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Everything You Need To Know

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Everything You Need To Know

Google has just released Bard, its answer to ChatGPT, and users are getting to know it to see how it compares to OpenAI’s artificial intelligence-powered chatbot.

The name ‘Bard’ is purely marketing-driven, as there are no algorithms named Bard, but we do know that the chatbot is powered by LaMDA.

Here is everything we know about Bard so far and some interesting research that may offer an idea of the kind of algorithms that may power Bard.

What Is Google Bard?

Bard is an experimental Google chatbot that is powered by the LaMDA large language model.

It’s a generative AI that accepts prompts and performs text-based tasks like providing answers and summaries and creating various forms of content.

Bard also assists in exploring topics by summarizing information found on the internet and providing links for exploring websites with more information.

Why Did Google Release Bard?

Google released Bard after the wildly successful launch of OpenAI’s ChatGPT, which created the perception that Google was falling behind technologically.

ChatGPT was perceived as a revolutionary technology with the potential to disrupt the search industry and shift the balance of power away from Google search and the lucrative search advertising business.

On December 21, 2022, three weeks after the launch of ChatGPT, the New York Times reported that Google had declared a “code red” to quickly define its response to the threat posed to its business model.

Forty-seven days after the code red strategy adjustment, Google announced the launch of Bard on February 6, 2023.

What Was The Issue With Google Bard?

The announcement of Bard was a stunning failure because the demo that was meant to showcase Google’s chatbot AI contained a factual error.

The inaccuracy of Google’s AI turned what was meant to be a triumphant return to form into a humbling pie in the face.

Google’s shares subsequently lost a hundred billion dollars in market value in a single day, reflecting a loss of confidence in Google’s ability to navigate the looming era of AI.

How Does Google Bard Work?

Bard is powered by a “lightweight” version of LaMDA.

LaMDA is a large language model that is trained on datasets consisting of public dialogue and web data.

There are two important factors related to the training described in the associated research paper, which you can download as a PDF here: LaMDA: Language Models for Dialog Applications (read the abstract here).

  • A. Safety: The model achieves a level of safety by tuning it with data that was annotated by crowd workers.
  • B. Groundedness: LaMDA grounds itself factually with external knowledge sources (through information retrieval, which is search).

The LaMDA research paper states:

“…factual grounding, involves enabling the model to consult external knowledge sources, such as an information retrieval system, a language translator, and a calculator.

We quantify factuality using a groundedness metric, and we find that our approach enables the model to generate responses grounded in known sources, rather than responses that merely sound plausible.”

Google used three metrics to evaluate the LaMDA outputs:

  1. Sensibleness: A measurement of whether an answer makes sense or not.
  2. Specificity: Measures if the answer is the opposite of generic/vague or contextually specific.
  3. Interestingness: This metric measures if LaMDA’s answers are insightful or inspire curiosity.

All three metrics were judged by crowdsourced raters, and that data was fed back into the machine to keep improving it.

The LaMDA research paper concludes by stating that crowdsourced reviews and the system’s ability to fact-check with a search engine were useful techniques.

Google’s researchers wrote:

“We find that crowd-annotated data is an effective tool for driving significant additional gains.

We also find that calling external APIs (such as an information retrieval system) offers a path towards significantly improving groundedness, which we define as the extent to which a generated response contains claims that can be referenced and checked against a known source.”

How Is Google Planning To Use Bard In Search?

The future of Bard is currently envisioned as a feature in search.

Google’s announcement in February was insufficiently specific on how Bard would be implemented.

The key details were buried in a single paragraph close to the end of the blog announcement of Bard, where it was described as an AI feature in search.

That lack of clarity fueled the perception that Bard would be integrated into search, which was never the case.

Google’s February 2023 announcement of Bard states that Google will at some point integrate AI features into search:

“Soon, you’ll see AI-powered features in Search that distill complex information and multiple perspectives into easy-to-digest formats, so you can quickly understand the big picture and learn more from the web: whether that’s seeking out additional perspectives, like blogs from people who play both piano and guitar, or going deeper on a related topic, like steps to get started as a beginner.

These new AI features will begin rolling out on Google Search soon.”

It’s clear that Bard is not search. Rather, it is intended to be a feature in search and not a replacement for search.

What Is A Search Feature?

A feature is something like Google’s Knowledge Panel, which provides knowledge information about notable people, places, and things.

Google’s “How Search Works” webpage about features explains:

“Google’s search features ensure that you get the right information at the right time in the format that’s most useful to your query.

Sometimes it’s a webpage, and sometimes it’s real-world information like a map or inventory at a local store.”

In an internal meeting at Google (reported by CNBC), employees questioned the use of Bard in search.

One employee pointed out that large language models like ChatGPT and Bard are not fact-based sources of information.

The Google employee asked:

“Why do we think the big first application should be search, which at its heart is about finding true information?”

Jack Krawczyk, the product lead for Google Bard, answered:

“I just want to be very clear: Bard is not search.”

At the same internal event, Google’s Vice President of Engineering for Search, Elizabeth Reid, reiterated that Bard is not search.

She said:

“Bard is really separate from search…”

What we can confidently conclude is that Bard is not a new iteration of Google search. It is a feature.

Bard Is An Interactive Method For Exploring Topics

Google’s announcement of Bard was fairly explicit that Bard is not search. This means that, while search surfaces links to answers, Bard helps users investigate knowledge.

The announcement explains:

“When people think of Google, they often think of turning to us for quick factual answers, like ‘how many keys does a piano have?’

But increasingly, people are turning to Google for deeper insights and understanding – like, ‘is the piano or guitar easier to learn, and how much practice does each need?’

Learning about a topic like this can take a lot of effort to figure out what you really need to know, and people often want to explore a diverse range of opinions or perspectives.”

It may be helpful to think of Bard as an interactive method for accessing knowledge about topics.

Bard Samples Web Information

The problem with large language models is that they mimic answers, which can lead to factual errors.

The researchers who created LaMDA state that approaches like increasing the size of the model can help it gain more factual information.

But they noted that this approach fails in areas where facts are constantly changing during the course of time, which researchers refer to as the “temporal generalization problem.”

Freshness in the sense of timely information cannot be trained with a static language model.

The solution that LaMDA pursued was to query information retrieval systems. An information retrieval system is a search engine, so LaMDA checks search results.

This feature from LaMDA appears to be a feature of Bard.

The Google Bard announcement explains:

“Bard seeks to combine the breadth of the world’s knowledge with the power, intelligence, and creativity of our large language models.

It draws on information from the web to provide fresh, high-quality responses.”

Screenshot of a Google Bard Chat, March 2023

LaMDA and (possibly by extension) Bard achieve this with what is called the toolset (TS).

The toolset is explained in the LaMDA researcher paper:

“We create a toolset (TS) that includes an information retrieval system, a calculator, and a translator.

TS takes a single string as input and outputs a list of one or more strings. Each tool in TS expects a string and returns a list of strings.

For example, the calculator takes “135+7721”, and outputs a list containing [“7856”]. Similarly, the translator can take “hello in French” and output [‘Bonjour’].

Finally, the information retrieval system can take ‘How old is Rafael Nadal?’, and output [‘Rafael Nadal / Age / 35’].

The information retrieval system is also capable of returning snippets of content from the open web, with their corresponding URLs.

The TS tries an input string on all of its tools, and produces a final output list of strings by concatenating the output lists from every tool in the following order: calculator, translator, and information retrieval system.

A tool will return an empty list of results if it can’t parse the input (e.g., the calculator cannot parse ‘How old is Rafael Nadal?’), and therefore does not contribute to the final output list.”

Here’s a Bard response with a snippet from the open web:

Google Bard: Everything You Need To KnowScreenshot of a Google Bard Chat, March 2023

Conversational Question-Answering Systems

There are no research papers that mention the name “Bard.”

However, there is quite a bit of recent research related to AI, including by scientists associated with LaMDA, that may have an impact on Bard.

The following doesn’t claim that Google is using these algorithms. We can’t say for certain that any of these technologies are used in Bard.

The value in knowing about these research papers is in knowing what is possible.

The following are algorithms relevant to AI-based question-answering systems.

One of the authors of LaMDA worked on a project that’s about creating training data for a conversational information retrieval system.

You can download the 2022 research paper as a PDF here: Dialog Inpainting: Turning Documents into Dialogs (and read the abstract here).

The problem with training a system like Bard is that question-and-answer datasets (like datasets comprised of questions and answers found on Reddit) are limited to how people on Reddit behave.

It doesn’t encompass how people outside of that environment behave and the kinds of questions they would ask, and what the correct answers to those questions would be.

The researchers explored creating a system read webpages, then used a “dialog inpainter” to predict what questions would be answered by any given passage within what the machine was reading.

A passage in a trustworthy Wikipedia webpage that says, “The sky is blue,” could be turned into the question, “What color is the sky?”

The researchers created their own dataset of questions and answers using Wikipedia and other webpages. They called the datasets WikiDialog and WebDialog.

  • WikiDialog is a set of questions and answers derived from Wikipedia data.
  • WebDialog is a dataset derived from webpage dialog on the internet.

These new datasets are 1,000 times larger than existing datasets. The importance of that is it gives conversational language models an opportunity to learn more.

The researchers reported that this new dataset helped to improve conversational question-answering systems by over 40%.

The research paper describes the success of this approach:

“Importantly, we find that our inpainted datasets are powerful sources of training data for ConvQA systems…

When used to pre-train standard retriever and reranker architectures, they advance state-of-the-art across three different ConvQA retrieval benchmarks (QRECC, OR-QUAC, TREC-CAST), delivering up to 40% relative gains on standard evaluation metrics…

Remarkably, we find that just pre-training on WikiDialog enables strong zero-shot retrieval performance—up to 95% of a finetuned retriever’s performance—without using any in-domain ConvQA data. “

Is it possible that Google Bard was trained using the WikiDialog and WebDialog datasets?

It’s difficult to imagine a scenario where Google would pass on training a conversational AI on a dataset that is over 1,000 times larger.

But we don’t know for certain because Google doesn’t often comment on its underlying technologies in detail, except on rare occasions like for Bard or LaMDA.

Large Language Models That Link To Sources

Google recently published an interesting research paper about a way to make large language models cite the sources for their information. The initial version of the paper was published in December 2022, and the second version was updated in February 2023.

This technology is referred to as experimental as of December 2022.

You can download the PDF of the paper here: Attributed Question Answering: Evaluation and Modeling for Attributed Large Language Models (read the Google abstract here).

The research paper states the intent of the technology:

“Large language models (LLMs) have shown impressive results while requiring little or no direct supervision.

Further, there is mounting evidence that LLMs may have potential in information-seeking scenarios.

We believe the ability of an LLM to attribute the text that it generates is likely to be crucial in this setting.

We formulate and study Attributed QA as a key first step in the development of attributed LLMs.

We propose a reproducible evaluation framework for the task and benchmark a broad set of architectures.

We take human annotations as a gold standard and show that a correlated automatic metric is suitable for development.

Our experimental work gives concrete answers to two key questions (How to measure attribution?, and How well do current state-of-the-art methods perform on attribution?), and give some hints as to how to address a third (How to build LLMs with attribution?).”

This kind of large language model can train a system that can answer with supporting documentation that, theoretically, assures that the response is based on something.

The research paper explains:

“To explore these questions, we propose Attributed Question Answering (QA). In our formulation, the input to the model/system is a question, and the output is an (answer, attribution) pair where answer is an answer string, and attribution is a pointer into a fixed corpus, e.g., of paragraphs.

The returned attribution should give supporting evidence for the answer.”

This technology is specifically for question-answering tasks.

The goal is to create better answers – something that Google would understandably want for Bard.

  • Attribution allows users and developers to assess the “trustworthiness and nuance” of the answers.
  • Attribution allows developers to quickly review the quality of the answers since the sources are provided.

One interesting note is a new technology called AutoAIS that strongly correlates with human raters.

In other words, this technology can automate the work of human raters and scale the process of rating the answers given by a large language model (like Bard).

The researchers share:

“We consider human rating to be the gold standard for system evaluation, but find that AutoAIS correlates well with human judgment at the system level, offering promise as a development metric where human rating is infeasible, or even as a noisy training signal. “

This technology is experimental; it’s probably not in use. But it does show one of the directions that Google is exploring for producing trustworthy answers.

Research Paper On Editing Responses For Factuality

Lastly, there’s a remarkable technology developed at Cornell University (also dating from the end of 2022) that explores a different way to source attribution for what a large language model outputs and can even edit an answer to correct itself.

Cornell University (like Stanford University) licenses technology related to search and other areas, earning millions of dollars per year.

It’s good to keep up with university research because it shows what is possible and what is cutting-edge.

You can download a PDF of the paper here: RARR: Researching and Revising What Language Models Say, Using Language Models (and read the abstract here).

The abstract explains the technology:

“Language models (LMs) now excel at many tasks such as few-shot learning, question answering, reasoning, and dialog.

However, they sometimes generate unsupported or misleading content.

A user cannot easily determine whether their outputs are trustworthy or not, because most LMs do not have any built-in mechanism for attribution to external evidence.

To enable attribution while still preserving all the powerful advantages of recent generation models, we propose RARR (Retrofit Attribution using Research and Revision), a system that 1) automatically finds attribution for the output of any text generation model and 2) post-edits the output to fix unsupported content while preserving the original output as much as possible.

…we find that RARR significantly improves attribution while otherwise preserving the original input to a much greater degree than previously explored edit models.

Furthermore, the implementation of RARR requires only a handful of training examples, a large language model, and standard web search.”

How Do I Get Access To Google Bard?

Google is currently accepting new users to test Bard, which is currently labeled as experimental. Google is rolling out access for Bard here.

Google Bard is ExperimentalScreenshot from bard.google.com, March 2023

Google is on the record saying that Bard is not search, which should reassure those who feel anxiety about the dawn of AI.

We are at a turning point that is unlike any we’ve seen in, perhaps, a decade.

Understanding Bard is helpful to anyone who publishes on the web or practices SEO because it’s helpful to know the limits of what is possible and the future of what can be achieved.

More Resources:


Featured Image: Whyredphotographor/Shutterstock



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What Are They Really Costing You?

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What Are They Really Costing You?

This post was sponsored by Adpulse. The opinions expressed in this article are the sponsor’s own.

As managers of paid media, one question drives us all: “How do I improve paid ad performance?”. 

Given that our study found close variant search terms perform poorly, yet more than half of the average budget on Google & Microsoft Ads is being spent on them, managing their impact effectively could well be one of your largest optimization levers toward driving significant improvements in ROI. 

“Close variants help you connect with people who are looking for your business, despite slight variations in the way they search.” support.google.com

Promising idea…but what about the execution?

We analyzed over 4.5 million clicks and 400,000 conversions to answer this question: With the rise in close variants (intent matching) search terms, what impact are they having on budgets and account performance? Spoiler alert, the impact is substantial. 


True Match Vs. Close Variants: How Do They Perform?

To understand close variant (CV) performance, we must first define the difference between a true match and a close variant. 

 

What Is a True Match? 

We still remember the good-old-days where keyword match types gave you control over the search terms they triggered, so for this study we used the literal match types to define ‘close variant’ vs ‘true match’. 

  • Exact match keyword => search term matches the keyword exactly. 
  • Phrase match keyword => search term must contain the keyword (same word order).
  • Broad match keyword => search term must contain every individual word in the keyword, but the word order does not matter (the way modified broad match keywords used to work).   

 

What Is a Close Variant? 

If you’re not familiar with close variants (intent matching) search terms, think of them as search terms that are ‘fuzzy matched’ to the keywords you are actually bidding on. 

Some of these close variants are highly relevant and represent a real opportunity to expand your keywords in a positive way. 

Some are close-ish, but the conversions are expensive. 

And (no shocks here) some are truly wasteful. 

….Both Google and Microsoft Ads do this, and you can’t opt-out.

To give an example: if you were a music therapist, you might bid on the phrase match keyword “music therapist”. An example of a true match search term would be ‘music therapist near me’ because it contains the keyword in its true form (phrase match in this case) and a CV might be ‘music and art therapy’.


How Do Close Variants Compare to True Match?

Short answer… poorly, on both Google and Microsoft Ads. Interestingly however, Google showed the worst performance on both metrics assessed, CPA and ROAS. 

Image created by Adpulse, May 2024

1718772963 395 What Are They Really Costing You

Image created by Adpulse, May 2024

Want to see the data – jump to it here…

CVs have been embraced by both platforms with (as earlier stated), on average more than half of your budget being spent on CV variant matches. That’s a lot of expansion to reach searches you’re not directly bidding for, so it’s clearly a major driver of performance in your account and, therefore, deserving of your attention. 

We anticipated a difference in metrics between CVs and true match search terms, since the true match search terms directly align with the keywords you’re bidding on, derived from your intimate knowledge of the business offering. 

True match conversions should therefore be the low-hanging fruit, leaving the rest for the platforms to find via CVs. Depending on the cost and ROI, this isn’t inherently bad, but logically we would assume CVs would perform worse than true matches, which is exactly what we observed. 


How Can You Limit Wastage on Close Variants?

You can’t opt out of them, however, if your goal is to manage their impact on performance, you can use these three steps to move the needle in the right direction. And of course, if you’re relying on CVs to boost volume, you’ll need to take more of a ‘quality-screening’ rather than a hard-line ‘everything-must-go’ approach to your CV clean out!

 

Step 1: Diagnose Your CV Problem 

We’re a helpful bunch at Adpulse so while we were scoping our in-app solution, we built a simple spreadsheet that you can use to diagnose how healthy your CVs are. Just make a copy, paste in your keyword and search term data then run the analysis for yourself. Then you can start to clean up any wayward CVs identified. Of course, by virtue of technology, it’s both faster and more advanced in the Adpulse Close Variant Manager 😉.

 

Step 2: Suggested Campaign Structures for Easier CV Management  

Brand Campaigns

If you don’t want competitors or general searches being matched to your brand keywords, this strategy will solve for that. 

Set up one ad group with your exact brand keyword/s, and another ad group with phrase brand keyword/s, then employ the negative keyword strategies in Step 3 below. You might be surprised at how many CVs have nothing to do with your brand, and identifying variants (and adding negative keywords) becomes easy with this structure.

Don’t forget to add your phrase match brand negatives to non-brand campaigns (we love negative lists for this).

Non-Brand Campaigns with Larger Budgets

We suggest a campaign structure with one ad group per match type:

Example Ad Groups:

    • General Plumbers – Exact
    • General Plumbers – Phrase
    • General Plumbers – Broad
    • Emergency Plumbers – Exact
    • Emergency Plumbers – Phrase
    • Emergency Plumbers – Broad

This allows you to more easily identify variants so you can eliminate them quickly. This also allows you to find new keyword themes based on good quality CVs, and add them easily to the campaign. 

Non-Brand Campaigns with Smaller Budgets

Smaller budgets mean the upside of having more data per ad group outweighs the upside of making it easier to trim unwanted CVs, so go for a simpler theme-based ad group structure:

Example Ad Groups:

    • General Plumbers
    • Emergency Plumbers

 

Step 3: Ongoing Actions to Tame Close Variants

Adding great CVs as keywords and poor CVs as negatives on a regular basis is the only way to control their impact.

For exact match ad groups we suggest adding mainly root negative keywords. For example, if you were bidding on [buy mens walking shoes] and a CV appeared for ‘mens joggers’, you could add the single word “joggers” as a phrase/broad match negative keyword, which would prevent all future searches that contain joggers. If you added mens joggers as a negative keyword, other searches that contain the word joggers would still be eligible to trigger. 

In ad groups that contain phrase or broad match keywords you shouldn’t use root negatives unless you’re REALLY sure that the root negative should never appear in any search term. You’ll probably find that you use the whole search term added as an exact match negative much more often than using root negs.


The Proof: What (and Why) We Analyzed

We know CVs are part of the conversations marketers frequently have, and by virtue of the number of conversations we have with agencies each week, we’ve witnessed the increase of CV driven frustration amongst marketers. 

Internally we reached a tipping point and decided to data dive to see if it just felt like a large problem, or if it actually IS a large enough problem that we should devote resources to solving it in-app. First stop…data. 

Our study of CV performance started with thousands of Google and Microsoft Ads accounts, using last 30-day data to May 2024, filtered to exclude:

  • Shopping or DSA campaigns/Ad Groups.
  • Accounts with less than 10 conversions.
  • Accounts with a conversion rate above 50%.
  • For ROAS comparisons, any accounts with a ROAS below 200% or above 2500%.

Search terms in the study are therefore from keyword-based search campaigns where those accounts appear to have a reliable conversion tracking setup and have enough conversion data to be individually meaningful.

The cleaned data set comprised over 4.5 million clicks and 400,000 conversions (over 30 days) across Google and Microsoft Ads; a large enough data set to answer questions about CV performance with confidence.

Interestingly, each platform appears to have a different driver for their lower CV performance. 

CPA Results:

Google Ads was able to maintain its conversion rate, but it chased more expensive clicks to achieve it…in fact, clicks at almost double the average CPC of true match! Result: their CPA of CVs worked out roughly double the CPA of true match.                 

Microsoft Ads only saw slightly poorer CPA performance within CVs; their conversion rate was much lower compared to true match, but their saving grace was that they had significantly lower CPCs, and you can afford to have a lower conversion rate if your click costs are also lower. End outcome? Microsoft Ads CPA on CVs was only slightly more expensive when compared to their CPA on true matches; a pleasant surprise 🙂.

What Are They Really Costing You

Image created by Adpulse, May 2024

ROAS Results:

Both platforms showed a similar story; CVs delivered roughly half the ROAS of their true match cousins, with Microsoft Ads again being stronger overall. 

 

1718772963 395 What Are They Really Costing You

Image created by Adpulse, May 2024

Underlying Data:

For the data nerds amongst us (at Adpulse we self-identify here !) 

1718772963 88 What Are They Really Costing You

Image created by Adpulse, May 2024


TL;DR

Close variant search terms consume, on average, more than half an advertiser’s budget whilst in most cases, performing significantly worse than search terms that actually match the keywords. How much worse? Read above for details ^. Enough that managing their impact effectively could well be one of your largest optimization levers toward driving significant improvements in account ROI. 


Image Credits

Featured Image: Image by Adpulse. Used with permission.

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How To Uncover Traffic Declines In Google Search Console And How To Fix Them

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How To Uncover Traffic Declines In Google Search Console And How To Fix Them

Google Search Console is an essential tool that offers critical insights into your website’s performance in Google search results.

Occasionally, you might observe a sudden decline in organic traffic, and it’s crucial to understand the potential causes behind this drop. The data stored within Google Search Console (GSC) can be vital in troubleshooting and understanding what has happened to your website.

Before troubleshooting GSC traffic declines, it’s important to understand first what Google says about assessing traffic graphs in GSC and how it reports on different metrics.

Understanding Google Search Console Metrics

Google’s documentation on debugging Search traffic drops is relatively comprehensive (compared to the guidance given in other areas) and can, for the most part, help prevent any immediate or unnecessary panic should there be a change in data.

Despite this, I often find that Search Console data is misunderstood by both clients and those in the first few years of SEO and learning the craft.

Image from Google Search Central, May 2024

Even with these definitions, if your clicks and impressions graphs begin to resemble any of the above graph examples, there can be wider meanings.

Search Central description  It could also be a sign that…
Large drop from an algorithmic update, site-wide security, or spam issue This could also signal a serious technical issue, such as accidentally deploying a noindex onto a URL or returning the incorrect status code – I’ve seen it before where the URL renders content but returns a 410.
Seasonality You will know your seasonality better than anyone, but if this graph looks inverse it could be a sign that during peak search times, Google is rotating the search engine results pages (SERPs) and choosing not to rank your site highly. This could be because, during peak search periods, there is a slight intent shift in the queries’ dominant interpretation.
Technical issues across your site, changing interests This type of graph could also represent seasonality (both as a gradual decline or increase).
Reporting glitch ¯_(ツ)_/¯ This graph can represent intermittent technical issues as well as reporting glitches. Similar to the alternate reasons for graphs like Seasonality, it could represent a short-term shift in the SERPs and what meets the needs of an adjusted dominant interpretation of a query.

Clicks & Impressions

Google filters Click and Impression data in Google Search Console through a combination of technical methods and policies designed to ensure the accuracy, reliability, and integrity of the reported data.

Reasons for this include:

  • Spam and bot filtering.
  • Duplicate data removal.
  • User privacy/protection.
  • Removing “invalid activities.”
  • Data aggregation and sampling.

One of the main reasons I’ve seen GSC change the numbers showing the UI and API is down to the setting of thresholds.

Google may set thresholds for including data in reports to prevent skewed metrics due to very low-frequency queries or impressions. For example, data for queries that result in very few impressions might be excluded from reports to maintain the statistical reliability of the metrics.

Average Position

Google Search Console produces the Average Position metric by calculating the average ranking of a website’s URLs for a specific query or set of queries over a defined period of time.

Each time a URL appears in the search results for a query, its position is recorded. For instance, if a URL appears in the 3rd position for one query and in the 7th position for another query, these positions are logged separately.

As we enter the era of AI Overviews, John Mueller has confirmed via Slack conversations that appearing in a generative snapshot will affect the average position of the query and/or URL in the Search Console UI.

1718702762 996 How To Uncover Traffic Declines In Google Search Console AndSource: John Mueller via The SEO Community Slack channel

I don’t rely on the average position metric in GSC for rank tracking, but it can be useful in trying to debug whether or not Google is having issues establishing a single dominant page for specific queries.

Understanding how the tool compiles data allows you to better diagnose the reasons as to why, and correlate data with other events such as Google updates or development deployments.

Google Updates

A Google broad core algorithm update is a significant change to Google’s search algorithm intended to improve the relevance and quality of search results.

These updates do not target specific sites or types of content but alter specific systems that make up the “core” to an extent it is noteworthy for Google to announce that an update is happening.

Google makes updates to the various individual systems all the time, so the lack of a Google announcement does not disqualify a Google update from being the cause of a change in traffic.

For example, the website in the below screenshot saw a decline from the March 2023 core update but then recovered in the November 2023 core update.

GSC: the website saw a decline from the March 2023 core updateScreenshot by author from Google Search Console, May 2024

The following screenshot shows another example of a traffic decline correlating with a Google update, and it also shows that recovery doesn’t always occur with future updates.

traffic decline correlating with a Google updateScreenshot by author from Google Search Console, May 2024

This site is predominantly informational content supporting a handful of marketing landing pages (a traditional SaaS model) and has seen a steady decline correlating with the September 2023 helpful content update.

How To Fix This

Websites negatively impacted by a broad core update can’t fix specific issues to recover.

Webmasters should focus on providing the best possible content and improving overall site quality.

Recovery, however, may occur when the next broad core update is rolled out if the site has improved in quality and relevance or Google adjusts specific systems and signal weightings back in the favour of your site.

In SEO terminology, we also refer to these traffic changes as an algorithmic penalty, which can take time to recover from.

SERP Layout Updates

Given the launch of AI Overviews, I feel many SEO professionals will conduct this type of analysis in the coming months.

In addition to AI Overviews, Google can choose to include a number of different SERP features ranging from:

  • Shopping results.
  • Map Packs.
  • X (Twitter) carousels.
  • People Also Ask accordions.
  • Featured snippets.
  • Video thumbnails.

All of these not only detract and distract users from the traditional organic results, but they also cause pixel shifts.

From our testing of SGE/AI Overviews, we see traditional results being pushed down anywhere between 1,000 and 1,500 pixels.

When this happens you’re not likely to see third-party rank tracking tools show a decrease, but you will see clicks decline in GSC.

The impact of SERP features on your traffic depends on two things:

  • The type of feature introduced.
  • Whether your users predominantly use mobile or desktop.

Generally, SERP features are more impactful to mobile traffic as they greatly increase scroll depth, and the user screen is much smaller.

You can establish your dominant traffic source by looking at the device breakdown in Google Search Console:

Device by users: clicks and impressionsImage from author’s website, May 2024

You can then compare the two graphs in the UI, or by exporting data via the API with it broken down by devices.

How To Fix This

When Google introduces new SERP features, you can adjust your content and site to become “more eligible” for them.

Some are driven by structured data, and others are determined by Google systems after processing your content.

If Google has introduced a feature that results in more zero-click searches for a particular query, you need to first quantify the traffic loss and then adjust your strategy to become more visible for similar and associated queries that still feature in your target audience’s overall search journey.

Seasonality Traffic Changes

Seasonality in demand refers to predictable fluctuations in consumer interest and purchasing behavior that occur at specific times of the year, influenced by factors such as holidays, weather changes, and cultural events.

Notably, a lot of ecommerce businesses will see peaks in the run-up to Christmas and Thanksgiving, whilst travel companies will see seasonality peaks at different times of the year depending on the destinations and vacation types they cater to.

The below screenshot is atypical of a business that has a seasonal peak in the run-up to Christmas.

seasonal peaks as measured in GSCScreenshot by author from Google Search Console, May 2024

You will see these trends in the Performance Report section and likely see users and sessions mirrored in other analytics platforms.

During a seasonal peak, Google may choose to alter the SERPs in terms of which websites are ranked and which SERP features appear. This occurs when the increase in search demand also brings with it a change in user intent, thus changing the dominant interpretation of the query.

In the travel sector, the shift is often from a research objective to a commercial objective. Out-of-season searchers are predominantly researching destinations or looking for deals, and when it is time to book, they’re using the same search queries but looking to book.

As a result, webpages with a value proposition that caters more to the informational intent are either “demoted” in rankings or swapped out in favor of webpages that (in Google’s eyes) better cater to users in satisfying the commercial intent.

How To Fix This

There is no direct fix for traffic increases and decreases caused by seasonality.

However, you can adjust your overall SEO strategy to accommodate this and work to create visibility for the website outside of peak times by creating content to meet the needs and intent of users who may have a more research and information-gathering intent.

Penalties & Manual Actions

A Google penalty is a punitive action taken against a website by Google, reducing its search rankings or removing it from search results, typically due to violations of Google’s guidelines.

As well as receiving a notification in GSC, you’ll typically see a sharp decrease in traffic, akin to the graph below:

Google traffic decline from penaltyScreenshot by author from Google Search Console, May 2024

Whether or not the penalty is partial or sitewide will depend on how bad the traffic decline is, and also the type (or reason) as to why you received a penalty in the first place will determine what efforts are required and how long it will take to recover.

Changes In PPC Strategies

A common issue I encounter working with organizations is a disconnect in understanding that, sometimes, altering a PPC campaign can affect organic traffic.

An example of this is brand. If you start running a paid search campaign on your brand, you can often expect to see a decrease in branded clicks and CTR. As most organizations have separate vendors for this, it isn’t often communicated that this will be the case.

The Search results performance report in GSC can help you identify whether or not you have cannibalization between your SEO and PPC. From this report, you can correlate branded and non-branded traffic drops with the changelog from those in command of the PPC campaign.

How To Fix This

Ensuring that all stakeholders understand why there have been changes to organic traffic, and that the traffic (and user) isn’t lost, it is now being attributed to Paid.

Understanding if this is the “right decision” or not requires a conversation with those managing the PPC campaigns, and if they are performing and providing a strong ROAS, then the organic traffic loss needs to be acknowledged and accepted.

Recovering Site Traffic

Recovering from Google updates can take time.

Recently, John Mueller has said that sometimes, to recover, you need to wait for another update cycle.

However, this doesn’t mean you shouldn’t be active in trying to improve your website and better align with what Google wants to reward and relying on Google reversing previous signal weighting changes.

It’s critical that you start doing all the right things as soon as possible. The earlier that you identify and begin to solve problems, the earlier that you open up the potential for recovery. The time it takes to recover depends on what caused the drop in the first place, and there might be multiple factors to account for. Building a better website for your audience that provides them with better experiences and better service is always the right thing to do.

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Featured Image: Ground Picture/Shutterstock

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SEO

Barriers To Audience Buy-In

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Barriers to audience buy-in with lead generation

This is an excerpt from the B2B Lead Generation ebook, which draws on SEJ’s internal expertise in delivering leads across multiple media types.

People are driven by a mix of desires, wants, needs, experiences, and external pressures.

It can take time to get it right and convince a person to become a lead, let alone a paying customer.

Here are some nuances of logic and psychology that could be impacting your ability to connect with audiences and build strong leads.

1. Poor Negotiations & The Endowment Effect

Every potential customer you encounter values their own effort and information. And due to something called the endowment effect, they value that time and data much more than you do.

In contrast, the same psychological effect means you value what you offer in exchange for peoples’ information more than they will.

If the value of what you’re offering fails to match the value of what consumers are giving you in exchange (read: their time and information), the conversions will be weak.

The solution? You can increase the perceived value of the thing you’re offering, or reduce the value of what the user “pays” for the thing you offer.

Want an exclusive peek into tactics we use when developing our own lead gen campaigns? Check out our upcoming webinar.

Humans evaluate rewards in multiple dimensions, including the reward amount, the time until the reward is received, and the certainty of the reward.

The more time before a reward occurs, and the less certain its ultimate value, the harder you have to work to get someone to engage.

Offering value upfront – even if you’re presenting something else soon after, like a live event, ebook, or demo – can help entice immediate action as well as convince leads of the long-term value of their investment.

It can even act as a prime for the next step in the lead gen nurturing process, hinting at even more value to come and increasing the effectiveness of the rest of your lead generation strategy.

It’s another reason why inbound content is a critical support for lead generation content. The short-term rewards of highly useful ungated content help prepare audiences for longer-term benefits offered down the line.

3. Abandonment & The Funnel Myth

Every lead generation journey is carefully planned, but if you designed it with a funnel in mind, you could be losing many qualified leads.

That’s because the imagery of a funnel might suggest that all leads engage with your brand or offer in the same way, but this simply isn’t true – particularly for products or services with high values.

Instead, these journeys are more abstract. Leads tend to move back and forth between stages depending on their circumstances. They might change their minds, encounter organizational roadblocks, switch channels, or their needs might suddenly change.

Instead of limiting journeys to audience segments, consider optimizing for paths and situations, too.

Optimizing for specific situations and encounters creates multiple opportunities to capture a lead while they’re in certain mindsets. Every opportunity is a way to engage with varying “costs” for time and data, and align your key performance indicators (KPIs) to match.

Situational journeys also create unique opportunities to learn about the various audience segments, including what they’re most interested in, which offers to grab their attention, and which aspects of your brand, product, or service they’re most concerned about.

4. Under-Pricing

Free trials and discounts can be eye-catching, but they don’t always work to your benefit.

Brands often think consumers will always choose the product with the lowest possible price. That isn’t always the case.

Consumers work within something referred to as the “zone of acceptability,” which is the price range they feel is acceptable for a purchasing decision.

If your brand falls outside that range, you’ll likely get the leads – but they could fail to buy in later. The initial offer might be attractive, but the lower perception of value could work against you when it comes time to try and close the sale.

Several elements play into whether consumers are sensitive to pricing discounts. The overall cost of a purchase matters, for example.

Higher-priced purchases, such as SaaS or real estate, can be extremely sensitive to pricing discounts. They can lead to your audience perceiving the product as lower-value, or make it seem like you’re struggling. A price-quality relationship is easy to see in many places in our lives. If you select the absolute lowest price for an airline ticket, do you expect your journey to be timely and comfortable?

It’s difficult to offer specific advice on these points. To find ideal price points and discounts, you need good feedback systems from both customers and leads – and you need data about how other audiences interact. But there’s value in not being the cheapest option.

Get more tips on how we, here at SEJ, create holistic content campaigns to drive leads in this exclusive webinar.

5. Lead Roles & Information

In every large purchasing decision, there are multiple roles in the process. These include:

  • User: The person who ultimately uses the product or service.
  • Buyer: The person who makes the purchase, but may or may not know anything about the actual product or service being purchased.
  • Decider: The person who determines whether to make the purchase.
  • Influencer: The person who provides opinions and thoughts on the product or service, and influences perceptions of it.
  • Gatekeeper: The person who gathers and holds information about the product or service.

Sometimes, different people play these roles, and other times, one person may hold more than one of these roles. However, the needs of each role must be met at the right time. If you fail to meet their needs, you’ll see your conversions turn cold at a higher rate early in the process.

The only way to avoid this complication is to understand who it is you’re attracting when you capture the lead, and make the right information available at the right time during the conversion process.

6. Understand Why People Don’t Sign Up

Many businesses put significant effort into lead nurturing and understanding the qualities of potential customers who fill out lead forms.

But what about the ones who don’t fill out those forms?

Understanding these values and the traits that drive purchasing decisions is paramount.

Your own proprietary and customer data, like your analytics, client data, and lead interactions, makes an excellent starting place, but don’t make the mistake of basing your decisions solely on the data you have collected about the leads you have.

This information creates a picture based solely on people already interacting with you. It doesn’t include information about the audience you’ve failed to capture so far.

Don’t fall for survivorship bias, which occurs when you only look at data from people who have passed your selection filters.

This is especially critical for lead generation because there are groups of people you don’t want to become leads. But you need to make sure you’re attracting as many ideal leads as possible while filtering out those that are suboptimal. You need information about the people who aren’t converting to ensure your filters are working as intended.

Gather information from the segment of your target audience that uses a competitor’s products, and pair them with psychographic tools and frameworks like “values and lifestyle surveys” (VALS) to gather insights and inform decisions.

In a digital world of tough competition and even more demands on every dollar, your lead generation needs to be precise.

Understanding what drives your target audience before you capture the lead and ensuring every detail is crafted with the final conversion in mind will help you capture more leads and sales, and leave your brand the clear market winner.

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Featured Image: Pasuwan/Shutterstock

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