From sourdough to stuffed crust, the top places across the UK for pizza have been named to celebrate National Calzone Day (November 1) and one Edinburgh spot has been highlighted.

The funky Edinburgh pizza spot named one of the best in the UK by reviewers

Discover Canada's top attraction, Jasper National Park, and its scenic beauty. Explore hiking trails, waterfalls and wildlife, or relish the thrill of West Island's online casinos.

Jasper National Park Claims TripAdvisor’s Top Canadian Attraction Spot Amidst Increasing Virtual Gaming Interest

The Park Plaza's prior owner said the hotel will likely need significant investment to keep its competitive edge following a 2016 renovation.

The Boston Park Plaza has been sold: It’s now a Hilton

The Suidhe Viewpoint is one of Tripadvisor’s best-rated attractions in Fort Augustus, offering incredible views of the landscape. ‘Mike O’ wrote on Tripadvisor: “The views from here are stunning. Lakes, mountains, glens. In fact, the stop is also ...

‘Walkers’ paradise’ named one of this year’s best winter staycations

From Amsterdam to Italy, discover the top gourmet restaurants as per TripAdvisor's 2023 Travellers' Choice Awards

2023's best fine dining restaurants in the world

BRITS have got their eye on a small Italian city for their 2024 holidays, with stunning beaches and cheap hotels all part of its appeal. It may still only be October, but people are already

Stunning Italian seaside city tipped as top 2024 holiday spot – thanks to £23 hotels, great beaches & cheap nightlife

Stacker compiled a list of the highest-rated Middle Eastern restaurants in Dallas on Tripadvisor. Tripadvisor rankings factor in the average rating and number of reviews. Restaurants on this list may have recently closed. Keep reading to see if your ...

The best Middle Eastern restaurants in Dallas, according to reviewers on Tripadvisor

Anyone looking to make the most of the remainder of autumn with a cosy Scottish staycation should consider spending a night in the "fabulous" Stonefield Castle Hotel.

The cosy 'fabulous' Scottish castle you can stay in for less than £100 this November

The University Hospitals of Derby and Burton NHS Foundation Trust has continuously sought to avoid providing information when asked by journalists and the public

Derby NHS trust is getting a bad reputation over Freedom of Information requests

Still not convinced? The TripAdvisor reviews of Giethoorn speak for themselves. "It's so incredibly charming to walk along these canals," said one satisfied customer. "Every step is worth taking a break, taking a pic and taking in the charming scenery.

'Mini Venice' where tourists enjoy cafes and boating on canals without the crowds

Tripadvisor

tripadvisor

This post represents some key SEO strategies for guesthouses and hotels websites, based on what was discussed during the discussion of an SEO blog post. Creating a Website Both sources note that having online visibility is important, especially when it comes to companies operating in the travel market. According to the blog post, WordPress is a very good choice in which to operate within the USA market because it's friendly, easy to customize, and has a very strong support network. Understanding the Travel Market Sources underscore the need to understand the details of the English travel market. Tourists are often seeking something specific - for instance, proximity to some important cultural site or some spectacular natural landmark - so targeting keywords reflecting these needs is important, such as "guest house near Rila Monastery" or "guest house with traditional English breakfast." Listings in the travel directories and guesthouse websites will continue to be influential, especially in tourist capture. Reviews and Social Proof Reviews are cardinal on Google My Business and TripAdvisor for travelers. On the other hand, sources indicate one up-and-coming review platform and remind one that local context is important in SEO strategy. Since word of mouth is a big part of the culture, adding in social proof, such as testimonials and positive mentions on social media, is key. Outsmarting the Booking Giants While valuing the dominance of sites such as Booking.com and Airbnb, all sources strongly advocate not relying exclusively on these reservation platforms. They recommend balancing marketing efforts and making direct contact with guests through a website using SEO. This involves creating unique content related to local experiences, food, and traditions that appeal to a special type of visitor. Keyword Research and Content Creation This blog post, therefore, highlights that researching keywords is one of the best means of persuading owners not to use generic terms but to focus on keywords depicting their experiences and amenities. Instead of "guest house with a pool," think "guest house with natural spring-fed pool" for those special touches. The focus should be on highly qualitative and captivating content worth the visitor's time, such as blog posts covering local attractions, photo galleries, and even videos. Adding traditional folk music or telling stories about them will make them even more immersive. Link Building and Collaboration Sources stress the importance of relationship-building with other businesses and websites in the travel industry. It is possible to reach out to local bloggers, tourism organizations, and even other guesthouses to discuss collaborations. For instance, two guesthouses from different regions could create a joint blog post showcasing a beautiful road trip, which would extend their exposure and build quality backlinks. The Human Touch and Future of SEO The sources stress the human psychological aspect of SEO, where English travelers find authenticity and personal recommendations particularly appealing. They foresee further growth in voice search and the requirement for optimization regarding conversational searches. The sources, however, still believe human judgment will be important, especially in deeper insights and catering to the peculiar needs of each guest, as AI-powered search engines get smarter. It's all about finding that balance in how to automate and remain authentic, using AI as a strategic tool that makes sure that the distinctive character of a guesthouse is maintained. Holistic Marketing Approach The sources go on to point out that the holistic marketing approach is an integration of online-offline activities. Examples are the creation of an e-welcome package for guests with useful information and a reflection of traditional English hospitality within online communication. Similarly, social media should be utilized for contemporary guest interaction, sharing behind-the-scenes content, and local influencers assisting further increasing the scope and effectiveness of this interaction. In other words, effective SEO strategies for guesthouses and hotel websites balance technical prowess with cultural sensitivity and highlight the heart and soul of a guesthouse experience through meaningful connections between guesthouse owners and travelers. This post represents some key SEO strategies for guesthouses and hotels websites, based on what was discussed during the discussion of an SEO blog post. Creating a Website Creating a Website Both sources note that having online visibility is important, especially when it comes to companies operating in the travel market. According to the blog post, WordPress is a very good choice in which to operate within the USA market because it's friendly, easy to customize, and has a very strong support network. Understanding the Travel Market Understanding the Travel Market Sources underscore the need to understand the details of the English travel market. Tourists are often seeking something specific - for instance, proximity to some important cultural site or some spectacular natural landmark - so targeting keywords reflecting these needs is important, such as "guest house near Rila Monastery" or "guest house with traditional English breakfast." Listings in the travel directories and guesthouse websites will continue to be influential, especially in tourist capture. Reviews and Social Proof Reviews and Social Proof Reviews are cardinal on Google My Business and TripAdvisor for travelers. On the other hand, sources indicate one up-and-coming review platform and remind one that local context is important in SEO strategy. Since word of mouth is a big part of the culture, adding in social proof, such as testimonials and positive mentions on social media, is key. Outsmarting the Booking Giants Outsmarting the Booking Giants While valuing the dominance of sites such as Booking.com and Airbnb, all sources strongly advocate not relying exclusively on these reservation platforms. They recommend balancing marketing efforts and making direct contact with guests through a website using SEO. This involves creating unique content related to local experiences, food, and traditions that appeal to a special type of visitor. Booking.com Keyword Research and Content Creation Keyword Research and Content Creation This blog post, therefore, highlights that researching keywords is one of the best means of persuading owners not to use generic terms but to focus on keywords depicting their experiences and amenities. Instead of "guest house with a pool," think "guest house with natural spring-fed pool" for those special touches. The focus should be on highly qualitative and captivating content worth the visitor's time, such as blog posts covering local attractions, photo galleries, and even videos. Adding traditional folk music or telling stories about them will make them even more immersive. Link Building and Collaboration Link Building and Collaboration Sources stress the importance of relationship-building with other businesses and websites in the travel industry. It is possible to reach out to local bloggers, tourism organizations, and even other guesthouses to discuss collaborations. For instance, two guesthouses from different regions could create a joint blog post showcasing a beautiful road trip, which would extend their exposure and build quality backlinks. The Human Touch and Future of SEO The Human Touch and Future of SEO The sources stress the human psychological aspect of SEO, where English travelers find authenticity and personal recommendations particularly appealing. They foresee further growth in voice search and the requirement for optimization regarding conversational searches. The sources, however, still believe human judgment will be important, especially in deeper insights and catering to the peculiar needs of each guest, as AI-powered search engines get smarter. It's all about finding that balance in how to automate and remain authentic, using AI as a strategic tool that makes sure that the distinctive character of a guesthouse is maintained. Holistic Marketing Approach Holistic Marketing Approach The sources go on to point out that the holistic marketing approach is an integration of online-offline activities. Examples are the creation of an e-welcome package for guests with useful information and a reflection of traditional English hospitality within online communication. Similarly, social media should be utilized for contemporary guest interaction, sharing behind-the-scenes content, and local influencers assisting further increasing the scope and effectiveness of this interaction. In other words, effective SEO strategies for guesthouses and hotel websites balance technical prowess with cultural sensitivity and highlight the heart and soul of a guesthouse experience through meaningful connections between guesthouse owners and travelers. effective SEO strategies for guesthouses and hotel websites

SEO Strategies for Optimizing Hotel Websites

የሆቴል ድር ጣቢያዎችን ለማመቻቸት SEO ስልቶች

SEO Estrategias ukax Hotel ukan sitios web ukanakar sum apnaqañataki

Στρατηγικές SEO για βελτιστοποίηση ιστοσελίδων ξενοδοχείων

Estrategias SEO para optimizar sitios web de hoteles

Stratégies SEO pour optimiser les sites Web des hôtels

ホテルのウェブサイトを最適化するための SEO 戦略

SEO სტრატეგიები სასტუმროს ვებსაიტების ოპტიმიზაციისთვის

ຍຸດທະສາດ SEO ສໍາລັບການເພີ່ມປະສິດທິພາບເວັບໄຊທ໌ໂຮງແຮມ

Зочид буудлын вэбсайтыг оновчтой болгох SEO стратеги

SEO-strategieën voor het optimaliseren van hotelwebsites

SEO Strategies Yekukwidziridza Hotels Webhusaiti

กลยุทธ์ SEO สำหรับการเพิ่มประสิทธิภาพเว็บไซต์โรงแรม

Pawsitive Results: How to Build the Ultimate Puppy Trainer AI-chatbot With Coze

Pawsitive Results: Hoe om die Ultimate Puppy Trainer AI-chatbot met Coze te bou

Pawsitive Nəticələr: Coze ilə Ən Yaxşı Puppy Təlimçisi AI-chatbotunu necə qurmaq olar

Pawsitive Results: Как да изградите Ultimate Puppy Trainer AI-chatbot с Coze

Resultados positivos: Cómo crear el mejor chatbot de inteligencia artificial para adiestrar cachorros con Coze

نتایج Pawsitive: چگونه با Coze ربات هوش مصنوعی بهترین مربی توله سگ بسازیم

ポジティブな結果: Coze で究極の子犬トレーナー AI チャットボットを構築する方法

Pawsitive Results: Ndenge nini kotonga AI-chatbot ya Formateur ya mwana ya mbwa ya suka na Coze

Pawsitive rezultāti: kā izveidot izcilu kucēnu treneri AI tērzēšanas robotu ar Coze

Resultados positivos: como criar o melhor chatbot de IA para treinador de filhotes com Coze

Rezultatet Pawsitive: Si të ndërtoni AI-chatbotin e fundit të trajnerit të qenve me Coze

Павситивни резултати: Како са Цозе-ом изградити врхунског тренера штенаца са вештачком интелигенцијом

Vuyelo bya Pawsitive: Ndlela yo Aka AI-chatbot ya Mudyondzisi wa Timbyana wo Hetelela Hi Coze

Google Has Monopoly Power In The U.S. Search Ads Market

How Does Google Make Money: Search Ads and Text Ads Explained

Effective Anomaly Detection Pipeline for Amazon Reviews: References & Appendix

Author:
(1) David Novoa-Paradela, Universidade da Coruña, CITIC, Campus de Elviña s/n, 15008, A Coruña, Spain & Corresponding author (Email: david.novoa@udc.es);
(2) Oscar Fontenla-Romero, Universidade da Coruña, CITIC, Campus de Elviña s/n, 15008, A Coruña, Spain (Email: oscar.fontenla@udc.es);
(3) Bertha Guijarro-Berdiñas, Universidade da Coruña, CITIC, Campus de Elviña s/n, 15008, A Coruña, Spain (Email: berta.guijarro@udc.es). Table of Links Abstract and Introduction Related work The proposed pipeline Evaluation Conclusion & Acknowledgements References & Appendix Abstract This paper presents a pipeline to detect and explain anomalous reviews in online platforms. The pipeline is made up of three modules and allows the detection of reviews that do not generate value for users due to either worthless or malicious composition. The classifications are accompanied by a normality score and an explanation that justifies the decision made. The pipeline’s ability to solve the anomaly detection task was evaluated using different datasets created from a large Amazon database. Additionally, a study comparing three explainability techniques involving 241 participants was conducted to assess the explainability module. The study aimed to measure the impact of explanations on the respondents’ ability to reproduce the classification model and their perceived usefulness. This work can be useful to automate tasks in review online platforms, such as those for electronic commerce, and offers inspiration for addressing similar problems in the field of anomaly detection in textual data. We also consider it interesting to have carried out a human evaluation of the capacity of different explainability techniques in a real and infrequent scenario such as the detection of anomalous reviews, as well as to reflect on whether it is possible to explain tasks as humanly subjective as this one. Keywords: Anomaly Detection, Text Reviews, Transformers, Explainability 1. Introduction Nowadays more than ever in history, the user opinions about products and services have a great impact on the future of the company that offers them. In such a globalized and highly competitive world, online review platforms, such as electronic commerce (e-commerce), play a crucial role in the credibility of products and services. These user reviews usually come in the form of text reviews or numerical ratings, accompanied in some cases by images or videos. These reviews provide users with information about the product or service they are considering purchasing, which directly influences the number of sales. Most people make purchase decisions based on ratings and reviews from other users [1]. In the case of many companies, such as Amazon, each product in the store has a list of text reviews published by customers of the platform. Users can access this list of reviews (opinions) to obtain extra information about the product, being able to mark the reviews as useful, which will position those reviews with the most votes at the top of the list. In addition to this, users can report to Amazon if they feel a review is inappropriate, for example, if its content is incorrect. This procedure to rank reviews based on their usefulness and report inappropriate reviews is carried out manually by platform users. As a result, Amazon reported more than 200 million suspected fake reviews in 2020 alone [2]. This problem does not only occur on Amazon, but affects all platforms that allow their users to post reviews. For example, Tripadvisor uses an automatic system capable of distinguishing between normal, suspicious and inappropriate reviews [3]. Inappropriate ones are automatically removed (3.1% of review submissions in 2020), while those classified as suspicious are reviewed again by a human moderator (5.1% of review submissions in 2020). On the other hand, in machine learning (ML), anomaly detection (AD) is the branch that builds models capable of differentiating between normal and abnormal data [4]. At first, anomaly detection might seem like a classification problem with only two classes. However, anomalies tend to occur infrequently or are non-existent, so normal data prevails in these scenarios. Because of this, it is common for models to be trained only with normal data. The goal of these models is to represent the normal class as well as possible in order to classify new data as normal or anomalous. The technological development of recent years has allowed the construction of very powerful models for Natural Language Processing (NLP) [5]. Contrary to other tasks such as Sentiment Analysis [6] or Question Answering [7], the application of anomaly detection on texts is still at an early stage, probably due to its lower demand. For this reason, we present in this article a pipeline that, given text reviews of a product (in this case from Amazon), addresses opinion filtering as an anomaly detection problem where: • Reviews containing representative information about the product are considered as the normal class. • Reviews whose content has nothing to do with the product to be represented are considered as the anomalous class. The proposed pipeline allows us to carry out the following tasks: • Classify reviews as normal or anomalous, allowing us to locate those that do not describe characteristics of the product to which they are associated, and therefore have no value for the users of the platform. • Issue a normality score associated with each review. • Generate an explanation that justifies the classification made for each review by the system. The pipeline’s ability to solve the anomaly detection task was evaluated using different datasets created from a large Amazon database [8]. In addition, to evaluate the explainability module, a study was carried out to compare three explainability techniques in which a total of 241 people had participated. The objective of this study is both to measure the impact of the explanations on the reproducibility of the classification model by the respondents, as well as the usefulness of these explanations. We believe that this work can be useful to automate tasks such as those mentioned in online review platforms, in addition to the existing interest in the application of anomaly detection models on texts, for which it can serve as inspiration to solve similar problems. We also consider it interesting to have carried out a human evaluation of the capacity of different explainability techniques in a real and infrequent scenario such as the detection of anomalous reviews, as well as to reflect on whether it is possible to explain tasks as humanly subjective as this one. This document is structured as follows. Section 2 contains a brief review of the main anomaly detection works in texts and, more specifically, in text reviews. Section 3 describes the proposed pipeline and its operation. Section 4 collects the experimentation carried out and, finally, conclusions are drawn in Section 5. This paper is available on arxiv under CC 4.0 license. Author: (1) David Novoa-Paradela, Universidade da Coruña, CITIC, Campus de Elviña s/n, 15008, A Coruña, Spain & Corresponding author (Email: david.novoa@udc.es); (2) Oscar Fontenla-Romero, Universidade da Coruña, CITIC, Campus de Elviña s/n, 15008, A Coruña, Spain (Email: oscar.fontenla@udc.es); (3) Bertha Guijarro-Berdiñas, Universidade da Coruña, CITIC, Campus de Elviña s/n, 15008, A Coruña, Spain (Email: berta.guijarro@udc.es). Author: Author: (1) David Novoa-Paradela, Universidade da Coruña, CITIC, Campus de Elviña s/n, 15008, A Coruña, Spain & Corresponding author (Email: david.novoa@udc.es); (2) Oscar Fontenla-Romero, Universidade da Coruña, CITIC, Campus de Elviña s/n, 15008, A Coruña, Spain (Email: oscar.fontenla@udc.es); (3) Bertha Guijarro-Berdiñas, Universidade da Coruña, CITIC, Campus de Elviña s/n, 15008, A Coruña, Spain (Email: berta.guijarro@udc.es). Table of Links Abstract and Introduction Abstract and Introduction Related work Related work The proposed pipeline The proposed pipeline Evaluation Evaluation Conclusion & Acknowledgements Conclusion & Acknowledgements References & Appendix References & Appendix Abstract This paper presents a pipeline to detect and explain anomalous reviews in online platforms. The pipeline is made up of three modules and allows the detection of reviews that do not generate value for users due to either worthless or malicious composition. The classifications are accompanied by a normality score and an explanation that justifies the decision made. The pipeline’s ability to solve the anomaly detection task was evaluated using different datasets created from a large Amazon database. Additionally, a study comparing three explainability techniques involving 241 participants was conducted to assess the explainability module. The study aimed to measure the impact of explanations on the respondents’ ability to reproduce the classification model and their perceived usefulness. This work can be useful to automate tasks in review online platforms, such as those for electronic commerce, and offers inspiration for addressing similar problems in the field of anomaly detection in textual data. We also consider it interesting to have carried out a human evaluation of the capacity of different explainability techniques in a real and infrequent scenario such as the detection of anomalous reviews, as well as to reflect on whether it is possible to explain tasks as humanly subjective as this one. Keywords: Anomaly Detection, Text Reviews, Transformers, Explainability Keywords: 1. Introduction Nowadays more than ever in history, the user opinions about products and services have a great impact on the future of the company that offers them. In such a globalized and highly competitive world, online review platforms, such as electronic commerce (e-commerce), play a crucial role in the credibility of products and services. These user reviews usually come in the form of text reviews or numerical ratings, accompanied in some cases by images or videos. These reviews provide users with information about the product or service they are considering purchasing, which directly influences the number of sales. Most people make purchase decisions based on ratings and reviews from other users [1]. In the case of many companies, such as Amazon, each product in the store has a list of text reviews published by customers of the platform. Users can access this list of reviews (opinions) to obtain extra information about the product, being able to mark the reviews as useful, which will position those reviews with the most votes at the top of the list. In addition to this, users can report to Amazon if they feel a review is inappropriate, for example, if its content is incorrect. This procedure to rank reviews based on their usefulness and report inappropriate reviews is carried out manually by platform users. As a result, Amazon reported more than 200 million suspected fake reviews in 2020 alone [2]. This problem does not only occur on Amazon, but affects all platforms that allow their users to post reviews. For example, Tripadvisor uses an automatic system capable of distinguishing between normal, suspicious and inappropriate reviews [3]. Inappropriate ones are automatically removed (3.1% of review submissions in 2020), while those classified as suspicious are reviewed again by a human moderator (5.1% of review submissions in 2020). On the other hand, in machine learning (ML), anomaly detection (AD) is the branch that builds models capable of differentiating between normal and abnormal data [4]. At first, anomaly detection might seem like a classification problem with only two classes. However, anomalies tend to occur infrequently or are non-existent, so normal data prevails in these scenarios. Because of this, it is common for models to be trained only with normal data. The goal of these models is to represent the normal class as well as possible in order to classify new data as normal or anomalous. The technological development of recent years has allowed the construction of very powerful models for Natural Language Processing (NLP) [5]. Contrary to other tasks such as Sentiment Analysis [6] or Question Answering [7], the application of anomaly detection on texts is still at an early stage, probably due to its lower demand. For this reason, we present in this article a pipeline that, given text reviews of a product (in this case from Amazon), addresses opinion filtering as an anomaly detection problem where: • Reviews containing representative information about the product are considered as the normal class. • Reviews whose content has nothing to do with the product to be represented are considered as the anomalous class. The proposed pipeline allows us to carry out the following tasks: • Classify reviews as normal or anomalous, allowing us to locate those that do not describe characteristics of the product to which they are associated, and therefore have no value for the users of the platform. • Issue a normality score associated with each review. • Generate an explanation that justifies the classification made for each review by the system. The pipeline’s ability to solve the anomaly detection task was evaluated using different datasets created from a large Amazon database [8]. In addition, to evaluate the explainability module, a study was carried out to compare three explainability techniques in which a total of 241 people had participated. The objective of this study is both to measure the impact of the explanations on the reproducibility of the classification model by the respondents, as well as the usefulness of these explanations. We believe that this work can be useful to automate tasks such as those mentioned in online review platforms, in addition to the existing interest in the application of anomaly detection models on texts, for which it can serve as inspiration to solve similar problems. We also consider it interesting to have carried out a human evaluation of the capacity of different explainability techniques in a real and infrequent scenario such as the detection of anomalous reviews, as well as to reflect on whether it is possible to explain tasks as humanly subjective as this one. This document is structured as follows. Section 2 contains a brief review of the main anomaly detection works in texts and, more specifically, in text reviews. Section 3 describes the proposed pipeline and its operation. Section 4 collects the experimentation carried out and, finally, conclusions are drawn in Section 5. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Explained Anomaly Detection in Text Reviews: Can Subjective Scenarios Be Correctly Evaluated?

Calling All Writers: Learn What The Crypto-API Writing Contest Is All About From The CoinGecko Team

সমস্ত লেখকদের কল করা হচ্ছে: CoinGecko টিম থেকে ক্রিপ্টো-এপিআই লেখার প্রতিযোগিতাটি কী তা জানুন

Aufruf an alle Autoren: Erfahren Sie vom CoinGecko-Team, worum es beim Crypto-API-Schreibwettbewerb geht

Llamando a todos los escritores: aprenda de qué se trata el concurso de redacción Crypto-API del equipo CoinGecko

Appel à tous les écrivains : découvrez en quoi consiste le concours d'écriture Crypto-API auprès de l'équipe CoinGecko

सभी लेखकों को बुलावा: कॉइनगेको टीम से जानें क्रिप्टो-एपीआई लेखन प्रतियोगिता क्या है

ライターの皆さんへ: CoinGecko チームから暗号 API ライティング コンテストについて学びましょう

모든 작가에게 전화하기: CoinGecko 팀으로부터 Crypto-API 글쓰기 콘테스트의 모든 내용을 알아보세요

Ligando para todos os escritores: saiba do que se trata o concurso de redação de Crypto-API da equipe CoinGecko

Обращение ко всем писателям: узнайте, что такое конкурс авторов Crypto-API, от команды CoinGecko

Tüm Yazarlara Çağrı: CoinGecko Ekibinden Kripto API Yazma Yarışmasının Neyle İlgili Olduğunu Öğrenin

Kêu gọi tất cả người viết: Tìm hiểu nội dung cuộc thi viết về tiền điện tử-API từ nhóm CoinGecko

召集所有作家：从 CoinGecko 团队了解 Crypto-API 写作比赛的全部内容

While the headline might look a little click-baity, the reason you’re reading this is because you’ve had enough of something Google has done. And… You’re not alone. Google has been slowly making a mess of the internet for a few years now. Maybe not quite as much as the shit show that is Facebook or Meta or whatever the hell they’re called at the moment. But actually, while Zuck’s platforms might be responsible for sucking our attention and (and also proliferating , among other things) - Google sucks for a slightly different and more tangible reason. damaging our mental health hate speech and genocide They’ve basically made all the things that we’ve gotten used to for free online into crappy shadows of what they once were. Sitting comfortably? Let me explain… The Slow Decline of the Search Engine I’m a SEO professional, which means I spend a lot of time looking at and analyzing Google’s search results. I’ve created countless articles for a huge variety of websites, from tech unicorns to niche blogs. And one thing that must be said is that the world's favorite search engine has not been making it easy for either the end-user or those looking to build their platform. To clarify: Around ten years ago, everyone sussed out that keyword stuffing (basically putting their focus keyword tens of times into a blog post) got you onto page one. Cue loads of low-quality content on page one, all of it packed full of obvious keywords. Google decided to fix this, and suddenly there was a strong(er) focus on domain authority. This basically meant stronger domains with lots of backlinks tended to do well. Not always though, and good content was still important, but the backlinks meant you could game the system. Now the SERPs were packed full of crappy websites made to host backlinks with very thin or regurgitated content that didn’t answer the users’ intent. Enter the AI revolution in late 2022. Everyone and their gran is into using ChatGPT to write their shopping list, and also, oh hey… There are blogs packed full of AI content popping up everywhere. Now, I’m not against AI content at all; I think it’s great (if done right). Check my previous article on here: . why AI content isn’t a bad thing But it seems that at Google at least, something clicked. Google’s Helpful Content Update 2023 If you’re in search engine marketing, or you spend any time on Linkedin, you probably noticed everyone was up in arms about the late . 2023 Google algorithm update Now, Google releases probably 3 or 4 big algorithm updates every year. Most result in some ranking changes, usually a little turbulence, and then back to business as usual. Not this puppy. The 2023 edition seems to have royally f**ked a number of quite big publishers, not to mention niche bloggers and site owners. It has also rewarded a number of fairly safe bet forums and magazine sites such as Reddit, Tripadvisor, Forbes, and other big publishers. What’s the reason, many are asking? Well, the general juice is that Google saw that AI content was starting to become a big deal and was starting to dominate the SERPs. So, to combat this, they switched the focus to the ‘safe’ publishers, to the huge detriment of many smaller site owners. OK, you might say. Fair enough. Maybe. Except, if you ask me, much of the time the actual query isn’t being answered - it’s just the trusted (aka big domain authority) sites that have done well from this. Not entirely, I might add. I am currently running a niche blog that has done very well since the helpful update, highlighting that you can still rank well if you do your SEO right. But a quick look at the SERPs shows some more mess. The same algorithm update seems to have placed a bunch of links to Google Books in the search results. Check out this search result below, which is on about page three. Now, you might say that page 3 is irrelevant because, as the , you could hide a dead body on page 2 of Google, and no one would spot it. old joke goes Not so true these days as the first page of Google now scrolls seamlessly into pages two through to pretty much forever. This is one good Google update by the way. But the addition of the book’s link totally messes up the flow and is one of the really sucky things that Google has added in this recent algorithm update. The Hot Mess That Is YouTube Alright, now we come to the exhibit B - which has been a mess for a while now. If you’re old like me, you might remember how YouTube was free and cool, and you could spend hours on there going down cute cat rabbit holes. Google bought them out, and soon enough, ads were popping up every so often. Fine at the time, in moderation. But now it seems that YouTube is basically American TV. Ads pop up halfway through content at inopportune moments, and some of the ad placements are horrible. I’ve seen ads for horror movies during kids’ content - yes, seriously… On top of this, the algorithm that recommends stuff is just… I don’t really know where to start. It’s a mess. I get recommended weird alt-right stuff because I might have watched a click-bait video about the state of the Middle East. And now, I’m being shilled vids about how Jews are responsible for whatever or Muslims are evil. OK. And the search. WT-actual-F? Say you search for a specific music artist on YouTube - maybe the first three to five results will be them (if you’re lucky), and the rest of the content will be random AF. If you ask me, YouTube is barely fit for purpose. But, it’s pretty much the only choice, despite some limp competition from sites like Vimeo and , it seems that YouTube is going to remain the dominant video platform for the foreseeable. DailyMotion Which might be why the youth are turning to TikTok in droves. And Another Thing… It’s not just YouTube; the Google search engine and its algorithms are kinda sucky too by the way. Their paid search platform is a mess and is also a hotbed of . A subject I’ve written about extensively. Yes, they have done some work to try and close this practice down but it’s the gift that keeps giving. ad fraud and click fraud Basically, ad fraud works for Google, so while they’ll be seen to be doing something to fix it, why stop it completely? They make money from it, so hey. So… What’s the Alternative? The thing is, there are alternatives - of course. But by and large, we’re all very stuck in Google’s eco-system. The Google SERPs are still massively important and are far and away the most used method of searching online. Despite reports that TikTok overtook Google a few years back, the fact is, they didn’t. People do tend to use the internet differently now though. We ask Alexa or Siri for local or timely information, we check Instagram or TikTok for some stuff, and we check forums like Reddit for info when we don’t want to sift through the shitty SERPs on Google. Even AI and the rise of Chat GPT have become an alternative for some of us. And while you can search using AI on a number of search engines and browsers, it’s still in its infancy. Until the mainstream susses that AI or social media offer better results, don’t expect things to change any time soon. And while there is always Bing, Google is still going to remain the big dog for a while. Bing’s foray into AI search results has so far not shifted the market much in their favor and probably won’t any time soon. For search engine pros and business managers? It’s time to start looking at strategy, beyond simple search engine rankings. As a content strategist, I focus heavily on SEO and hitting those search engine rankings (e.g., Google). But I’m also very aware of the fact that one algorithm change can wipe out all your organic hard work. Use Google, but be aware: they’re using you too. And make sure to spread your net beyond their eco-system when it comes to marketing.

Why Google Sucks: But We're Stuck With Them (For Now)

কেন গুগল খারাপ: কিন্তু আমরা তাদের সাথে আটকে আছি (এখনের জন্য)

Warum Google scheiße ist: Aber wir bleiben (vorerst) bei ihnen hängen

Por qué Google apesta: pero estamos atrapados con ellos (por ahora)

Pourquoi Google est nul : mais nous sommes coincés avec eux (pour l'instant)

Google बेकार क्यों है: लेकिन हम उनमें फंस गए हैं (अभी के लिए)

Google がダメな理由: しかし、私たちは (今のところ) Google に行き詰まっています

Google이 형편없는 이유: 하지만 우리는 그들과 붙어있습니다(현재로서는)

Por que o Google é uma merda: mas estamos presos a eles (por enquanto)

Почему Google — отстой: но мы застряли на них (пока)

Google Neden Berbat: Ama Biz Onlara Kaldık (Şimdilik)

Tại sao Google tệ: Nhưng chúng tôi đang mắc kẹt với họ (Hiện tại)

为什么谷歌很糟糕：但我们还是被他们困住了（暂时）

Choosing the right markets and the pitfalls to avoid along the way Not all businesses are good choices for solo founders. Some are almost impossible to grow when you're a business of one. But how can you find a business that you can easily grow as a solo founder or small team? Understanding the types of businesses that are most suited to being solo founders can save years of frustration. Understanding the markets that are suited to small self-funded businesses, will shorten your road to success from years to months. Unfortunately, I spent a decade learning this the hard way. Every day I see people making the same mistakes, mainly because we naturally copy what big companies do. Don't think big. Think small We're unconsciously influenced by companies that we use ourselves. Think , Notion, , Gmail. Netflix Twitter These companies all have a few things in common: They require scale (lots of customers) They target consumers for a major part of their business They charge a low price (or are free) They exist in large markets They have a lot of money to spend But don't worry. As a small business, we can craft an offering that avoids all these points and is much more likely to succeed. Here's how to do it, step by step: 1. Avoid businesses that require scale As a solo-founder or small bootstrapped business, any type of product/service that requires a massive number of users is best to avoid. I discovered this the hard way with one of my early startup attempts. The idea was a wedding directory that I was positioning as "TripAdvisor for weddings" (this was in TripAdvisor's heyday). It was a 2-sided marketplace and targeted the entire UK wedding market. The market I chose was too big. The business required network effects on a massive scale to work, and trying to attract engaged couples and wedding vendors on this scale, with no budget, was basically impossible. As a result, I was forced to keep changing the focus of the business, making it constantly more niche, in an attempt to have a small enough market where the network effects could take over. Ultimately I couldn't build those effects quickly enough, in such a transient market as weddings. I shut it down. It failed. It's very hard to create network effects when you're the only one working on it. You don't have the time or money to make it work. Have a think about other businesses that require network effects. For example, social networks. All the major social networks were funded early and were typically unprofitable for a long time before they reached a scale that allowed them to monetize effectively. As a small business, you can't compete against the spending, or sustain losses for a prolonged period. 2. Don't charge low prices The other thing to avoid is charging low prices. Why? Because getting customers is hard! Sales and marketing are both major time drains and you can't spend all your time doing them. Again, if you have low prices, you need to reach hundreds or thousands of customers (that scale problem again). I see a lot of people starting a subscription business and charging "Netflix-style" prices. e.g. $10/month. We are so conditioned to these types of pricing, that we naturally want to replicate them. But converting consumers to a $10/month subscription is much more difficult than you might imagine. If you work really hard and get up to 100 users, you're still only at $1k per month and that doesn't take into account running costs and users canceling. You need to constantly bring on more users to replace those who leave. On the flip side, you could just have 1 single B2B client paying $10k per month for a service and make 10x the amount, with lower running costs and no churn. 1 sale vs. 100. 10x revenue vs 1x. 3. Target businesses. Not consumers The easiest way to avoid the challenges of scale and low prices is to target businesses, rather than consumers. Not only does it allow you to sell at higher prices (so you don't need scale), it's also a much easier sell. Here are a few reasons why: Businesses are happy to spend money on things that help them grow. It increases revenue. Business expenses reduce profits and therefore taxes on those profits. The expense is offset against future taxes. Businesses allocate money for training, marketing, etc. They are literally looking to spend the money. It's often part of somebody's job description to spend that money, so you can find out what role within the company spends the money and sell to them directly. This is not true in the consumer world. Consumers are very cautious with spending. A $49 subscription to a consumer is a lot, whereas a business wouldn't bat an eyelid. Here are a couple of examples of the difference between consumer and business mentality with spending: Twitter Blue caused outrage in the Twitter community when they started charging £6 per month. They charge organizations £1,140 a month, for that same tick in a different color and Organizations are happy to pay. Gmail is one of the most popular email tools ever created. If they started charging $1 a month, they would lose 90+% of their users to a free alternative. Meanwhile, Superhuman email launched a couple of years ago and targeted business users exclusively. They shook the market by charging $30 per month for an email client. This is a service we are conditioned to expect to be free. But the product was great and helped business users save time on emails, and therefore allocate their time elsewhere and make more money. It grew like wildfire. If you're a solo founder, you need to minimize your time spent on attracting customers (because it's just you) and maximize your product price. Selling B2B solves both of these problems. 4. Don’t choose a large market The larger the market, the bigger the competition. If you were a goldfish, would you rather live in an ocean or a garden pond? Probably the pond. In the ocean, you are competing with millions of other fish, most of whom are much bigger than you, and will ultimately eat you. In a pond, you might be the biggest fish. This is exactly how you want to think about your business. Choose a market where you can be the biggest fish. If you decide to start a sports shoe brand, you'll be competing with Nike, Adidas, and countless others. Niche that down and make it a Pickleball shoe brand and suddenly you're the only player in the market. You're the biggest fish. You can hone in specifically on that customer's needs and take the entire pickleball shoe market. In Summary Swap scale for small and charge higher prices to businesses. Instead of a social network, build a niche professional community Instead of an AI blog, write an AI image-generation newsletter for social media managers Instead of a general web design agency, start a web agency targeting Logistics companies in the UK Think of smaller markets with bigger budgets. The opportunities are endless. I hope this guide was helpful. Next week I'll be posting a continuation of this guide on choosing a business that suits your personality and skill sets, which makes it exponentially more likely to grow. Subscribe to get that guide sent to your inbox next week. Also published here.

4 Mistakes to Avoid When Building a One-Person Business

4 errores que se deben evitar al crear un negocio unipersonal

4 erreurs à éviter lors de la création d’une entreprise individuelle

एक व्यक्ति का व्यवसाय बनाते समय बचने योग्य 4 गलतियाँ

個人事業を立ち上げる際に避けるべき 4 つの間違い

4 erros a evitar ao construir um negócio individual

4 sai lầm cần tránh khi xây dựng doanh nghiệp một người

建立一个人的企业时要避免的 4 个错误

Meta recently conceded that in-app behavioral advertising in the EU can no longer be opt-in by default, marking the end of an era. The regulators won, at least for now. Every few decades, major shifts happen that change how an industry fundamentally operates and who the new winners and losers are. We are in the midst of one such shift in the digital advertising industry. This has been driven by three critical changes in the ecosystem, all privacy-related: Apple announced the in 2021 App Tracking Transparency framework Google is in 2024 deprecating third-party cookies A slew of privacy legislation is either already in place (like the , ) or is about to be in place (like the ) California Privacy Rights Act EU General Data Protection Regulation EU Digital Services Act While the effects of these changes have been felt by the advertising industry globally, the for a few years now. . In the past three weeks, there have been significant announcements from , , and about upcoming changes to their products in the EU. It’s probably fair to say these changes mark the end of an era for behavioral advertising in the EU, and the platforms have made peace with that. EU, in particular, has been at war with behavioral advertising And winning Meta Snap TikTok In this piece, we’ll dive into: How the advertising ecosystem runs (or used to run) Privacy interventions and impact on behavioral advertising EU’s war on behavioral advertising and why platforms are conceding Possible future paths How the advertising ecosystem runs (or used to run) Let’s start with some simple concepts. An ad shown to you can be contextual or behavioral (sometimes both). A is something that is shown alongside organic results (non-ads) in the context of whatever you are doing. For example, if you are on a food delivery app like DoorDash and you see an ad for a restaurant alongside organic results, that is a contextual ad. In that case, your identity doesn’t really matter. DoorDash knows your location and what you’re looking for and therefore shows you a relevant ad. Knowing your identity and interests does help improve the relevance of these ads, but it’s secondary to the context itself. contextual ad A is something that’s shown to you because of your past behavior. For example, you might have visited an office chair brand’s page on Instagram, and therefore you get served ads for another office chair brand; that’s exactly what happened to me with the ad on the right. This can either be based on direct behavior (like the office chair example) or inferred behavior — people like you who looked for chairs also looked for monitors. One step further, people who looked at chairs are also likely to work from home more and therefore be open to lunch service. All of those are potential ads that can be shown to you. behavioral ad A subset of products, most often search products, are perfect for contextual ads — Google Maps, Google Search, DoorDash, Yelp, Thumbtack, TripAdvisor, and Zocdoc, to name a few. The user has high intent to do something specific. However, most high-intent products are low-medium frequency. You are not looking for plumbers every day on Thumbtack, and you are not looking to order food delivery every hour of the day on DoorDash; therefore, the amount of contextual ad inventory is lower — highly effective but lower in volume. On the other hand, social media platforms where users spend hours doomscrolling have a large volume of non-contextual inventory. Random ads are annoying and ineffective, and therefore the most effective way to monetize this inventory is through behavioral ads. Unsurprisingly, . Before ~2018 (when EU’s GDPR kicked in), there was essentially a about you, collected through a long list of highly effective AdTech mechanisms. Some notable examples: behavioral ads require a lot of data, specifically data about you free flow of data Every iOS or Android phone had a unique “ ” assigned to it (and by association, to you). This ID was accessible to all apps on your device, i.e. your Uber app and your Google Maps app both knew you are mobile advertising ID 123 and could easily cross-identify you across apps. mobile advertising ID Most browsers (except Safari and Firefox) support “ ”, which are essentially a piece of data that one site could place on your browser and another site could access. There would be a “cookie ID” associated with you, and you could be easily cross-identified across websites. third party cookies Note that . You could go deep inside your settings and reset these IDs, but most people never do that. This resulted in a couple of second-order effects. these identifiers, for all practical purposes, were permanent First, companies that are “ ” started purchasing data from several different data providers and ; for example, app 1 could tell the broker that you bought an expensive piece of furniture, website 2 could tell them you have an account with a high-end bank, and the data broker could put that together and categorize you as “high propensity of spend” person; note that when I say “you,” that refers to your identifiers (mobile advertising ID or third party cookie ID) data brokers building “profiles” about you As more and more data became available, the profiles had more fidelity to them, and identification became more sophisticated; for example, if app 1 had your mobile advertising ID and website 2 had your Chrome third-party cookie ID, a data broker or an ad platform could compare your IP addresses and know it is the same person. Take this one step further. A data broker could also use a third data source and find an email associated with your IDs. One more step further, they could buy your address from a utility or telecom company and find your address. This might sound far-fetched, but it’s not — are a very commonly used data product in the AdTech stack today, and telecom companies are . If you need a more entertaining take, check out . device graphs notorious for selling your data John Oliver’s episode on data brokers Second, ; through a combination of technical mechanisms, an advertiser could say you first saw an ad on Facebook, then an ad on Google Search, then a display ad on NYTimes, and eventually bought an item from their website, so each of those three advertising platforms get shared credit. Attribution/measurement of advertising campaigns became more and more precise While this sounds privacy invasive (and it is), this resulted in a highly efficient advertising ecosystem. Advertisers knew exactly which users they were targeting, and since they had all these extra behavioral signals to know how likely a user was to engage, they were willing to pay higher cost per impression (CPM) for ad inventory, thereby generating more revenue for a media publisher. Precise attribution/measurement turbocharged this further. However, you can see how this was . This rightfully raised concerns about data consolidation in the hands of both data brokers and large technology companies, and an intervention was inevitable. It was less a matter of if and more a matter of when. becoming the wild west — highly effective advertising no doubt, but also an uncontrolled orgy of data acquired with non-existent or questionable user consent Privacy interventions and impact on behavioral advertising Let’s dive into each of the three privacy interventions that arose in an attempt to tame the Wild West. First, Apple introduced the . It sounds jargon-y, but the change is relatively simple. Prior to ATT, every app by default had access to your advertising ID, i.e. it was opt-in by default. This meant you could easily be tracked across apps and therefore shown effective behavioral ads. For example, you installed the Strava app to track your runs, you are now on Facebook, and you are shown an ad for Strava Premium. After ATT, the access to this , i.e. an app had to show you a pretty aggressive prompt to get access to your identifier, and you explicitly needed to say yes. The average opt-in rate ended up close to (with a lot of caveats).' App Tracking Transparency (ATT) framework identifier became opt-out by default ~34% We won’t go into much detail here, but this prompt was launched by Apple in the guise of embracing privacy — a smart chess move. The consensus opinion today is that this was an opportunistic move from Apple, which no doubt improves privacy but also heavily hurts Apple’s competitors as they . The impact was that user identity was available much less often. prop up their own ads business Note here that for behavioral advertising to happen in the Strava-Facebook example, there is not one but two apps that need to have received opt-in from you, i.e. the addressable market does not drop to 34%, it drops to 34% * 34% = ~12%. Therefore, . cross-app behavioral advertising on iOS is no longer effective at scale Second, Google announced that it will . The consensus opinion is that the change helps Google achieve a dual purpose: appease regulators who are breathing down their neck for , while taking control back from what’s now a . Google’s post third-party cookies will still allow cross-site retargeting, but in a more private way where all information is stored on-device within the browser, i.e. there are no more cross-site “cookie IDs” assigned to you. While the new mechanism preserves some of the status quo, . deprecate third-party cookies in 2024 potential anti-trust behavior in AdTech fairly bloated advertising tech stack new mechanisms cross-site behavioral advertising is going to have much less fidelity and, therefore, effectiveness Which brings us to the third intervention — . The most aggressive of these is the EU’s GDPR, which went into effect in 2018. The California Consumer Privacy Act (CCPA) went into effect in 2020. While the progression has been gradual, the reason these laws matter for advertising companies today more than ever is because the laws take aim at the (i.e. you do a bunch of different things inside the Facebook app and Facebook gets to use that data to show you behavioral ads within the app). privacy regulations only remaining and mission-critical advertising mechanism — behavioral advertising within companies’ own apps EU’s war on behavioral advertising and why platforms are conceding A primary feature that makes the California laws (arguably the strictest privacy law in the US) less aggressive than the EU’s GDPR is that it . For example, the 2020 California Consumer Privacy Act (CCPA) requires companies that are considered “data sellers” under the law to provide explicit opt-outs on web pages, but the . does not require explicit opt-ins and only requires platforms to provide opt-outs default is still opt-in EU’s GDPR takes this up another notch and requires for behavioral advertising. This consent needs to be . For example, Meta cannot gate content behind a behavioral advertising consent prompt. explicit opt-in/consent freely given, specific, informed, and unambiguous So, the simplistic inference from this is that Meta needs to get explicit consent for all behavioral understanding, including the last remaining mechanism — showing ads within their own platform. If Meta is forced to do this, the opt-in will likely be small (the Apple opt-in rates were ~34%), and this majorly shrinks Meta’s addressable advertising market in the EU. To not meet that fate, Meta made a creative legal argument: GDPR requires any company to have one of ** **if the company needs to process personal data. Paraphrasing, these are — consent, a necessity to fulfill a contract, a necessity for legal compliance, a necessity to protect the vital interests of the user, a necessity to perform a public interest task, and a necessity for legitimate interests pursued by the company. six legal bases Of these six, Meta held the stance that they were processing personal data using “ legitimate interests” (the last of the six) as the legal basis and therefore did not need to show an explicit opt-in prompt. Last month, the Norwegian Data Protection Authority that . Paraphrasing the : provided their enforcement decision Meta’s use of “legitimate interests” as the legal basis is not valid enforcement decision Based on several past court rulings, Meta must prove that legitimate interest “cannot reasonably be achieved just as effectively by other means less restrictive of the fundamental rights and freedoms of data subjects.” Based on another past court ruling, despite the fact that Meta provides a free service, the user of that network cannot reasonably expect that the operator of the social network will process that user’s personal data, without his or her consent, for the purposes of personalized advertising. Four days after the Norwegian Data Protection Authority’s ruling, Meta that they would be changing their legal basis from “legitimate interests” to “consent.” In practice, what this means is that . While Apple’s changes significantly cut down the ability to advertise cross-app, Meta was holding on to hope that it would be able to preserve all in-app behavioral advertising (including in the EU), and the writing is now on the wall. shortly after with their own announcements that non-personalized versions of their products will soon be available to users. A total of are likely to follow suit. announced Meta is conceding that behavioral advertising within their own app in the EU can no longer be opt-in by default and followed suit Snap TikTok 19 platforms that are in scope Possible future paths It is still to be seen what percent of users opt into behavioral advertising. If the Apple opt-in rates are any indication, it may get to the ~34% rate seen there, but it may also go higher if platforms are allowed to be creative about opt-in language and user interfaces. It is hard to say what exactly the long-term effects of ending the opt-in-by-default regime for behavioral advertising in the EU will be, but here are some educated guesses: — Meta has made towards letting users provide inputs to the feed algorithm. I would expect the creation of more explicit preferences/interests that users explicitly select. While it has worked to a smaller extent for some products like , it is likely that the quality of content provided by just preferences will be nowhere close to the relevance of a system that is constantly ingesting data and inferring interests. Explicit preferences instead of inferred interests some progress Reddit : TikTok has announced that they will create a that is based on what’s popular in your region. Most social feed-based platforms will also (re)introduce simplistic ranking options like chronological. I am not bullish that any of these will create an engaging user experience. Non-personalized feeds non-personalized feed : My (potentially contentious) take is that opted-out users will see a significantly degraded experience and eventually decide to provide platforms consent to personalize. The platforms will likely use this mechanism to receive content for all personalization (including behavioral advertising) — It’s unclear if there are any regulatory constraints that force them not to bundle consent for user experience personalization and behavioral advertising. I don’t expect the consent rate to go anywhere close to the status quo, but I think it will be a meaningful bump to what’s seen with Apple’s opt-in rates. Degraded user experience, leading to more opt-in : Twitter launched Twitter Verified a few months back, and Meta is with a similar concept. I am not bullish that a majority (or even a minority) of users will be willing to pay for social media products after years of being conditioned to get these products for free. Experiments with subscriptions experimenting If platforms are not able to effectively monetize the EU market, it is likely that they will start de-prioritizing investments for the EU market, which is a real risk of an aggressive regulatory regime. I don’t expect them to stop supporting EU markets, but every new feature built is an investment, and companies will start heavily questioning if they are willing to do any extra work for this market. For example, if there’s additional work to make a feature comply with GDPR, that might not be worth it anymore. We have seen other equivalent situations of this — several apps have a much better experience on iOS than Android because iOS apps monetize better. De-prioritization of EU markets: Conclusion The advertising market exploded over the last several decades due to the availability of essentially infinite user data, which brought both large efficiencies in advertising and major privacy risks. Regulation was necessary. Great regulation is about finding the balance between promoting innovation/letting new businesses emerge that move society forward and having guardrails so people are protected. Did the EU go too far? I personally think they did, and if that is true, the effects will start showing in a 5–10 year horizon, and course correction will follow. Until then, this is the new reality of behavioral advertising in the EU. The regulators and legislators came all guns blazing, and they won. 🚀 If you liked this piece, consider subscribing to Every week, I publish one in the form of a 10-minute read. Best, Viggy. . my weekly newsletter deep-dive analysis on a current tech topic/product strategy Also published . here

The New Reality of Behavioral Advertising in the EU

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