing performance by allocating the right budget to each channel based on its contribution to achieving your business goals.



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Wednesday, August 28, 2024

New Google tools to boost first-party data strategies

Google is rolling out two new features to help you navigate the evolving digital privacy landscape and strengthen first-party data strategies.

Why we care. Despite Google deciding to roll back on its plans to remove third-party cookies, with privacy regulations still in place, first-party data has become crucial. These tools is intended to help you simplify data collection and management while respecting user privacy.

Driving the news. Google announced Tag Diagnostics and a new consent management setup, both designed to streamline first-party data collection and utilization.

Details. The new offerings include:

Tag Diagnostics:

  • Provides at-a-glance view of account health.
  • Alerts users to potential measurement issues.
  • Offers guidance on fixing problemsAvailable in Google Tag Manager, Google Ads, and Google Analytics.

Integrated Consent Management Platform (CMP) setup:

  • Streamlines consent banner creation and consent mode implementation.
  • Works within Google Ads, Analytics, and Tag Manager interfaces.
  • Integrates with several CMP partners, including consent manager, Cookiebot, iubenda, and Usercentrics.

What’s next: Google plans to continue adding new diagnostics capabilities and investing in first-party data solutions as the industry evolves.

Bottom line. As digital advertising faces a privacy-driven shift, Google is positioning itself as a key enabler of first-party data strategies for advertisers.



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Adtech antitrust trial judge blasts Google’s business practices

Google’s legal troubles appeared to worsen yesterday as the judge of the adtech antitrust trial delivered sharp criticism of the tech giant, signaling possible consequences for its business practices.

What happened: During the pre-trial motion hearings, Judge Leonie Brinkema sharply criticized Google for its handling of privileged information, labeling the company’s actions as “absolutely inappropriate and not proper.” 

The court singled out the so-called ‘Walker Memo,’ containing what she referred to as “incredible smoking guns,” as evidence of potential wrongdoing.

The judge also condemned Google’s practice of auto-deleting chats, mockingly referred to by employees as “Vegas mode,” implying that the company may have intentionally destroyed evidence.

Why we care. If Google is found to have engaged in anticompetitive practices, it could lead to significant changes in the digital advertising landscape. That could include changes in pricing and bidding models, and possibly increased competition from other platforms.

The big picture: This trial, one of the most significant antitrust cases in decades, could reshape the landscape for the media and tech industries.

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The judge’s remarks suggest that Google’s internal practices could heavily influence the trial’s outcome, with potential inferences drawn against the company as witnesses testify.

What to watch: With less than two weeks until the trial begins, Google faces mounting scrutiny. The judge’s familiarity with related antitrust rulings, including a recent unfavorable decision against Google, sets the stage for what could be a pivotal moment in the company’s legal battle.

Dig deeper.

U.S. vs. Google. This is the second major antitrust trial for Google within the year. Earlier this month, in the U.S. vs. Google antitrust trial, a federal judge ruled that Google illegally monopolized search and search advertising markets, especially by paying $20 billion annually for default search status on iPhones.



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Global search engine AI innovations: What SEOs need to know

Global search engines and their AI innovations- What SEOs need to know

Google and Bing get a lot of attention, which you’d expect given both hold approximately 95% of the global search market share

However, users searching for “Google alternatives” has increased by 7% year-on-year (mainly driven by a big spike at the end of May), with the U.S. seeing a 4% increase, according to data from Glimpse.

Google alternatives - Monthly search volume

While this isn’t substantial, the spike at the end of May correlates with the period immediately after Google I/O and the less-than-desirable AI Overview results.

It’s uncertain how much the end-of-May spike was due to SEO articles about Google alternatives.

If broader market trends mostly caused the spike, the recent DOJ lawsuit might change how we understand the market’s reaction to Google and its feature issues.

It also reminds us that Google and Microsoft Bing aren’t the only search engines out there; they aren’t the only search engines developing core search products or new products and features using AI and LLMs as a backbone.

For several years, I’ve been covering Yandex, Baidu and other search engines worldwide as they develop their search products with and without AI.

Baidu (China)

Baidu has faced new competition in recent years. While it remains the dominant search engine in China, it has lost market share to non-traditional “search engine” applications.

Baidu is looking to AI to revamp its content ecosystem, bringing new capabilities to existing platforms like Baidu App, Baidu Content Ecosystem, Baidu Tieba and Baidu Marketing.

At the forefront of Baidu’s search revolution is Wenxin Yiyan (a.k.a. ERNIE).

ERNIE (Enhanced Representation through kNowledge Integration) is an AI language model developed by Baidu that leverages knowledge graphs to enhance natural language understanding and generation. 

While ERNIE is a part of Baidu’s broader AI strategy, including applications in search, autonomous driving and cloud computing, in May 2024, Baidu disclosed that around 11% of traditional core search results were generated using AI technology.

Another key product in Baidu’s portfolio is Wenku.

Wenku is being transformed into a smarter, more intuitive document creation tool, much like Copilot, making it easier for users to create and manage content.

Baidu is, however, feeling the pressure as other platforms improve and work to acquire user focus, develop their own search tools and use AI.

Recently, competition has heated up even more. On July 10, Alibaba’s search engine, Quark, launched its AI-driven “Super Search Box.” 

Tencent’s Yuanbao rolled out a new deep search feature on July 1, and DingTalk’s AI search started its exclusive testing phase on June 26.

Dig deeper: International SEO: How to avoid common translation and localization pitfalls

Yandex (Russia)

Yandex has claimed to have used AI in search technologies for more than 2020 years. At the 2020 YAC (Yet Another Conference), Tigran Khudaverdyan unveiled YATI (Yet Another Transformer with Improvements).

YATI is a machine learning algorithm developed to enhance its search engine capabilities. It focuses on better understanding the semantic meaning of search queries and documents, similar to human comprehension. 

YATI processes more of the text content on a page than previous Yandex algorithms, which only looked at partial text on a page.

Yandex introduced Neuro, an AI tool designed for the Russian market and language, in May. It uses advanced linguistic models to handle Cyrillic script better than competitors like Gemini and Copilot.

Neuro is now integrated into important Yandex products, including Alice and YaBrowser. Its core functions and use are in image and speech recognition and NLP tasks like search and content creation.

Yandex upgraded its YandexGPT to the third version in April. At the time of launch, YandexGPT 3 was free (100 API requests per hour), and plans were made to integrate this technology into wider products.

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Naver (South Korea)

A core element of Naver’s strategy is the introduction of “Cue:,” a new AI-powered search service designed to understand complex queries and provide structured conversational results.

After experimenting with Cue:, I’d compare it more to Copilot than to Gemini.

South Korean media have likened the extended use of Cue: to be closer to a search “personal assistant” than a traditional information retrieval search engine. 

Cue: also appears to be positioned as an “add-on” to the traditional Naver search ecosystem and not detract from traditional results, Cafes or Blogs.

Cue:’s capabilities and success are down to the LLM behind it, HyperCLOVA X.

HyperCLOVA X builds on the original HyperCLOVA, first launched in 2021. The X iteration is more powerful, enabling complex, context-aware interactions beyond simple queries and has been integrated into other business tools, such as CLOVA studio.

Daum and NATE

Staying in South Korea, I feel it is important to mention two other region-specific search engines:

  • Daum, operated by Kakao, uses AI to improve the relevance and personalization of search results. It tailors outcomes based on user behavior, particularly in news and local searches. 
  • NATE, by SK Communications, uses AI to enhance search accuracy, especially in news and life searches, focusing on trustworthiness. Its AI approach is more cautious, avoiding experimental or generative AI features.

Neither Daum nor NATE’s AI integrations are as prominent as Naver’s AI efforts, although according to a 2024 survey, Daum does have a higher market share in South Korea than ChatGPT.

Dig deeper: Multinational SEO vs. multilingual SEO: What’s the difference?

Seznam (Czechia)

Seznam.cz is a Czech search engine and web portal, often considered the “Google of the Czech Republic.”

In addition to its search engine, Seznam provides various other services like Sreality (real estate), Sauto (automobile classifieds) and Novinky (news).

Seznam’s LLM is already being utilized in its search engine, Vyhledávání, with plans to expand its deployment across other Seznam services this year and the next. 

In June 2023, Seznam introduced Hacsiko, the first AI-created female presenter with a synthetic voice, on Expres FM radio. This marked a groundbreaking event in the audio market in the Czech Republic and Europe.

Seznam has also collaborated with Microsoft to showcase the practical uses of AI to Czech companies, showcasing them through the Seznam Native project.



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Tuesday, August 27, 2024

What is PPC?

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The post What is PPC? appeared first on Search Engine People Blog.



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Google removes Auction Insights from Looker Studio

Google Ads users no longer have access to Auction Insights fields in new data sources within Looker Studio. Existing data sources will lose this access on Sept. 23.

Why we care. Auction Insights provides critical competitive data, helping advertisers understand their performance relative to competitors in the same auctions. The removal of these fields from Looker Studio will require advertisers to adjust their Looker reports and strategy tools, and revert to getting the competitive metrics they want to see in Google Ads.

Fields impacted. Affected fields include Auction Insight metrics such as Domain, Average Position, Impression Share, Outranking Share and several others critical for competitive analysis.

Action needed: Advertisers should proactively remove these fields from their Looker Studio reports and charts to prevent disruptions. Failing to do so could result in broken reports, impacting the ability to track and optimize ad performance effectively.

Yes but. Advertisers shouldn’t worry here – Google has not said Auction Insights will go away. You can still access all the competitive analysis metrics in Google Ads.

Bottom line: With the removal of Auction Insights from Looker Studio, you will need to find alternative ways to track competitive performance metrics and adjust Google Ads reporting strategies.



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Google introduces YouTube creator-based audience targeting

YouTube

Google Ads now allows advertisers to create remarketing lists based on viewers of specific YouTube creator videos, unlocking new possibilities for targeted campaigns.

Previously, advertisers could only create remarketing lists based on viewers’ interactions with their own linked YouTube channels. This update opens up new opportunities for targeting based on creator content.

Why we care. This new feature allows advertisers to create remarketing lists based on viewers of specific YouTube creator videos, potentially expanding reach and targeting capabilities.

How it works:

  • Advertisers can now link YouTube creator videos to their Google Ads accounts.
  • This provides access to organic view metrics and the ability to create remarketing segments.

Key features:

  1. View counts: Access to non-paid metrics for linked videos.
  2. Remarketing: Create audience segments based on video views.

Between the lines. This feature could be seen as an alternative to impression-based remarketing, offering more precise targeting based on specific creator content.

What to watch: How advertisers will leverage this new capability and its impact on campaign performance and audience targeting strategies.

First seen. We were first alerted to this update by Georgi Zayakov on LinkedIn:

Bottom line: This update expands the toolkit for Google Ads users, potentially allowing for more nuanced and effective YouTube-based remarketing campaigns.



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Google adds generative AI insights, shopping ad campaign goals

Google today introduced four updates meant to help retailers ahead of the holiday shopping season.

Key updates:

  • Shopping trends insights. You can now access real-time shopping trends in Google Merchant Center. This could help you adjust your inventory and product descriptions based on what shoppers are actually searching for or viral trends.
  • AI-powered insights. Two new generative AI features in Merchant Center Next provide quick summaries of product performance and custom reports based on specific data queries.
  • Automated in-store availability. Google is streamlining the management of local inventory ads by automatically syncing in-store availability from retailers’ websites. This should make it easier for customers to find products nearby.
  • New campaign goals. Google Ads now includes options for setting customer acquisition goals and optimizing campaigns with profit-focused metrics, helping advertisers prioritize profit and optimizations during peak shopping periods.

Why we care: There are fewer days between Thanksgiving and New Year’s to reach consumers. This means retailers will be under pressure to hit holiday sales goals. Google’s new tools are meant to help businesses stay agile and make smarter, data-driven decisions.



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