Welcome to robot city

Tourists to Odense, Denmark, come for the city’s rich history and culture: It’s where King Canute, Denmark’s last Viking king, was murdered during the 11th century, and the renowned fairy tale writer Hans Christian Andersen was born there some 700 years later. But today, Odense (with a population just over 210,000) is also home to more than 150 robotics, automation, and drone companies. It’s particularly renowned for collaborative robots, or cobots—those designed to work alongside humans, often in an industrial setting. Robotics is a “darling industry” for the city, says Mayor Peter Rahbæk Juel, and one its citizens are proud of.

Odense’s robotics success has its roots in the more traditional industry of shipbuilding. In the 1980s, the Lindø shipyard, owned by the Mærsk Group, faced increasing competition from Asia and approached the nearby University of Southern Denmark for help developing welding robots to improve the efficiency of the shipbuilding process. Niels Jul Jacobsen, then a student, recalls jumping at the chance to join the project; he’d wanted to work with robots ever since seeing Star Wars as a teenager. But “in Denmark [it] didn’t seem like a possibility,” he says. “There was no sort of activity going on.”

That began to change with the partnership between the shipyard and the university. In the ’90s, that relationship got a big boost when the foundation behind the Mærsk shipping company funded the creation of the Mærsk Mc-Kinney Møller Institute (MMMI), a center dedicated to studying autonomous systems. The Lindø shipyard eventually wound down its robotics program, but research continued at the MMMI. Students flocked to the institute to study robotics. And it was there that three researchers had the idea for a more lightweight, flexible, and easy-to-use industrial robot arm. That idea would become a startup called Universal Robots, Odense’s first big robotics success story. In 2015, the US semiconductor testing giant Teradyne acquired Universal Robots for $285 million. That was a significant turning point for robotics in the city. It was proof, says cofounder Kristian Kassow, that an Odense robotics company could make it without being tied to a specific project, like the previous shipyard work. It was a signal of legitimacy that attracted more recognition, talent, and investment to the local robotics scene.

Kim Povlsen, president and CEO of Universal Robots, says it was critical that Teradyne kept the company’s main base in Odense and maintained the Danish work culture, which he describes as nonhierarchical and highly collaborative. This extends beyond company walls, with workers generally happy to share their expertise with others in the local industry. “It’s like this symbiotic thing, and it works really well,” he says. Universal Robots positions itself as a platform company rather than just a manufacturer, inviting others to work with its tech to create robotic solutions for different sectors; the company’s robot arms can be found in car-part factories, on construction sites, in pharmaceutical laboratories, and on wine-bottling lines. It’s a growth play for the company, but it also offers opportunities to startups in the vicinity.

In 2018 Teradyne bought a second Odense robotics startup, Mobile Industrial Robots, which was founded by Jacobsen, the Star Wars fan who worked on the ship-welding robots in his university days. The company makes robots for internal transportation—for example, to carry pallets or tow carts in a warehouse. The sale has allowed Jacobsen to invest in other robotics projects, including Capra, a maker of outdoor mobile robots, where he is now CEO.

The success of these two large robotics companies, which together employ around 800 people in Odense, created a ripple effect, bringing both funding and business acumen into the robotics cluster, says Søren Elmer Kristensen, CEO of the government-funded organization Odense Robotics.

There are challenges to being based in a city that, though the third-largest in Denmark, is undeniably small on the global scale. Attracting funding is one issue. Most investment still comes from within the country’s borders. Sourcing talent is another; demand outstrips supply for highly qualified tech workers. Kasper Hallenborg, director of the MMMI, says the institute feels an obligation to produce enough graduates to support the local industry’s needs. Even now, too few women and girls enter STEM fields, he adds; the MMMI supports programs aimed at primary schoolers to try to strengthen the pipeline. As the Odense robotics cluster expands, however, it has become easier to attract international talent. It’s less of a risk for people to move, because plenty of companies are hiring if one job doesn’t work out. 

And Odense’s small size can have advantages. Juel, the mayor, points to drone-testing facilities established at the nearby Hans Christian Andersen Airport, which, thanks to relatively low air traffic, is able to offer plenty of flying time. The airport is one of the few that allow drones to fly beyond the visual line of sight.

The shipyard, once the city’s main employer, closed down completely shortly after the 2007–2008 financial crisis but has recently become an industrial park aimed at manufacturing particularly large structures like massive steel monopiles. The university is currently building a center to develop automation and robotics for use in such work. Visit today and you may see not ships but gigantic offshore wind turbines—assembled, of course, with the help of robots.

Victoria Turk is a technology journalist based in London.

Job titles of the future: Pharmaceutical-grade mushroom grower

Studies have indicated that psychedelic drugs, such as psilocybin and MDMA, have swift-acting and enduring antidepressant effects. Though the US Food and Drug Administration denied the first application for medical treatments involving psychedelics (an MDMA-based therapy) last August, these drugs appear to be on the road to mainstream medicine. Research into psilocybin led by the biotech company Compass Pathways has been slowed in part by the complexity of the trials, but the data already shows promise for the psychedelic compound within so-called magic mushrooms. Eventually, the FDA will decide whether to approve it to treat depression. If and when it does—a move that would open up a vast legal medical market—who will grow the mushrooms?

Scott Marshall already is. The head of mycology at the drug manufacturer Optimi Health in British Columbia, Canada, he is one of a very small number of licensed psilocybin mushroom cultivators in North America. Growers and manufacturers would need to do plenty of groundwork to be able to produce pharmaceutical psilocybin on an industrial, FDA-approved scale. That’s why Optimi is keen to get a head start.

A nascent industry

Marshall is at the cutting edge of the nascent psychedelics industry. Psilocybin mushroom production was not legally permitted in Canada until 2022, when the country established its limited compassionate-­access program. “Our work is pioneering large-scale, legal cultivation of psilocybin mushrooms, ensuring the highest standards of safety, quality, and consistency,” he says. 

Backed by more than $22 million in investment, Optimi received a drug establishment license in 2024 from Canadian regulators to export pharmaceutical-­grade psilocybin to psychiatrists abroad in the limited number of places that have legal avenues for its use. Oregon has legalized supervised mushroom journeys, Australia has approved psilocybin therapy for PTSD and depression, and an increasing number of governments—national, state, and local—are considering removing legal barriers to psychedelic mushrooms on a medical basis as the amount of research supporting their use grows. There are also suggestions that the Trump administration may be more likely to support federal reform in the US.

But the legal market, medical or otherwise, remains tiny. So for now, almost all of Marshall’s mushrooms—he has grown more than 500 pounds since joining Optimi in 2022—stay in the company’s vault. “By setting the bar for production and [compliance with] regulation,” he says, “we’re helping to expand scientific understanding and accessibility of psychedelics for therapeutic use.”

Learning the craft

Before Marshall, 40, began cultivating mushrooms, he was working in property management. But that changed in 2014, when a friend who was an experienced grower gave him a copy of the book Mushroom Cultivator: A Practical Guide to Growing Mushrooms at Home (1983). That friend also gave him a spore print, effectively the “seeds” of a mushroom, from which Marshall grew three Psilocybin cubensis mushrooms from the golden teacher variety, his first foray into the field. “I kept growing and growing and growing—for my own health and well-being—and then got to a point where I wanted to help other people,” he says.

In 2018, he established his own company, Ra Mushrooms, selling cultivation kits for several varieties, including illegal psilocybin, and he was regularly posting photos on Instagram of mushrooms he had grown. In 2022, he was hired by Optimi, marking his journey from underground grower to legal market cultivator—“an unbelievable dream of mine.” 

Mattha Busby is a journalist specializing in drug policy and psychedelic culture.

The Download: Introducing the Relationships issue

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.

Introducing: the Relationships issue

Relationships are the stories of people and systems working together. Sometimes by choice. Sometimes for practicality. Sometimes by force. Too often, for purely transactional reasons.

That’s why we’re exploring relationships in this issue. Relationships connect us to one another, but also to the machines, platforms, technologies, and systems that mediate modern life.

They’re behind the partnerships that make breakthroughs possible, the networks that help ideas spread, and the bonds that build trust—or at least access. In this issue, you’ll find stories about the relationships we forge with each other, with our past, with our children, and with technology itself.

Here’s just a taste of what you can expect:

+ People are forming relationships with AI chatbots. Some of these are purely professional, others more complicated. This kind of relationship may be novel now, but it’s something we will all take for granted in just a few years. 

+ Adventures in the genetic time machine. Ancient DNA is telling us more and more about humans and environments long past. Could it also help rescue the future?

+ Frozen embryos are filling storage banks around the world. It’s a struggle to know what to do with them. Read the full story.

+ Our relationships with our employers are often mediated through monitoring systems. And while it’s increasing the power imbalance between companies and workers, protections are lagging far behind. Read the full story.

MIT Technology Review Narrated: The messy quest to replace drugs with electricity

“Electroceuticals” promised the post-pharma future for medicine. But their exclusive focus on the nervous system is seeming less and less warranted.

This is our latest story to be turned into a MIT Technology Review Narrated podcast, which 
we’re publishing each week on Spotify and Apple Podcasts. Just navigate to MIT Technology Review Narrated on either platform, and follow us to get all our new content as it’s released.

The must-reads

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

1 DOGE is working on software to automate firing workers
It builds on an existing program previously used by the US Department of Defense. (Wired $)
+ DOGE workers are already resigning from the department. (Fast Company $)
+ Can AI help DOGE slash government budgets? It’s complex. (MIT Technology Review)

2 American workers are generally pessimistic about AI
Whereas Silicon Valley can’t get enough of it.(WP $)
+ How to fine-tune AI for prosperity. (MIT Technology Review)

 3 iPhones are autocorrecting the term ‘racist’ to ‘Trump’
The company is blaming what it calls a ‘phonetic overlap.’ (NYT $)
+ It’s promised to fix the bug as soon as possible. (FT $)

4 Amy Gleason is the head of DOGE, apparently
The former Digital Service senior advisor is the acting administrator. (NY Mag $)
+ But Elon Musk is still ultimately in charge. (NBC News)

5 Grok’s new unhinged mode can simulate phone sex
If that’s what you’re into. (Ars Technica)

6 More data centers don’t necessarily mean more jobs
The massive facilities don’t actually need many humans to run them. (WSJ $)
+ Not that that’s putting Meta off building a gigantic data center campus. (The Information $)

7 China is keen for tech companies to monetize their data
But not everyone is buying in. (Rest of World)

8 The slow death of the combustion engine
Pistons are out, and electrons are in. (IEEE Spectrum)
+ Why EVs are (mostly) set for solid growth in 2025. (MIT Technology Review)

9 The US is in love with cheap clothing
And established brands are the ones paying the price. (Insider $)

10 What frozen mummies can tell us about the ancient world
From wolf pups to mammoths. (New Scientist $)

Quote of the day

“I felt nothing but utter disgust. I no longer enjoyed sitting in my Tesla.”

—Mike Schwede, an entrepreneur living in Switzerland, tells the Guardian he’s turned his back on the electric car company after Elon Musk’s Nazi-linked salutes during Trump’s inauguration.

The big story

Think that your plastic is being recycled? Think again.

October 2023

The problem of plastic waste hides in plain sight, a ubiquitous part of our lives we rarely question. But a closer examination of the situation is shocking. To date, humans have created around 11 billion metric tons of plastic. 72% of the plastic we make ends up in landfills or the environment. Only 9% of the plastic ever produced has been recycled.

To make matters worse, plastic production is growing dramatically; in fact, half of all plastics in existence have been produced in just the last two decades. Production is projected to continue growing, at about 5% annually.

So what do we do? Sadly, solutions such as recycling and reuse aren’t equal to the scale of the task. The only answer is drastic cuts in production in the first place. Read the full story

—Douglas Main

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.)

+ Look up to the sky over the next few nights: seven planets will be aligned, and won’t do so again until 2040.
+ Jeremy Strong probably won’t win an Oscar next week, but he definitely deserves to.
+ Why English is such a strange language.
+ 1985 produced some truly anthemic songs—and some absolute bilge.

The Rebirth of ‘Marketing Mix Modeling’

Lingering privacy challenges and ever-improving cloud and artificial intelligence technology are driving a marketing model renaissance.

Marketing mix modeling (MMM), launched in 1949, fell out of favor in the early 2000s when digital advertising took off. The data-driven technique had long helped marketers understand how variables such as advertising, promotion, and prices impact revenue.

Yet compared to tracking cookies and last-touch attribution models, MMM seemed complex and expensive.

Renaissance

In 2025, however, MMM has enjoyed renewed attention.

Meta and Google have released free, open-source MMM tools in the past couple of years — Google’s Meridian on January 29, 2025, and Meta’s Robyn in 2023.

Why does MMM interest two of the largest digital advertising platforms? I see three probable factors: tracking cookies, AI, and cloud computing.

Meridian: Empower your team with best-in-class marketing mix models and drive better business outcomesMeridian is an open-source MMM built by Google that provides innovative solutions to key measurement challenges.

Google launched Meridian, an open-source marketing-mix model, last month.

Cookie-less Advertising

Controversies surrounding tracking cookies are the first driver. Cookies are a foundational and useful technology. A first-party cookie on a browser keeps users logged into a website and retains their preferences.

However, third-party tracking cookies that catalog an individual’s behavior across web properties are a privacy pariah. Laws such as Europe’s General Data Protection Regulation and the California Consumer Privacy Act limit such cookies, and many browser companies have stopped supporting them entirely.

The potential of cookie-less ad targeting makes MMM attractive to large-scale advertisers and platforms.

Advertising performance. Third-party cookies, despite privacy concerns, drive ad targeting and thus performance. MMM should help advertisers identify which marketing channels and creatives produce the best returns. Coupled with new ad targeting techniques, MMM will almost certainly improve performance.

Meta’s Robyn, for example, helps advertisers analyze the performance of campaigns across Facebook, Instagram, and other channels. It gauges channel effectiveness and optimizes ad spend based on results.

The era of cookie-less targeting encourages the use of MMM. Some of the most forward-looking, high-budget advertisers are considering alternative targeting methods and new promotional channels. Monitoring those experiments requires complicated multi-touch attribution or MMM.

For example, Google’s Meridian MMM moved beyond standard regression models to a theory called “Bayesian causal inference,” which captures the impact of imprecise marketing actions, such as a social media post.

Personal privacy is yet another reason why MMM appeals to Google, Meta, and many advertisers. The model aggregates data and generally avoids personally identifiable information.

Artificial Intelligence

AI makes MMM relatively faster, more adaptive, and easier to scale.

Improved speeds come first from training the model quickly. The foundational, now-available models are a massive headstart compared to starting from scratch.

Second, AI helps process and clean large, complex datasets from multiple sources such as digital ads, TV, print, and online and in-store sales. The associated algorithms detect seasonality, outliers, and data anomalies, reducing manual work but still requiring data scientists to fine-tune the models.

Regardless, the AI behind Meta’s Robyn dynamically adjusts model variables, improving accuracy automatically. It is thus more adaptable and scalable.

Cloud Computing

Twenty years ago, the rise of web-based marketing produced massive datasets and hefty processing loads. Analyzing that info typically required custom-built infrastructure and expensive data warehouses

These limitations no longer exist thanks to cloud computing advances and affordability. Instead of spending $500,000 or more on MMM software and servers, a company can run Google Meridian in the cloud for a fraction of the amount, perhaps as little as $10,000 a year.

Accurate modeling, however, requires some scale — businesses investing at least $500,000 per year in advertising likely benefit the most. But that could change if MMM becomes available as a service.

Data Suggests Google Indexing Rates Are Improving via @sejournal, @martinibuster

New research of over 16 million webpages shows that Google indexing rates have improved but that many pages in the dataset were not indexed and over 20% of the pages were eventually deindexed. The findings may be representative of trends and challenges that are specific to sites that are concerned about SEO and indexing.

Research By IndexCheckr Tool

IndexCheckr is a Google indexing tracking tool that enables subscribers to be alerted to when content is indexed, monitor currently indexed pages and to monitor the indexing status of external pages that are hosting backlinks to subscriber web pages.

The research may not statistically correlate to Internet-wide Google indexing trends but it may have a close-enough correlation to sites whose owners are concerned with indexing and backlink monitoring, enough to subscribe to a tool to monitor those trends.

About Indexing

In web indexing, search engines crawl the internet, filter content (such as removing duplicates or low-quality pages), and store the remaining pages in a structured database called a Search Index. This search index is stored on a distributed file system. Google originally used the Google File System (GFS) but later upgraded to Colossus, which is optimized for handling massive amounts of search data across thousands of servers.

Indexing Success Rates

The research shows that most pages in their dataset were not indexed but that indexing rates have improved from 2022 to 2025. Most pages that Google indexed are indexed within six months.

  • Most pages in the dataset were not indexed (61.94%).
  • Indexing rates have improved from 2022 to 2025.
  • Google indexes most pages that do get indexed within six months (93.2%).

Deindexing Trends

The indexing trends are very interesting, especially about how fast Google is at deindexing pages. Of all the indexed pages in the entire dataset, 13.7% of them are deindexed within three months after indexing. The overall rate of deindexing is 21.29%. A sunnier way of interpreting that data is that 78.71% remained firmly indexed by Google.

Deindexing is generally related to Google quality factors but it could also reflect website publishers and SEOs who purposely request web page deindexing through noindex directives like the Meta Robots element.

Here is the time-based cumulative percentages of deindexing:

  • 1.97% of the indexed pages are deindexed within 7 days.
  • 7.97% are deindexed within 30 days.
  • 13.70% deindexed within 90 days
  • 21.29% deindexed after 90 days.

The research paper that I was provided offers this observation:

“This timeline highlights the importance of early monitoring and optimization to address potential issues that could lead to deindexing. Beyond three months, the risk of deindexing diminishes but persists, making periodic audits essential for long-term content visibility.”

Impact Of Indexing Services

The next part of the research highlights the effectiveness of tools designed to increase the web page indexing. They found that URLs submitted to indexing tools had a low 29.37% success rate. That means that 70.63% of submitted web pages remained unindexed, possibly highlighting limitations in manual submission strategies.

High Percentage Of Pages Not Indexed

Less than 1% of the tracked websites were entirely unindexed. The majority of unindexed URLs were from websites that were indexed by Google. 37.08% of all the tracked pages were fully indexed.

These numbers may not reflect the state of the Internet because the data is pulled from a set of sites that are subscribers to an indexing tool. That slants the data being measured and makes it different from what the state of the entire Internet may be.

Google Indexing Has Improved Since 2022

Although there are some grim statistics in the data a bright spot is that there’s been a steady increase in indexing rates from 2022 to 2025, suggesting that Google’s ability to process and include pages may have improved.

According to IndexCheckr:

“The data from 2022 to 2025 shows a steady increase in Google’s indexing rate, suggesting that the search engine may be catching up after previously reported indexing struggles.”

Summary Of Findings

Complete deindexing at a website-level are rare for this dataset. Google’s indexing speed varies and more than half of the web pages in this dataset struggles to get indexed, possibly related to site quality.

What kinds of site quality issues would impact indexing? In my opinion, some of what is causing this could include commercial product pages with content that’s bulked up for the purposes of feeding the bot. I’ve reviewed a few ecommerce sites doing that who either struggled to get indexed or to rank. Google’s organic search results (SERPs) for ecommerce are increasingly precise. Those kinds of SERPs don’t make sense when reviewed through the lens of SEO and that’s because strategies based on feeding the bot entities, keywords and topical maps tend to result in search engine first websites and that’s not going to affect the ranking factors that really count that are related to how users may react to content.

Read the indexing study at IndexCheckr.com:

Google Indexing Study: Insights from 16 Million Pages

Featured Image by Shutterstock/Shutterstock AI Generator

AI Search Engines Often Cite Third-Party Content, Study Finds via @sejournal, @MattGSouthern

A recent analysis by xfunnel.ai examines citation patterns across major AI search engines.

The findings provide new insight into how these tools reference web content in their responses.

Here are the must-know highlights from the report.

Citation Frequency Differs By Platform

Researchers submitted questions across different buyer journey stages and tracked how the AI platforms responded.

The study analyzed 40,000 responses containing 250,000 citations and found differences in citation frequency:

  • Perplexity: 6.61 citations per response
  • Google Gemini: 6.1 citations per response
  • ChatGPT: 2.62 citations per response

ChatGPT was tested in its standard mode, not with explicitly activated search features, which may explain its lower citation count.

Third-Party Content Leads Citation Types

The research categorized citations into four groups:

  • Owned (company domains)
  • Competitor domains
  • Earned (third-party/affiliate sites)
  • UGC (user-generated content)

Across all platforms, earned content represents the largest percentage of citations, with UGC showing increasing representation.

Affiliate sites and independent blogs hold weight in AI-generated responses as well.

Citations Change Throughout Customer Journey

The data shows differences in citation patterns based on query types:

  • During the problem exploration and education stages, there is a higher percentage of citations from third-party editorial content.
  • UGC citations from review sites and forums increase in the comparison stages.
  • In the final research and evaluation phase, citations tend to come directly from brand websites and competitors.

Source Quality Distribution

When examining the quality distribution of cited sources, the data showed:

  • High-quality sources: ~31.5% of citations
  • Upper-mid quality sources: ~15.3% of citations
  • Mid-quality sources: ~26.3% of citations
  • Lower-mid quality sources: ~22.1% of citations
  • Low-quality sources: ~4.8% of citations

This indicates AI search engines prefer higher-quality sources but regularly cite content from middle-tier sources.

Platform-Specific UGC Preferences

Each AI search engine shows preferences for different UGC sources:

  • Perplexity: Favors YouTube and PeerSpot
  • Google Gemini: Frequently cites Medium, Reddit, and YouTube
  • ChatGPT: Often references LinkedIn, G2, and Gartner Peer Reviews

The Third-Party Citation Opportunity

The data exposes a key area that many SEO professionals might be overlooking.

While the industry often focuses on technical changes to owned content for AI search optimization, this research suggests a different approach may be more effective.

Since earned media (content from third parties) is the biggest citation source on AI search platforms, it’s important to focus on:

  • Building relationships with industry publications
  • Creating content that others want to cover
  • Contributing guest articles to trusted websites
  • Developing strategies for the user-generated content (UGC) platforms that each AI engine prefers

This is a return to basics: create valuable content that others will want to reference instead of just modifying existing content for AI.

Why This Matters

As AI search is more widely used, understanding these citation patterns can help you stay visible.

The findings show the need to use different content strategies across various platforms.

However, maintaining quality and authority is essential. So don’t neglect SEO fundamentals in pursuit of broader content distribution.

Top Takeaway

Invest in a mix of owned content, third-party coverage, and presence on relevant UGC platforms to increase the likelihood of your content being cited by AI search engines.

The data suggests that earning mentions on trusted third-party sites may be even more valuable than optimizing your domain content.


Featured Image: Tada Images/Shutterstock

TikTok Beats Competitors by 2X with $6B In-App Revenue via @sejournal, @MattGSouthern

TikTok, including Douyin in China, earned $6 billion in in-app purchases last year, more than double any competitor, according to Sensor Tower’s Q4 Digital Market Index.

This marked a 36% increase from the previous year, showcasing TikTok’s growing financial dominance in mobile commerce.

Mobile App Market Growth

Global in-app purchase revenue reached $39.4 billion in Q4 2024, up 13.5% year over year. Total 2024 revenue reached $150 billion (+12.5% from 2023).

Non-gaming apps grew faster, with revenue up 28.2% to $19.2 billion. The revenue gap between apps and games narrowed to just $1 billion, down from $5 billion a year earlier.

TikTok’s Commerce Transformation

Recent Ipsos research commissioned by TikTok reveals how the platform reshapes commerce behavior.

The study of nearly 4,000 US consumers found that 73% of TikTok shoppers value the platform’s personalized recommendations, with three-quarters agreeing it’s their go-to place for discovering new brands and products.

Discovery Engine Drives Purchases

TikTok’s approach to discovery helps drive in-app sales.

Two main components of product discovery on TikTok include:

  1. Personalized For You feed: 68% of TikTok shoppers say the personalized content allows for greater product discovery
  2. Intent-based search: Nearly 1 in 4 users search for something within 30 seconds of opening the app

This discovery system translates to sales. 70% of TikTok shoppers report purchasing after seeing an ad or shoppable content on the platform.

The Authenticity Factor

TikTok’s commerce success is built on trust.

Ipsos found that 74% of users believe TikTok’s creator content feels authentic, which is higher than that of other platforms.

Aaron Jones, Liquid I.V. VP of E-commerce & Media, explained how this authenticity drove results:

“An affiliate creator created an honest review that took off, resulting in a sales lift across omnichannel and a full sell out of the flavor with over 59K total orders on TikTok Shop. Of the purchasers, 88% were new customers.”

Actionable Strategies for Marketers

The Ipsos research identifies three key strategies for brands:

  1. Capture immediate purchases with in-app commerce: Use TikTok Shop for shoppable videos, LIVE Shopping, and affiliate partnerships
  2. Maximize e-commerce with always-on tactics: Create full-funnel experiences between TikTok engagement and external purchases
  3. Drive commerce everywhere with hybrid strategies: Facilitate seamless journeys across physical and online environments

Platform Context

iOS accounts for 70% of in-app revenue ($27.5B), while Google Play leads in downloads with 73.6% market share despite hitting its lowest download count (25.1B) since Q1 2020.

TikTok ROI

A recent study by Dentsu showed that TikTok gives advertisers the best short-term ROI in Nordic markets.

The study found that TikTok produced an ROI of 11.8. This means that brands earned almost 12 times their initial investment in sales revenue within six weeks of advertising on the platform.

Brands that consistently used TikTok as an always-on channel instead of running occasional campaigns saw better sales results and higher returns.

Looking Ahead

In 2025, TikTok is becoming an essential platform for digital marketers due to its solid monetization strategies, noteworthy ROI metrics, and expanding role in commerce.

How To Navigate Performance Fluctuations In Google Shopping Campaigns via @sejournal, @brookeosmundson

Managing Google Shopping campaigns is both an art and a science.

Even with the most refined strategies and detailed data, performance fluctuations can happen – and when they do, they often leave marketers scrambling for answers.

Understanding why these fluctuations occur, knowing how to respond, and effectively communicating with clients are essential skills for anyone managing these campaigns.

This article will explore:

  • Factors behind expected and unexpected performance changes.
  • How to create actionable strategies for troubleshooting.
  • Advice on communicating effectively with clients when things don’t go as planned.

Expected Fluctuations In Google Shopping Campaigns

Expected fluctuations are those that follow predictable patterns, often driven by external factors like time of year or consumer behavior trends

While they can still be challenging to manage, they’re usually easier to anticipate and explain.

Seasonality Fluctuations

Seasonality is one of the most common drivers of performance fluctuations in Google Shopping campaigns.

Consumers behave differently depending on the time of year, and these patterns often align with major holidays or specific shopping periods.

For instance, campaigns tend to see increased traffic and conversions during Black Friday and Cyber Monday, as well as in the lead-up to Christmas. Conversely, industries like outdoor recreation may see a downturn in the winter months.

If your campaigns cater to niche markets, other seasonal trends might also come into play – such as back-to-school shopping in August or summer sales for outdoor equipment.

Leveraging historical data can help identify and pinpoint these trends.

Proper preparation is key to managing these seasonal shifts. This can include:

  • Increasing budgets and bids ahead of high-traffic periods.
  • Aligning your creative assets with seasonal themes.
  • Leveraging historical data to predict performance patterns.

By staying proactive, you can turn expected fluctuations into opportunities for growth.

Market Trends Fluctuations

Broader market trends also play a role in campaign performance.

For example, rising interest in eco-friendly products or the emergence of new tech gadgets can influence consumer buying behavior. These trends are often gradual, making them easier to spot and account for in your campaigns.

Monitoring industry reports and using tools like Google Trends can help you stay ahead of market shifts. Adjusting your product feeds to emphasize trending items or updating your bidding strategy can ensure your campaigns remain competitive.

Competitor Activity

Competitor behavior can lead to sudden Google Shopping performance changes.

For example, a new competitor entering the market may bid aggressively on your top-performing products, driving up cost-per-click (CPC).

Alternatively, an established competitor might launch a promotional campaign, temporarily capturing a larger share of clicks.

To address competitor-driven fluctuations, conduct a competitive analysis using tools like Auction Insights.

If you notice increased competition, consider differentiating your offerings by highlighting unique selling points or adjusting bids to focus on less competitive segments.

Unexpected Fluctuations And Their Challenges

While expected fluctuations can often be forecasted, unexpected changes in performance are trickier to diagnose.

These shifts might not have an obvious external cause, leaving PPC managers to dig into the depths of the Google Shopping campaigns to uncover underlying issues.

Below are some common unexpected fluctuations and what to investigate.

1. Seeing A Sharp Decline In Impressions

When impressions suddenly drop, it’s a red flag that your ads are no longer reaching as many people as possible. Several factors could be at play:

  • Budget Constraints: A limited daily budget can throttle impressions, especially if you’re running out of budget early in the day. Review your budget pacing to ensure you’re not capping performance.
  • Changes In Search Demand: While seasonality can explain some shifts, there are instances where search demand for specific products dips unexpectedly. Use the “Search Terms” report to spot if a few users are searching for your targeted keywords.
  • Bid Strategy Changes: If bid changes were recently made, they might have inadvertently lowered your competitive edge. Analyze auction insights to determine whether competitors have increased their bids, pushing your ads lower in the rankings.
  • Policy Violations: Account suspensions or disapprovals due to policy changes or errors in the product feed can lead to a sudden halt in ad delivery. Check the “Diagnostics” tab in the Merchant Center for any alerts.

2. A Sudden Decline In Conversions

A sudden drop in conversions is unsettling, especially when impressions and clicks remain steady. Here’s a quick look at where to investigate:

  • Landing Page Issues: A broken link, slow page load times, or changes to the landing page experience can derail conversions. Use tools like Google’s Page Speed Insights to test performance.
  • Inventory Problems: Out-of-stock or incorrect availability data in the product feed can negatively impact conversion rates. Make sure the Merchant Center feed is syncing properly.
  • Pricing Discrepancies: If competitors undercut your pricing, customers may click but not convert. Monitor competitor pricing to ensure your client remains competitive.
  • Shifts In Audience Behavior: Use the “Audience Insights” report to check if your targeting still aligns with customer intent.

It’s important to note that your product data feed is the backbone of your Google Shopping campaigns, and even minor errors can lead to unexpected drops in performance.

Regularly auditing your data feed is crucial to avoiding these issues. Ensuring your feed is accurate, up-to-date, and optimized can help prevent performance dips caused by feed-related problems.

3. Other Unexpected Shifts

Sometimes the fluctuations in Google Shopping campaigns are more subtle, but still indicative of deeper issues:

  • Click-Through Rate (CTR) Drops: A sudden decline in CTR might indicate that your ad creatives are losing relevance. Test new product images, titles, or promotional messaging. Additionally, review what products are being triggered by search terms to determine if a more granular product structure is needed to maintain relevance.
  • ROAS Changes: If your return on ad spend suddenly dips, assess whether you’re overbidding on low-value clicks or if your campaign bid strategies need adjustment.

4. Algorithm Updates

Now you’re probably thinking – don’t algorithm updates only affect SEO rankings?

Think again.

Google’s algorithm changes can be one of the most common culprits of unexpected fluctuations. These updates can impact how products are displayed, how ads are served, and even which search queries trigger your Shopping ads.

Unfortunately, Google doesn’t always announce these changes right away, which means marketers often find out the hard way – through dips in performance.

When faced with algorithm-related fluctuations, your best course of action is to monitor key metrics closely and investigate any significant changes.

Look for shifts in impression share, CTR, or CPC that might signal an update.

Do some search and discovery testing “in the wild” to trigger your products, and identify if the user experience has changed, and adapt your strategy based on the outcomes.

How To Communicate Performance Fluctuations To Clients

Handling performance fluctuations isn’t just about solving the problem; it’s also about maintaining client confidence.

Clients may not understand the nuances of Google Shopping campaigns, so it’s your job to explain the situation in a way that builds trust and sets realistic expectations.

Be Proactive

Don’t wait for clients to notice a performance dip before addressing it. As soon as you identify a fluctuation, reach out with an explanation of what’s happening, why it’s happening, and what steps you’re taking to resolve it.

For example, if a seasonal lull is causing lower conversion rates, provide historical data to show that this pattern is normal and temporary.

Use Data To Support Your Points

Data is your best friend when communicating with clients.

Use visualizations like graphs or charts to illustrate trends, compare performance to previous periods, and highlight your optimization efforts.

This helps clients see the bigger picture and understand that fluctuations are part of a broader strategy.

Offer A Plan Of Action & Manage Expectations

End every client conversation with clear next steps.

Rather than focusing solely on the issue, highlight the steps you’re taking to address the problem(s). For example:

  • Short-Term Solutions: “We’re adjusting the bid strategy and budgets to stabilize performance while we investigate further.”
  • Long-Term Strategies: “We’re monitoring search demand weekly to ensure we’re not missing out on new opportunities.”

This reassures them that their campaigns are in capable hands.

Set realistic timelines for recovery and provide regular updates.

Avoid overpromising quick fixes. Instead, frame your efforts as part of a comprehensive strategy.

Turning Fluctuations Into Opportunities

Performance fluctuations in Google Shopping campaigns are inevitable, but they don’t have to derail your strategy.

By understanding the difference between expected and unexpected fluctuations, preparing for seasonal changes, staying vigilant about potential issues, and communicating effectively with clients, you can navigate these challenges with confidence.

Remember, fluctuations are not failures – they’re opportunities to refine your approach and drive even better results for your campaigns.

More Resources:


Featured Image: CrizzyStudio/Shutterstock

Google Simplifies Removing Personal Info From Search Results via @sejournal, @MattGSouthern

Google is introducing new features that streamline removing personal information from search results.

These updates include:

  • A redesigned “Results about you” hub
  • A simplified removal request process
  • An option to refresh outdated search results.

Redesigned “Results About You” Page

Google has updated its Results About You tool.

Now, it proactively searches for personal information and alerts you if it finds any.

When you get this alert, you can ask Google to remove the information or contact the website directly.

The new interface is designed to make it easier for users to sign up for and manage alerts about their personal data.

Simplified Removal Process

Google is introducing a streamlined removal process that simplifies the steps needed to file a takedown request.

When you find a search result that contains your personal information, you can click on the three-dot menu next to that result to access an updated panel.

This panel clarifies the types of content that qualify for removal and guides you through the request process.

Image Credit: Google
Image Credit: Google
Image Credit: Google

Easier Refreshes For Outdated Results

Google is rolling out an update that addresses outdated search results.

Sometimes, a webpage’s content may no longer match what appears on Google if the webpage has been edited or removed.

Google now offers the ability to request a refresh of specific search results, prompting its systems to recrawl the webpage.

Previously, you had to wait for Google’s regular crawling schedule to notice any changes, which could take weeks.

Now, you can click the three dots next to an outdated search result and request a refresh. Google’s systems will then recrawl the page to retrieve the latest information.

Looking Ahead

Google’s latest update responds to the need for better privacy controls as more people worry about their personal information online. This change also shows that Google is adapting to regulatory pressure to protect personal data.

It’s important to note that these features only affect Google’s search results. They do not affect how your personal information appears on other search engines and websites.

For more details, see Google’s announcement.


Featured Image: mundissima/Shutterstock

Leveraging AI For Buyer-Centric Strategies To Effectively Engage B2B Buyers via @sejournal, @alexanderkesler

Investments in AI have reached unprecedented levels.

According to a World Economic Forum report, the global AI infrastructure market was valued at $35.42 billion in 2023, and projected to reach $223.45 billion by 2030.

Our own Q4 2024 market research indicates that 55.4% of B2B marketing teams are investing in AI to automate and analyze data to produce actionable buyer and buying group engagement and accelerate conversions.

However, this rapid adoption has revealed a key challenge: the lack of cohesive strategies for these unprecedented investments.

Microsoft’s 2024 Work Trend Index Annual Report echoes this, revealing that 60% of leaders are concerned over their organization’s AI strategy, while 59% are uncertain about AI’s impact on productivity.

For B2B marketers, the real opportunity lies in leveraging AI-optimized data analysis, combined with AI agents, to enable buyers more effectively in a market defined by complex buying journeys and large, defensive buying groups.

The strategic use of AI allows marketers to harness vast pools of data to develop buyer-centric strategies that address these complexities and empower decision-making.

In addition, AI can be leveraged to enhance and append market and marketing insights to demonstrate the tangible value of these efforts.

In this article, I will share key tactics for leveraging AI to enable buyers – and increase conversions by delivering a rich experience.

Why Is Buyer Enablement So Important?

We have seen a significant trend of buyers conducting their own independent research, with almost 70% of the buying journey spent collaborating with other members of the buying group, according to a recent report by 6sense.

To engage these buyers effectively, marketers must shift their focus toward enhancing brand awareness, creating brand preference, and delivering relevant content that supports these buyers in their research and decision-making.

In essence, marketers must create a more enriched buyer experience that aligns seamlessly with the preferences and behaviors of entire buying groups, accounting for the unique needs of each stakeholder.

AI is uniquely positioned to support these buyer-centric strategies by augmenting and optimizing marketing data.

This enables marketers to develop highly tailored and effective strategies to engage buyers at every stage of their journey.

4 Tactics For Leveraging AI To Power Buyer-Centric Strategies

1. Improve Personalization And Targeting With AI-Augmented Intelligence

Demand intelligence, derived from first-party data sources such as analytics, client relationship management (CRM) data, campaign metrics, and client feedback, is essential for delivering personalized outreach that drives qualified engagement.

AI can enhance this personalization by analyzing in bulk and enriching first-party data with firmographic, technographic, and location insights to build detailed buyer personas and detect prospect behavior and intent.

This not only improves targeting but also enables precise mapping of buyer journeys, offering the insights needed to craft highly personalized messaging that resonates deeply with each buying group member.

Additionally, AI can be leveraged to generate conversational content aligned with user behavior and preferences – obviously depending on organizational policies regarding generative content.

This ensures messaging is both relevant and engaging, further driving demand success.

2. ABX Enablement

Personalization at scale is a cornerstone of successful Account Based Experience (ABX) strategies, but achieving it can be both complex and resource-intensive.

AI offers a tactical solution by streamlining critical tasks such as segmentation and data analysis across large sets of accounts.

It can be leveraged to identify pain and friction points in the buyer’s journey, enabling marketers to craft and optimize omnichannel experiences tailored to target accounts.

AI also excels at account prioritization, leveraging dynamic scoring and intent data to pinpoint accounts and buyers with the highest likelihood of conversion.

This ensures that resources are directed toward the most promising opportunities, driving efficiency and maximizing the impact of ABX initiatives.

3. Sophisticated Automated Nurturing Sequences

One of the more exciting use cases of AI is the creation of automated omnichannel nurturing strategies that deliver targeted, cohesive experiences across channels such as email, social media, paid media, and content networks.

By leveraging data analysis, behavioral insights, and machine learning, AI can tailor messaging, timing, and delivery to individual prospect preferences.

How AI optimization can be utilized across marketing channels:

  • Email: Personalized content based on user engagement and behavior.
  • Social media: Social listening and sentiment analysis.
  • Paid media: Large-scale A/B testing and optimized messaging in real-time.
  • Content activation: The curation and distribution of content to niche platforms based on audience preference.

4. Performance Insights For Greater Optimization

AI has the potential to play a critical role in optimizing performance measurement by providing deeper insights and enabling smarter resource allocation in a timely and cost-effective manner.

Below are only a few ways AI can unlock demand performance:

  • Multi-touch attribution analysis: Identifying channels, content, and touchpoints that contribute most to conversions, as well as tracking content consumption patterns and trends.
  • Conversion rate insights: Uncovering key factors influencing conversion rates across account segments, sales stages, or campaigns to inform future outreach.
  • Engagement trend detection: Detecting shifts in how key accounts engage with different content types or formats to determine content priorities.
  • Centralized performance hub: Consolidating campaign metrics and results, enabling real-time monitoring and analysis of buying group behavior.
  • Resource optimization: Identifying underperforming tactics or channels to allow resource allocation to higher-impact activities.

The Importance Of A Unified Strategy

The promise of AI lies in driving innovation through efficiency over pursuing growth at any cost. For this reason, sophisticated strategic planning and data analysis should take precedence over ad-hoc content creation tasks.

With 55% of buyers using AI to automate and analyze data, and 45% focusing on streamlining and optimizing systems and processes, organizations need clear guidance, realistic expectations, and well-defined outcomes to succeed (findings from our Q4 2024 market research).

To achieve this, it is essential to upskill teams in AI and provide a suitable framework for its adoption, including clear guidelines for AI usage, privacy protections, and safeguards against cyber threats.

By doing so, Go-To-Market (GTM) teams can develop a structured approach to AI adoption, characterized by robust governance, standardization, and a focus on sustainable, value-driven implementation.

Key Takeaways

  • The world is experiencing an AI investment surge: Global investment in AI has reached unprecedented levels. However, many organizations struggle with a lack of cohesive AI strategies and measuring its impact on productivity.
  • Buyer-centric strategies: The increasing complexity of buying journeys, with large, defensive buying groups, presents a significant opportunity for B2B marketers to leverage generative and agentic AI for more effective engagement.
  • Ensure strong alignment with buyer needs: Centering AI practices around buyers and buying groups refines your targeting, messaging, and campaign optimization. This alignment directly influences brand perception and the overall quality of the buyer experience.

More Resources:


Featured Image: Golden Sikorka/Shutterstock