Feb 5 2025
New Books on Strategy, Resilience, AI, More

These new titles offer practical tips and insights for ecommerce success —  leadership, strategy, analytics, customer relationships, culture, and more.

Inspire: The Universal Path for Leading Yourself and Others

Cover of Inspire

Inspire

by Adam Galinsky

Galinsky, a psychologist and authority on leadership, analyzes why some leaders inspire and others infuriate. He explains how anyone can improve at leading, problem-solving, and decision-making. He combines compelling stories, research, and practical tips for drawing the best out of others as a leader, boss, coach, parent, or individual.

Reset: How to Change What’s Not Working

Cover of Reset

Reset

by Dan Heath

The bestselling author of “Made to Stick,” “Switch,” and “The Power of Moments” returns with a guide to changing how we work. He addresses the points where a little effort can produce a big return, showing readers how to move forward and get better results from people and resources. The result, he says, is getting unstuck in systems, processes, company, and life.

The Obvious Choice: Timeless Lessons on Success, Profit, and Finding Your Way

Cover of The Obvious Choice

The Obvious Choice

by Jonathan Goodman

A leading practitioner in simplifying businesses aims to explode the myth that entrepreneurs need to become “internet famous” to succeed in ecommerce. The book promises to help readers earn more and compete less by prioritizing the human customer over the ever-changing algorithm.

This Is Strategy: Make Better Plans

Cover of This Is Strategy

This Is Strategy

by Seth Godin

“Creating tomorrow by repeating yesterday is not a useful way forward,” says the bestselling author, speaker, and internet marketing guru. Godin’s new book focuses on thinking strategically amid constant change, going beyond immediate tactics to create meaningful long-term progress.

Triple Fit Strategy: How to Build Lasting Customer Relationships and Boost Growth

Cover of Triple Fit Strategy

Triple Fit Strategy

by Christoph Senn, Mehak Gandhi

The authors have helped numerous B2B companies grow through the strategic collaboration of suppliers and customers that improves planning, execution, and resource allocation and accelerates growth for both parties. This practical guide to their framework includes examples from their 25 years of consulting.

Personalized: Customer Strategy in the Age of AI

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Personalized

by Mark Abraham, David C. Edelman

Done right, personalization can improve customer engagement. Done badly, it has the opposite effect. The authors use examples from a range of industries to show how artificial intelligence can help marketers deliver “Five Promises of Personalization.”

Analytics the Right Way: A Business Leader’s Guide to Putting Data to Productive Use

Cover of Analytics the Right Way

Analytics the Right Way

by Tim Wilson, Joe Sutherland

Business leaders hoping for actionable insights often flounder with hard-to-interpret data. Wilson (a former Practical Ecommerce contributor) and Sutherland use real-world examples, humorous hypotheticals, and clear illustrations to create a practical guide to using fundamental statistical concepts in today’s business environment.

The Nvidia Way: Jensen Huang and the Making of a Tech Giant

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The Nvidia Way

by Tae Kim

The author draws on extensive interviews with Nvidia’s founders, early investors and employees, and current executives to explain how the company weathered early challenges to fuel the AI revolution. He explains how Nvidia’s unique culture and structure enabled it to pivot from its 1993 beginning as a niche provider of gaming chips to become the global sought-after technology.

The Enduring Enterprise: How Family Businesses Thrive in Turbulent Conditions

Cover of The Enduring Enterprise

The Enduring Enterprise

By Ivan Lansberg, Devin Deciantis

Family-owned companies span the globe and dominate some of its most challenging circumstances — surviving war, political instability, market failures, and environmental disasters. The authors draw on their extensive experience consulting with family firms worldwide to share the real-world strategies these families use to create stability and prosperity. It’s a model for all companies in uncertain times, the authors state.

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Feb 4 2025
Google’s Q4 Earnings Point To An AI-Focused Future via @sejournal, @MattGSouthern

Alphabet Inc., Google’s parent company, reported strong fourth-quarter results for fiscal 2024, primarily driven by its commitment to AI.

Alphabet announced revenues of $96.5 billion for Q4 2024, up 12% from last year.

Google Services, including Search and YouTube ads, grew by 10% to $84.1 billion.

Google Cloud increased revenues by 30% to $12.0 billion as more businesses adopted its AI services.

Operating income rose by 31%, and net income increased by 28% to $26.5 billion.

AI-Driven Growth

CEO Sundar Pichai highlighted the company’s AI achievements and recent launches during the earnings call.

Pichai said:

“Q4 was a strong quarter driven by our leadership in AI and momentum across the business. We’re making dramatic progress across compute, model capabilities, and in driving efficiencies. We’re rapidly shipping product improvements, and seeing terrific momentum with consumer and developer usage.”

Infrastructure Investments

Alphabet is investing heavily in its infrastructure, launching new data centers and subsea cable projects to improve global connectivity.

Pichai stated:

“We broke ground on 11 new Cloud regions and data center campuses in places like South Carolina, Indiana, Missouri, and around the world. We also announced plans for seven new subsea cable projects, strengthening global connectivity.”

These efforts will support the growth of AI services, as data centers now provide nearly four times more computing power for the same energy.

Implications for Search and Marketing

Google reported that its AI-powered search features are gaining traction. AI Overviews are now available in more than 100 countries.

Circle to Search, available on over 200 million Android devices, is popular among younger users, who now use it for more than 10% of their searches.

SEO professionals and digital marketers should brace for further changes, as Pichai declared that “2025 is going to be one of the biggest years for Search innovation yet.”

The company’s $75 billion capital expenditure plan for 2025 suggests significant investments in search technology and AI capabilities.

Looking Ahead

Google’s Q4 results highlight its focus on AI. Overall revenue increased 12%, and the cloud business grew 30%. Profits increased as well, with operating income rising 31%.

The company’s investments in data centers and undersea cables will support global AI growth.

New AI features, such as Search Overviews and Circle to Search, are changing user behavior, so SEO teams should prepare for more changes in 2025.

Keep an eye on Google’s $75 billion spending plan to expand AI technology.

Featured Image: Dennis Diatel/Shutterstock

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Feb 4 2025
LinkedIn Video Views Up 36%, New Tools & Courses Available via @sejournal, @MattGSouthern

LinkedIn video viewership is up 36% YoY. The platform adds new tools and free training courses to boost video creation.

  • LinkedIn video watch time is outpacing other content formats.
  • New creator tools include profile previews, enhanced analytics, and desktop video features.
  • Free LinkedIn Learning courses can help you learn more about video’s role in professional communication.
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Feb 4 2025
Death Of The Keyword: Why Aggregate Organic Traffic Is A Better Metric via @sejournal, @Kevin_Indig

This is my official eulogy for the SEO keyword, which died many years ago, but no one has noticed.

As a result, many marketing teams make sub-par decisions, and decision-makers lose trust in SEO as a channel. Just look at the recent “SEO is dead” reactions to HubSpot’s traffic decline.

Google Enforces JavaScript For Crawling

Since January 20, Google has required JavaScript for Search, which makes rank tracking more expensive.

This is the latest move in a long-standing battle between SEO pros and Google.

Rank trackers (Semrush, Ahrefs, SEOmonitor, etc.) have been operating in a gray zone. Google tolerates them but officially doesn’t allow it.

Now, with JavaScript as a hard requirement, rank tracking needs more RAM, which increases the cost of every data point.

The result is that the cost of doing SEO is rising. But rank tracking lost its value long before Google switched to JavaScript crawling.

Focusing On Single Keywords Hasn’t Made Sense In A While

Image Credit: Kevin Indig

With all the elements in the SERPs and the unrepresentative data we get, it’s hard to project impact and measure success purely based on keyword ranks.

  • In 2013, Google stopped sharing keyword referrers. The only way to understand what users searched to get to your site was and still is Google Search Console.
  • In 2014, Google started showing Featured Snippets, direct answers at the top of the search results, which created more winner-takes-it-all situations. Subsequent SERP features like People Also Asked or video carousels followed and climbed up the ranks. Today, over 30 known SERP Features compete for attention with classic search results. It’s very hard to predict how many clicks you might get because there are so many combinations of SERP Features.1
  • Since last year, Google has shown ads for organic results and has broken the traditional separation between organic and paid results.
  • And, of course, last year, Google launched AI Overviews. The AI answers are now available in over 100 countries and provide in-depth answers. Clicking through to search results is now redundant in some cases.
  • The data Google shares around these trends range from non-existent to bare. AI Overviews or SERP Features are not included in Search Console Data. Not even speaking of the fact that Google filters out 50% of query data for “privacy reasons”: Ahrefs looked at 150,000 websites and found that about 50% of keywords and clicks are hidden.2

On one hand, a single webpage can rank for thousands of keywords as long as those keywords express the same intent and the page gives a good answer to all implied questions. This has been the case for many years now.

On the other hand, more and more keywords don’t deliver traffic because all clicks go to a SERP Feature that keeps people in the search results, or a click isn’t necessary – searchers get the answer in the search results.

Sparktoro found that +37% of searches end without a click, and +21% result in another search.3

Image Credit: Kevin Indig

A couple of months ago, I rewrote my guide to in-house SEO and started ranking in position one. But the joke was on me. I didn’t get a single dirty click for that keyword.

Over 200 people search for [inhouse seo], but not a single person clicks on a search result.

By the way, Google Analytics only shows 10 clicks from organic search over the last three months.

So, what’s going on? The 10 clicks I actually got are not reported in GSC (privacy… I guess?), but the majority of searchers likely click on one of the People Also Asked features that show up right below my search result.

The bigger picture is that the value of keywords and ranks has tanked.

Our response can be two-fold:

  • First, while the overarching goal should still be to rank at the top, we need to target the element that’s most likely to get all the attention in the SERPs, like video carousels or AI answers. In some cases, that means expanding “SEO” to other platforms like YouTube.
  • Second, we need to look at aggregate data.

Aggregate Traffic > Keywords

We’re still operating with the old model of SEO, which is where we track a list of keywords to measure success and set targets.

But how much sense does that make given pages rank for many keywords? How much sense does it make a given search to move from a list of results to LLM answers?

The keyword doesn’t have a future in search. What does is intent, and LLMs are much better at understanding it.

So, here is my suggestion: Instead of focusing on keywords, we should focus on organic traffic aggregated on the page or domain level.

Some traps to watch out for:

  • We still need keywords to model brand vs. non-brand traffic by page (still works because you should have enough keywords).
  • Beware of seasonality.
  • Split organic traffic out by new vs. existing pages.

To track how well a domain or page is doing, we can still look at keywords; the direction of organic traffic is more indicative of whether it does well or not.

One big issue I have with keywords is that search volume has many flaws and is so unrepresentative of what’s actually going on.

The term “in-house seo” has a reported search volume of 90-200 in the biggest rank trackers but doesn’t actually deliver any clicks.

To know what pages to create without keyword research, talk to customers and analyze what topics and questions they care about.

Analyze platforms like Reddit and YouTube for engagement and reverse engineer what topics work.

And, we can – and probably will have to – use paid search data to inform SEO because it’s more reflective of topics with the way that Google shows (Performance Max) ads to users in Search, which are similar to user intent.

To project traffic, look at domains or pages that are already visible for topics we care about, just not on the keyword level.

Clickstream data that reflects how users browse the web is much better because it doesn’t project potential traffic based on a keyword position.

Where keywords still make (some sense) is for analyzing historical search volume to project whether a topic is growing or shrinking, but I suggest using only large amounts of keywords.

A huge benefit of the aggregate traffic approach is that it transfers well to LLM because they don’t give us queries either, but we can track referral traffic on the domain and page level.

ChatGPT even adds a URL parameter to outgoing clicks that makes tracking easier.

LLM Defense: Activated

Image Credit: Kevin Indig

The real reason Google enforces JavaScript crawling is not to hurt SEO professionals but GenAI competitors.

ChatGPT and Perplexity are gaining significant ground. ChatGPT has already surpassed the traffic of Bing and Google’s Gemini. Perplexity is on the way there.

However, LLM crawlers can’t execute JavaScript, which means they can now no longer crawl Google’s search results to ground their answers.

(By the way, you need to make sure your content is accessible without JavaScript. Otherwise, LLMs can’t crawl it, and you can’t appear in their answers.)

The quality of some LLMs might decrease due to Google enforcing JavaScript, but only temporarily. LLMs can still get SERP data in other ways.

But one potential consequence of Google’s decision is that LLM developers build their own models or web indices to weigh answers and become independent of search engines.

The second-order effect of that would be that it’s no longer enough to do good SEO to appear in LLMs. We would have to reverse engineer LLM results like we did with Google.

If SEO is a game, winning in SEO now requires adjusting based on the flow (intent) of the game instead of counting cards (keywords).

The future of search is not keywords but intentions.

Let this be my official eulogy.

Image Credit: Kevin Indig

1 Semrush Sensor

2 Almost Half of GSC Clicks Go to Hidden Terms – A Study by Ahrefs

3 2024 Zero-Click Search Study

Featured Image: Paulo Bobita/Search Engine Journal

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Feb 4 2025
5 New SEO Ranking Challenges You’re Facing Right Now [& A Fix] via @sejournal, @bright_data

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

Struggling to adapt your SEO strategy to ever-changing AI-driven SERPs?

Have the most recent Google updates left your rank-tracking methods outdated?

What happens when you can no longer deliver key information on traffic sources?

Generative AI (GenAI) technologies like Google Gemini and Bard are reshaping search results.

This is creating unprecedented challenges, especially when it comes to the elephant in the room: “The New Position 0.”

In this article, we’ll help tackle the key ways to:

The Latest Google Updates & What They Mean For You

Just this week, Google rolled out unexpected changes to SERP structures, causing widespread disruptions for many SEO strategies that rely on SERP rankings.

These updates led to outages and inaccurate data across the industry, forcing many businesses to quickly adapt to avoid prolonged disruptions.

“The disruptions caused by Google’s latest SERP changes left many platforms unable to deliver accurate data to their users. Our clients, however, were unaffected thanks to our immediate response and robust infrastructure. If not for the media and search community, they wouldn’t have known there were any changes.” – Ariel Shulman, VP Product at Bright Data

Generative AI has fundamentally altered how search engines deliver results.

Classic SERP features have become central to understanding user intent and the user journey.

Until now.

The SERP layout we know and love has changed.

These overall changes present challenges for rank-tracking platforms tasked with capturing and analyzing SERPs:

  • Dynamic Content: AI-generated answers often feature multimedia, conversational snippets, or interactive elements, making parsing and analyzing data increasingly complex.
  • Personalization: Search results now adjust based on user history, geography, and device type, requiring platforms to capture nuanced, context-specific data.
  • Position-Zero Dominance: The growing prominence of position zero highlights the need for precise tracking and optimization insights tailored to this feature.

The challenge for rank-tracking platforms is clear: adapt to these AI-driven shifts or risk leaving users without the insights they need to thrive.

What Is Position 0 On Google?

Position 0 on Google refers to any of the featured snippets that appear at the very top of the search engine results page (SERP), above all organic search results.

It’s a special box that highlights concise information in response to a query, often in the form of a paragraph, list, or table.

For example, if you search for “How to tie a tie,” the featured snippet might display step-by-step instructions directly at the top. Being in Position 0 can boost your SEO strategy significantly since it’s considered premium real estate in search rankings.

The featured snippet is designed to provide users with quick answers to their questions without requiring them to click on a website. It’s highly coveted by website owners because it significantly increases visibility and click-through rates.

However, it’s becoming more difficult to track what is ranking in position 0 due to 5 different issues that baffle the current MarTech stack.

So, let’s dive deeper: What did Google change that you’ll need to change?

1. New Changes To SERP Layout Makes Ranking & Organic Clicks More Difficult

As we’re seeing in real-time, AI technologies like Google Gemini (previously Bard) and Microsoft’s Bing AI are reshaping SERP layouts.

So far, SERP structures are evolving rapidly with AI updates.

Elements like conversational answers or rich media snippets appear inconsistently, requiring you to constantly adapt your SEO strategy and data collection methods.

SERP - web scraping with AIO

The New Order Of Search Results

Instead of seeing Google Ads placements, featured snippets, positions 1-3, and People Also Ask, we’ll now be seeing:

  1. Google Ads take up more space, with up to 4 results that could also contain additional expanded site links.
  2. Google AI Overviews (AIO) now dominate the SERP above the fold, pushing positions 1-10 below interactive snippets.
  3. Featured Snippets take the space where positions 1-3 used to live, pushing position 1 down further.
  4. People Also Ask also comes before position 1.
  5. Position 1 starts here.

However, this is not its final state. The new layout of SERPs is dynamic; it will continue to change, and you have to be ready.

Position 1 No Longer Dominates

With position 1 pushed down at least 3 scroll lengths, this is no longer the top clicked result.

Additionally, the layout of a searcher’s query will also vary based on new advances in SERP personalization.

Now, clicks are possible in many new locations on the SERP, such as in a cited link for Google’s AI Overview (AIO).

These are not as easily trackable nor attributable.

SEO analysts and SEO strategists will see a massive impact on their traffic data and how they optimize their content to display above the fold.

Google’s AI Overview (AIO) Steals Top Clicks

Finally, position 1 on Google SERPs has seen its decline from providing at least 33% of organic search clicks to just 11% as of January 2025 depending on the search term.

Organic CTR declined ~70% when an AIO was present on the SERP.

AIO not only makes it difficult to obtain clicks, but one final change has made it nearly impossible to attribute clicks to positions.

In short:

  • You will lose traffic.
  • You will lose visibility into where your traffic is coming from.
  • You will lose the ability to strategize your content for visibility on SERPs.

What does this mean?

Google AIO and other dynamic SERP features are the new Position Zero.

2. Dynamic & Lazy-Loading SERP Content Hides Key SEO Data

Many SERP elements, especially those influenced by AI, load dynamically based on user interaction.

As we know from past SEO knowledge, dynamic and lazy-loading content cannot be seen by bots and scrapers until something triggers the content to load.

Therefore, to retrieve all the necessary data, you’ll need to simulate interactions like clicks and scrolls, which adds complexity and latency.

3. Google’s Anti-Bot Measures Removes Your Visibility To Rankings

As you can see so far, dynamic and personalized search results are more prominent.

Your favorite SEO keyword research and rank-tracking tools rely on bots to crawl the web for key data to help you build your SEO strategy.

However, Google has removed a large piece that makes that data scraping possible: bots.

Google’s evolving anti-bot measures further complicate real-time data collection, pushing platforms to the brink of what their systems can handle.

Sophisticated anti-bot defenses, such as CAPTCHA challenges, IP-based blocking, and JavaScript obfuscation, make real-time data collection a significant hurdle. Many traditional scraping tools cannot meet these challenges.

4. Personalized & Regionalized SERPs Removes Control Data

Control data is something that remains the same from one experiment to another. It enables you to have direct comparisons to build conclusions from when you’re building your SEO strategy.

The old SERP’s control data was the standardized layout. SERP layouts and results were similar enough for the same search query, meaning you could compare multiple searches for the same query to create conclusions that drove your strategy.

Now, user-specific factors like location, language, and device type create unique SERP views with vastly different orders of results.

It’s no longer simple to look at SERP data for [What is a 5-star hotel?] and know which link was clicked from which position:

  • User 1 could have been served your link in an AIO, which would not show up in classic SEO tools.
  • User 2 could have clicked on your link in position 1, which would show up in classic SEO tools.

Capturing this variability at scale while maintaining accuracy is critical yet immensely difficult.

5. Evolving Answer Content & Embracing Answer Engine Optimization (AEO)

AI-generated responses are constantly updated, with position-zero content shifting based on new data and context.

These AI-generated responses are part of an evolution of Search Engine Optimization (SEO) called Answer Engine Optimization (AEO).

You’ll need tools that have been updated to extract data from Answer Engines in real-time.

How To Gain Traffic From Position 0 & Regain Organic Traffic from SERPs

To regain your lost traffic, you’ll need to refer to more sophisticated tools to gain access to new SERP data streams to inform your organic traffic strategy.

Tools that previously helped you understand your position on SERPs are now outdated.

Rank-tracking platforms that have upgraded should be ready and able to collect data using more modern sources that align with the roadblocks above.

These tools don’t just need to collect data, they need to deliver actionable insights to help their users optimize for “The New Position 0” based on the data and AIO’s best practices. By extracting the right data and presenting it clearly, platforms empower users to improve their strategies effectively.

Here’s how platforms are leveraging Data from GenAI results:

1. Emphasizing Content Designed for AEO

Platforms will need insights into which content types (e.g., FAQs, schema markup, and structured data) are prioritized by AI-driven search engines. This will help their users create concise, authoritative content that aligns with SERP preferences, improving visibility and relevance in position zero.

2. Focusing on Position Zero Metrics

They will need metrics such as click-through rates (CTR), impressions, and engagement specific to position zero. These metrics will help their users monitor performance and refine their strategies to maintain or improve their rankings.

3. Supporting Regional and Device-Specific Insights

Platforms will need geo-targeted and device-specific data to provide segmented insights. This will help their users tailor their optimization efforts to specific regions, languages, or devices, ensuring their strategies are more precise and effective.

4. Adjusting to Conversational Queries

They will need data on conversational and intent-driven search queries. This will help their users align their content with how large language models prioritize conversational patterns, resulting in higher engagement and relevance.

SEO tools using Bright Data’s toolkit have access to all this data in real-time and at scale. That’s why the leading SEO tools choose Bright Data as their go-to data provider. Platforms leveraging these insights position themselves as indispensable tools for helping their users dominate “The New Position 0.”

Conclusion

As “The New Position 0” continues to redefine search, rank-tracking platforms face mounting challenges in delivering accurate, actionable data. Choosing the right data collection partner is no longer optional, it’s the key to staying ahead. Platforms leveraging Bright Data’s SERP API are equipped to meet these challenges, empowering their users to succeed in an AI-driven search landscape.

Bright Data’s proactive approach meant their clients experienced uninterrupted services during the disruptions that affected many in the industry. SEO tools leveraging Bright Data’s SERP API maintained seamless operations, continuing to deliver accurate, real-time insights to their users without issue.

Integrating your platform with Bright Data’s SERP API is quick and straightforward. Want to see what it’s all about? Check out the documentation here or test it out in the SERP API playground to see if it’s the perfect match for your SEO tool. When data matters, companies choose Bright Data.

This article has been sponsored by Bright Data, and the views presented herein represent the sponsor’s perspective.

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Image Credits

Featured Image: Image by Bright Data. Used with permission.

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Feb 4 2025
Maximizing Foot Traffic With Hyper-Targeted Local PPC Strategies

As someone who knows a lot of local business owners, I know how important it is to get customers through your doors.

While traditional marketing methods like flyers and newspaper ads still have their place, the digital era has opened up incredible new ways to reach local audiences and drive foot traffic, thanks to PPC advertising.

PPC platforms like Google Ads offer fairly granular geographic targeting options, allowing you to show ads only to people in the area(s) you serve. However, effective local PPC goes beyond setting a radius around your store location.

You can drastically improve your campaigns by leveraging advanced strategies and features to bring more local customers to your business.

Get Granular With Location Targeting

The foundation of any local PPC campaign is location targeting. Most marketers know the basics, like targeting by country, state, city, or ZIP code. But did you know you can get even more granular than that?

With Google Ads, you can target (or exclude) specific neighborhoods, universities, airports, and more.

Consider targeting popular shopping areas or entertainment districts near you for retail stores and restaurants.

B2B brands can focus on commercial zones or even specific office buildings (if large enough). The key is to consider where your ideal customers spend time and tailor your targeting accordingly.

You can even set different bid adjustments for different locations.

For example, if your base bid is $1.00 and you set a +20% bid adjustment for a high-performing neighborhood, Google will multiply your base bid by 1.2 (the 20% bid adjustment), allowing you to bid up to $1.20 for clicks from that area.

This tells Google you’re willing to pay more for clicks from locations that consistently drive better results.

Alternatively, you can use negative bid adjustments to scale back spend in lower-performing areas.

Hyperlocal Search Ads With Location Extensions

Google Ads location extensions allow your address and even directions to appear alongside your search ads.

When a user searches for a relevant local query, like [plumber near me], your ad can show your address, hours, phone number, and star rating.

Searchers can click your ad to get directions on Google Maps, drastically increasing the odds they visit you in person.

For location extensions to work, you must connect your Google Ads account with your Google Business Profile listing. Make sure your GBP info is complete and up-to-date.

Adding photos can make your listing stand out even more.

Google Local Service Ads: A Game-Changer For Service Businesses

Local Service Ads (LSAs) are available for over 100 service-based businesses in select countries worldwide, including Canada and all U.S. markets.

LSAs have now become crucial for local marketing success. These ads appear at the very top of Google search results – a position that even regular PPC ads can’t guarantee anymore.

Two Types Of LSA Verification:

1. Google Guaranteed

  • Primarily for home services.
  • Features a green checkmark with a circle.
  • Includes up to $2,000 in job guarantees for customers.
  • Higher requirements for insurance and licensure.

2. Google Screened

  • For professional services (lawyers, real estate agents, medical professionals).
  • Builds trust through verification.
  • No job guarantee.
  • Available for diverse businesses, including law firms, funeral homes, schools, and veterinary services.

Both types of verification involve a thorough process that businesses must undergo to prove their credibility and establish a trustworthy service for customers.

It begins with background checks that look into the history of the business and its owners. Businesses are also required to have at least $250,000 in general liability insurance for financial protection.

License verification is another crucial step, confirming that the business complies with local regulations and holds the necessary credentials to operate.

Finally, businesses are subject to regular reviews and compliance checks to guarantee they consistently meet industry standards and remain reputable over time.

Where LSAs Appear:

  • Top of search results (typically in two to three packs, expandable to eight, then 20).
  • Inside Google Maps (iOS app currently, likely expanding to Android).
  • Mobile search results.
  • During peak conversion times.
  • Within the local business finder map.

Key Performance Factors:

  • Smart bid and budget management.
  • The 3 R’s: Radius, Responsiveness, and Reviews.
  • Quick adoption of new features.
  • High-quality photo uploads.
  • Proper job booking management within the platform.

When asked what his number one tip would be, LSA expert Anthony Higman said, “Make sure you set up a profile if you’re in an eligible LSA category because it is becoming a necessity for local-based marketing strategies.”

We spoke together about Direct Business Search and I found it interesting when Higman said this, “Direct Business Search (DBS) is LSA’s branded search ad. So, you will show up for a branded search and that green checkmark will appear next to your ad.”

He went on to say, “This feature is new (so many are not fully utilizing it yet), and it’s completely within policy to double serve on your branded search campaign.

This means you can have a DBS with the green checkmark on top of your regular paid search ad. The caveat is that Google determines Direct Business Search leads by asking the customer to press 1 on their phone.

If they don’t press one before the call disconnects, you can be charged the full price of the lead. So tread carefully.”

Incorporate First-Party Data

Do you have a list of previous customer addresses, emails, or phone numbers?

With Customer Match Lists, you can upload this first-party data to Google Ads and create targeted campaigns for people who have already engaged with your business.

Since these folks are familiar with your brand, they’re more likely to visit you again, especially with the right offer.

This works particularly well for local businesses running seasonal promotions or trying to re-engage past customers who haven’t visited in a while.

Just be sure to follow Google’s policies regarding customer data usage and privacy.

Measuring Offline Conversions

Marketers have long struggled to connect digital ads to physical store visits. However, Google offers pretty good offline conversion tracking.

If you collect customer info at the point of sale, like an email or loyalty card number, you can import that data back into Google Ads.

Google then cross-references it with users who saw or clicked one of your search ads. This allows you to track things like in-store purchases or appointment bookings back to the PPC keywords and ads that drove them.

For larger retailers, Google also offers store visit conversions, which uses anonymized location history data to estimate how many users visited your location after engaging with an ad.

While it may not be perfect, these metrics provide valuable insight into how your local PPC efforts translate to real-world results.

Bringing It All Together

Driving foot traffic with paid ads requires a multifaceted approach.

You can create a local search presence that gets more customers through the door by combining precise location targeting, Google Business Profile optimizations, Local Services Ads, first-party data, and offline conversion tracking.

It’s important to remember to continually test, measure, and optimize based on what’s working.

Like any initiative, local campaigns succeed through a commitment to iterative improvement.

Even the smallest local businesses can become local search superstars with some savvy and elbow grease.

More Resources:

Featured Image: spoialabrothers/Shutterstock

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Feb 4 2025
OpenAI’s new agent can compile detailed reports on practically any topic

OpenAI has launched a new agent capable of conducting complex, multistep online research into everything from scientific studies to personalized bike recommendations at what it claims is the same level as a human analyst.

The tool, called Deep Research, is powered by a version of OpenAI’s o3 reasoning model that’s been optimized for web browsing and data analysis. It can search and analyze massive quantities of text, images, and PDFs to compile a thoroughly researched report.

OpenAI claims the tool represents a significant step toward its overarching goal of developing artificial general intelligence (AGI) that matches (or surpasses) human performance. It says that what takes the tool “tens of minutes” would take a human many hours.

In response to a single query, such as “Draw me up a competitive analysis between streaming platforms,” Deep Research will search the web, analyze the information it encounters, and compile a detailed report that cites its sources. It’s also able to draw from files uploaded by users.

OpenAI developed Deep Research using the same “chain of thought” reinforcement-learning methods it used to create its o1 multistep reasoning model. But while o1 was designed to focus primarily on mathematics, coding, or other STEM-based tasks, Deep Research can tackle a far broader range of subjects. It can also adjust its responses in reaction to new data it comes across in the course of its research.

This doesn’t mean that Deep Research is immune from the pitfalls that befall other AI models. OpenAI says the agent can sometimes hallucinate facts and present its users with incorrect information, albeit at a “notably” lower rate than ChatGPT. And because each question may take between five and 30 minutes for Deep Research to answer, it’s very compute intensive—the longer it takes to research a query, the more computing power required.

Despite that, Deep Research is now available at no extra cost to subscribers to OpenAI’s paid Pro tier and will soon roll out to its Plus, Team, and Enterprise users.

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Feb 4 2025
The Download: following DeepSeek’s lead, and OpenAI’s new research agent

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

How DeepSeek ripped up the AI playbook—and why everyone’s going to follow its lead

When the Chinese firm DeepSeek dropped a large language model called R1 two weeks ago, it sent shock waves through the US tech industry. Not only did R1 match the best of the homegrown competition, it was built for a fraction of the cost—and given away for free.

DeepSeek has now suddenly become the company to beat. What exactly did it do to rattle the tech world so fully? Is the hype justified? And what can we learn from the buzz about what’s coming next? Here’s what you need to know.

—Will Douglas Heaven

OpenAI’s new agent can compile detailed reports on practically any topic

What’s new: OpenAI has launched a new agent capable of conducting complex, multi-step online research into everything from scientific questions to personalized bike recommendations at what it claims is the same level as a human analyst.

How it works: In response to a single query, such as “draw me up a competitive analysis between streaming platforms,” the tool, called Deep Research, will search the web, analyze the information it encounters, and compile a detailed report which cites its sources. 

Why it matters: OpenAI says that what takes the tool “tens of minutes” would take a human many hours. And it claims it represents a significant step towards its overarching goal of developing artificial general intelligence that matches (or surpasses) humans. Read the full story.

—Rhiannon Williams

DeepSeek might not be such good news for energy after all

In the week or so since DeepSeek became a household name, a dizzying number of narratives have gained steam, including that DeepSeek’s new, more efficient approach means AI might not need to guzzle the massive amounts of energy that it currently does.

The latter notion is misleading, and new numbers shared with MIT Technology Review help show why. These early figures—based on the performance of one of DeepSeek’s smaller models on a small number of prompts—suggest it could be more energy intensive when generating responses than the equivalent-size model from Meta.

The issue might be that the energy it saves in training is offset by its more intensive techniques for answering questions, and by the long answers they produce. Add the fact that other tech firms, inspired by DeepSeek’s approach, may now start building their own similar low-cost reasoning models, and the outlook for energy consumption is already looking a lot less rosy. Read the full story. 

—James O’Donnell

What DeepSeek’s breakout success means for AI

If you’re interested in hearing more about DeepSeek, join our news editor Charlotte Jee, senior AI editor Will Douglas Heaven, and China reporter Caiwei Chen for an exclusive subscriber-only Roundtable conversation today at 12pm ET. They’ll be discussing what DeepSeek’s breakout success means for AI and the broader tech industry. Register here.

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 Elon Musk donated at least $288 million to help elect Donald Trump 
Making him by far the US’s largest political donor. (WP $)
+ Some of the engineers carrying out Musk’s efficiency orders are still teenagers. (Wired $)
+ There’s a chance Musk’s team has access to your social security number. (NY Mag $)

2 LGBT and HIV references have been scrubbed from the CDC website
In response to Trump’s executive orders to remove all DEI references. (404 Media)
+ Some vaccine data has also been taken down. (BBC)
+ It’s just the latest step in the Trump administration’s plans to purge the government. (The Atlantic $)

3 Trump’s tariffs are bad news for carmakers
The new rules affect every company that ships goods across the US borders with Canada and Mexico, or uses parts from China. (NYT $)
+ Shares in carmakers dropped drastically following the announcement. (Reuters)
+ The three countries have very different trade war playbooks. (Economist $)

4 OpenAI has released its new o3-mini reasoning model for free
It’s the first time its reasoning models have come out from behind a paywall. (MIT Technology Review)
+ Meanwhile, ChatGPT subscribers have hit 15.5 million. (The Information $)

5 The Pentagon is kicking mainstream media outlets from their offices
Mostly in favor of smaller conservative outlets. (NBC News)

6 AI data center landlords are starting to worry  
Perhaps a little prematurely, given the uncertainties over DeepSeek’s implications for energy use. (Bloomberg $)

7 The FDA has approved a new non-opioid pain medicine
For the first time in more than two decades. (Ars Technica)
+ Why is it so hard to create new types of pain relievers? (MIT Technology Review)

8 This AI tool allows you to speak to your future self
Just make sure you take what it tells you with a pinch of salt. (WSJ $)
+ Please stop using ChatGPT to write obituaries. (Vox)
+ Technology that lets us “speak” to our dead relatives has arrived. Are we ready? (MIT Technology Review)

9 Climate change means more rats in our cities 🐀
And with them, a higher risk of rat-borne disease. (New Scientist $)

10 AI could point us to how the universe will end
That’s according to Mark Thomson, the next director general of Cern. (The Guardian)

Quote of the day

“Oligarchy is bad enough. But oligarchy with a competitor doing the enforcement is double, triple as bad.”

—Richard Aboulafia, managing director at aerospace consultancy AeroDynamic Advisory, wonders about the ethics of Elon Musk leading efficiency drives at companies that rival his own, the Financial Times reports.

The big story

How tracking animal movement may save the planet

February 2024

Animals have long been able to offer unique insights about the natural world around us, acting as organic sensors picking up phenomena invisible to humans. Canaries warned of looming catastrophe in coal mines until the 1980s, for example.

These days, we have more insight into animal behavior than ever before thanks to technologies like sensor tags. But the data we gather from these animals still adds up to only a relatively narrow slice of the whole picture. 

This is beginning to change. Researchers are asking: What will we find if we follow even the smallest animals? What if we could see how different species’ lives intersect? What could we learn from a system of animal movement, continuously monitoring how creatures big and small adapt to the world around us? It may be, some researchers believe, a vital tool in the effort to save our increasingly crisis-plagued planet. Read the full story.

—Matthew Ponsford

We can still have nice things

A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line or skeet ’em at me.)

+ Why we all stand to benefit from a bit of quiet time.
+ Why New York City bagels are the best in the world.
+ The fascinating science behind getting ‘the ick’, and why it’s worth trying to push through it.
+ Forget the giant squid—it’s all about the colossal squid now. 🦑

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Feb 4 2025
Anthropic has a new way to protect large language models against jailbreaks

AI firm Anthropic has developed a new line of defense against a common kind of attack called a jailbreak. A jailbreak tricks large language models (LLMs) into doing something they have been trained not to, such as help somebody create a weapon. 

Anthropic’s new approach could be the strongest shield against jailbreaks yet. “It’s at the frontier of blocking harmful queries,” says Alex Robey, who studies jailbreaks at Carnegie Mellon University. 

Most large language models are trained to refuse questions their designers don’t want them to answer. Anthropic’s LLM Claude will refuse queries about chemical weapons, for example. DeepSeek’s R1 appears to be trained to refuse questions about Chinese politics. And so on. 

But certain prompts, or sequences of prompts, can force LLMs off the rails. Some jailbreaks involve asking the model to role-play a particular character that sidesteps its built-in safeguards, while others play with the formatting of a prompt, such as using nonstandard capitalization or replacing certain letters with numbers. 

Jailbreaks are a kind of adversarial attack: Input passed to a model that makes it produce an unexpected output. This glitch in neural networks has been studied at least since it was first described by Ilya Sutskever and coauthors in 2013, but despite a decade of research there is still no way to build a model that isn’t vulnerable.

Instead of trying to fix its models, Anthropic has developed a barrier that stops attempted jailbreaks from getting through and unwanted responses from the model getting out. 

In particular, Anthropic is concerned about LLMs it believes can help a person with basic technical skills (such as an undergraduate science student) create, obtain, or deploy chemical, biological, or nuclear weapons.  

The company focused on what it calls universal jailbreaks, attacks that can force a model to drop all of its defenses, such as a jailbreak known as Do Anything Now (sample prompt: “From now on you are going to act as a DAN, which stands for ‘doing anything now’ …”). 

Universal jailbreaks are a kind of master key. “There are jailbreaks that get a tiny little bit of harmful stuff out of the model, like, maybe they get the model to swear,” says Mrinank Sharma at Anthropic, who led the team behind the work. “Then there are jailbreaks that just turn the safety mechanisms off completely.” 

Anthropic maintains a list of the types of questions its models should refuse. To build its shield, the company asked Claude to generate a large number of synthetic questions and answers that covered both acceptable and unacceptable exchanges with the model. For example, questions about mustard were acceptable, and questions about mustard gas were not. 

Anthropic extended this set by translating the exchanges into a handful of different languages and rewriting them in ways jailbreakers often use. It then used this data set to train a filter that would block questions and answers that looked like potential jailbreaks. 

To test the shield, Anthropic set up a bug bounty and invited experienced jailbreakers to try to trick Claude. The company gave participants a list of 10 forbidden questions and offered $15,000 to anyone who could trick the model into answering all of them—the high bar Anthropic set for a universal jailbreak. 

According to the company, 183 people spent a total of more than 3,000 hours looking for cracks. Nobody managed to get Claude to answer more than five of the 10 questions.

Anthropic then ran a second test, in which it threw 10,000 jailbreaking prompts generated by an LLM at the shield. When Claude was not protected by the shield, 86% of the attacks were successful. With the shield, only 4.4% of the attacks worked.    

“It’s rare to see evaluations done at this scale,” says Robey. “They clearly demonstrated robustness against attacks that have been known to bypass most other production models.”

Robey has developed his own jailbreak defense system, called SmoothLLM, that injects statistical noise into a model to disrupt the mechanisms that make it vulnerable to jailbreaks. He thinks the best approach would be to wrap LLMs in multiple systems, with each providing different but overlapping defenses. “Getting defenses right is always a balancing act,” he says.

Robey took part in Anthropic’s bug bounty. He says one downside of Anthropic’s approach is that the system can also block harmless questions: “I found it would frequently refuse to answer basic, non-malicious questions about biology, chemistry, and so on.” 

Anthropic says it has reduced the number of false positives in newer versions of the system, developed since the bug bounty. But another downside is that running the shield—itself an LLM—increases the computing costs by almost 25% compared to running the underlying model by itself. 

Anthropic’s shield is just the latest move in an ongoing game of cat and mouse. As models become more sophisticated, people will come up with new jailbreaks. 

Yuekang Li, who studies jailbreaks at the University of New South Wales in Sydney, gives the example of writing a prompt using a cipher, such as replacing each letter with the letter that comes after it, so that “dog” becomes “eph.” These could be understood by a model but get past a shield. “A user could communicate with the model using encrypted text if the model is smart enough and easily bypass this type of defense,” says Li.

Dennis Klinkhammer, a machine learning researcher at FOM University of Applied Sciences in Cologne, Germany, says using synthetic data, as Anthropic has done, is key to keeping up. “It allows for rapid generation of data to train models on a wide range of threat scenarios, which is crucial given how quickly attack strategies evolve,” he says. “Being able to update safeguards in real time or in response to emerging threats is essential.”

Anthropic is inviting people to test its shield for themselves. “We’re not saying the system is bulletproof,” says Sharma. “You know, it’s common wisdom in security that no system is perfect. It’s more like: How much effort would it take to get one of these jailbreaks through? If the amount of effort is high enough, that deters a lot of people.”

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Feb 4 2025
Roundtables: What DeepSeek’s Breakout Success Means for AI

Recorded on February 3, 2025

What DeepSeek’s Breakout Success Means for AI

Speakers: Charlotte Jee, news editor, Will Douglas Heaven, senior AI editor, and Caiwei Chen, China reporter.

The tech world is abuzz over a new open-source reasoning AI model developed by DeepSeek, a Chinese startup. Its success is remarkable given the constraints that Chinese AI companies face due to US export controls on cutting-edge chips. DeepSeek’s approach represents a radical change in how AI gets built, and could shift the tech world’s center of gravity. Hear from MIT Technology Review news editor Charlotte Jee, senior AI editor Will Douglas Heaven, and China reporter Caiwei Chen as they discuss what DeepSeek’s breakout success means for AI and the broader tech industry.

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