Summary

In this conversation, Sean and Jason Gillespie discuss their experiences working together at Criteo and the impact Jason had on Sean's understanding of retargeting. They also delve into the concept of deep learning and neural networks, and how RTB House, a direct competitor of Criteo, is using these technologies in retargeting. They explore the future of retargeting in a cookie-less world and the role of Google's Privacy Sandbox in maintaining privacy while still enabling personalized advertising. The conversation concludes with a discussion on how different ad tech companies can leverage the Privacy Sandbox to make business and creative decisions without tracking users across the internet. In this conversation, Jaysen Gillespie and Sean discuss the impact of Google's Privacy Sandbox on the advertising industry. They cover topics such as the deprecation of third-party cookies, the role of AI in advertising, the importance of first-party data, and the future of targeted advertising. Jaysen explains how RTB House is adapting to these changes and offers advice for performance marketers.

Takeaways

• Jason Gillespie had a significant impact on Sean's understanding of retargeting during their time working together at Criteo.

• RTB House, a direct competitor of Criteo, uses deep learning and neural networks in their retargeting approach.

• The future of retargeting lies in a cookie-less world, and Google's Privacy Sandbox is a solution that maintains privacy while enabling personalized advertising.

• Different ad tech companies can leverage the Privacy Sandbox to make business and creative decisions without tracking users across the internet. Google's Privacy Sandbox and the deprecation of third-party cookies are major changes that will impact the advertising industry.

• AI-driven solutions like RTB House's Content GPT can help advertisers target audiences more accurately and effectively.

• First-party data will become increasingly important for advertisers as third-party cookies are phased out.

• Publishers will need to collect more first-party data from users in order to deliver relevant ads and monetize their content.

• Marketers should audit their marketing vendors and prioritize partnerships with vendors that are prepared for a cookie-less future.

• Alternative IDs and advanced contextual targeting are potential solutions for advertisers navigating the post-cookie landscape.

Chapters

00:00

The Impact of Jason Gillespie on Sean's Understanding of Retargeting

03:20

The Future of Retargeting in a Cookie-less World: Google's Privacy Sandbox

09:02

Leveraging the Privacy Sandbox: Making Business and Creative Decisions

30:12

AI in Advertising: The Role of RTB House's Content GPT

33:54

The Importance of First-Party Data

37:17

Monetizing Content: The Rise of the Free Wall

46:18

Navigating the Post-Cookie Landscape

Summary

Cohley is a software platform that helps consumer brands generate content, including photos, videos, and product reviews. It focuses on creating content that fosters connection and trust between brands and consumers throughout the consumer journey. Cohley facilitates the relationship between brands and creators, allowing brands to tap into a network of over 100,000 creators. The platform also integrates with various channels and provides tools for brands to communicate directly with creators and approve assets. Cohley's business model involves a base license fee for using the platform and incremental spend directly to the creator. The platform secures content rights in perpetuity for brands, addressing a pain point in the market. UGC and influencer marketing are related but distinct concepts. UGC refers to content created by ordinary people, while influencer marketing involves leveraging the influence of individuals with large followings. The shift towards UGC and influencer marketing is driven by the need for scalable, high-quality content that resonates with consumers and builds trust. In this conversation, Tom and Sean discuss the value of user-generated content (UGC) in marketing and how Cohley, Tom's company, helps brands leverage UGC to drive engagement and conversions. They explore the different channels where brands can use UGC, such as social media, e-commerce websites, and even TV ads. They also discuss the importance of quality UGC and how it can provide information, social proof, and confidence to consumers. Tom shares how Cohley helps brands measure success by aligning content with strategic initiatives and key metrics. The conversation concludes with a discussion on the future of Cohley, including partnerships with platforms like Amazon and the use of AI to optimize content creation and decision-making.

Takeaways

Cohley is a software platform that helps consumer brands generate content, including photos, videos, and product reviews.

The platform focuses on creating content that fosters connection and trust between brands and consumers throughout the consumer journey.

Cohley facilitates the relationship between brands and creators, allowing brands to tap into a network of over 100,000 creators.

The platform integrates with various channels and provides tools for brands to communicate directly with creators and approve assets.

Cohley's business model involves a base license fee for using the platform and incremental spend directly to the creator.

UGC and influencer marketing are related but distinct concepts, with UGC referring to content created by ordinary people and influencer marketing involving leveraging the influence of individuals with large followings.

The shift towards UGC and influencer marketing is driven by the need for scalable, high-quality content that resonates with consumers and builds trust. User-generated content (UGC) is a valuable tool for brands to drive engagement and conversions.

UGC can be used on various channels, including social media, e-commerce websites, and TV ads.

Quality UGC provides information, social proof, and confidence to consumers.

Brands can measure success by aligning content with strategic initiatives and key metrics.

The future of Cohley includes partnerships with platforms like Amazon and the use of AI to optimize content creation and decision-making.

Chapters

00:00

Introduction to Cohley

02:25

Facilitating the Relationship Between Brands and Creators

08:40

Cohley's Business Model

13:53

Understanding UGC and Influencer Marketing

26:30

The Power of User-Generated Content

29:09

Expanding the Reach of UGC

31:02

The Future of TV Ads: UGC vs. Hollywood Commercials

33:04

The Importance of Quality UGC

34:54

The Influence of Real People in UGC

37:07

The Impact of UGC Reviews

41:02

The Historical Context of Influencer Marketing

Greg Stellato discusses the vision and objectives of M3, a brand advertising company focused on helping specialty retailers generate additional revenue through brand advertising and sponsorship opportunities with Fortune 1000 advertisers. The conversation delves into the challenges faced by retailers, the evolution of brand advertising, the differences between retail media and commerce media, and the impact of programmatic advertising on brand safety and consumer experience. The conversation delves into the future of advertising, the role of data in targeting, and the impact of ad placements on consumer behavior. It also explores the challenges and opportunities for specialty retailers in leveraging advertising and sponsorships to generate revenue. The discussion covers the use of email and on-site ad placements, the importance of context in advertising, and the long-term vision for the company.

The conversation covers the acquisition of Intellimize by Webflow, the unique features of Intellimize's A-B testing and personalization products, the challenges of integrating acquisitions, and the founding story of Intellimize. It also delves into the application of machine learning and AI in personalization, the impact of personalization on user experience, and the potential for personalized experiences on e-commerce websites. The conversation delves into the application of AI and machine learning in website optimization, highlighting the benefits of real-time personalization and the challenges faced by brands in traditional A-B testing. It also explores the objections and mindset shifts required for adopting AI-driven optimization, along with the success metrics and common pitfalls. The use case of Dermalogica's success with Intellimize is presented, emphasizing the significant impact on revenue and customer experience.

Takeaways

• The strategic fit between Webflow and Intellimize is expected to bring great new use cases and delight customers.
• Intellimize's A-B testing simplifies the process for marketers to visually drag and drop and move things around on their website to test ideas.
• Personalization at Intellimize involves using rules and machine learning to deliver personalized experiences down to the individual level.
• The application of machine learning and AI in personalization aims to optimize page content for the most efficient or highest volume conversions.
• The conversation highlights the potential for e-commerce brands to personalize every user's experience on every page of the website without a lot of effort.
• The founding story of Intellimize is rooted in the need for personalization and optimization on websites, driven by the experience of the company's founder as a marketer.
• The impact of personalization on user experience is discussed, emphasizing the importance of showing the right content to each person at the right time. AI and machine learning enable real-time personalization and optimization of website content, leading to improved user experience and increased conversions.
• Traditional A-B testing methods often result in delayed decision-making and are unable to adapt to rapidly changing user behaviors and market dynamics.
• The objections to AI-driven optimization often stem from a lack of understanding and mindset shifts required within the organization, but the benefits include increased efficiency, improved ROAS, and the ability to do more with less.
• Success in AI-driven optimization is measured not only by revenue and conversions but also by customer satisfaction and brand advocacy, highlighting the holistic impact on the business.
• Common pitfalls in AI-driven optimization include the failure to communicate the reality of idea success rates and the lack of alignment within the organization on testing strategies and brand guidelines.
• The use case of Dermalogica demonstrates the substantial impact of AI-driven optimization on revenue growth, subscription conversions, and user experience, showcasing the tangible benefits for brands.

Chapters

00:00 The Acquisition of Intellimize by Webflow

02:38 Simplifying A-B Testing with Intellimize

05:48 Delivering Personalized Experiences with Intellimize

15:14 The Founding Story of Intellimize

25:54 The Potential for Personalized Experiences on E-commerce Websites

26:29 The Power of AI and Machine Learning in Real-Time Website Optimization

29:21 Challenges and Mindset Shifts in Traditional A-B Testing

30:46 Overcoming Objections and Embracing AI-Driven Optimization

33:29 Measuring Success Beyond Revenue: The Holistic Impact

48:53 Common Pitfalls and Best Practices in AI-Driven Optimization

50:03 Dermalogica: A Case Study in Revenue Impact and Customer Experience

SummaryConsumer AI, offered by Profit Wheel, helps brands gain insights about their customers and target high-value audiences. The platform uses AI and cohort-level data to analyze customer behavior and interests, allowing brands to understand their customers better and find new potential customers. By uncovering common themes and interests among customer cohorts, brands can optimize their advertising and marketing strategies. Consumer AI offers various services, including audience identification, strategic partnerships, content creation, and market research. The platform integrates with different media accounts and provides seamless execution of campaigns. Brands can measure the success of Consumer AI through A/B testing and comparing the performance of their campaigns with and without the platform's insights. Getting started with Consumer AI is easy, requiring brands to grant access to their advertising accounts and undergo a demo of their data.

Takeaways

• Consumer AI helps brands gain insights about their customers and target high-value audiences.
• The platform uses AI and cohort-level data to analyze customer behavior and interests.
• Consumer AI offers various services, including audience identification, strategic partnerships, content creation, and market research.
• The platform integrates with different media accounts and provides seamless execution of campaigns.
• Brands can measure the success of Consumer AI through A/B testing and comparing campaign performance.
• Getting started with Consumer AI is easy, requiring brands to grant access to their advertising accounts and undergo a demo of their data.

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Chapters

00:49: Introduction to Consumer AI and Profit Wheel

01:34 Consumer Discussion

01:36 Overview of Consumer AI and Its Founding Purpose

03:08 Bridging Adtech and Martech for Better Advertising

03:38 Leveraging Customer Data for Enhanced Marketing Strategies

20:13 Mapping Cohorts and Interests Across Platforms

21:52 Challenges in the Advertising Industry

22:35 Future Proofing Our Product

22:56 Platform Integration and Campaign Support Across Multiple Channels

23:18 Consumer AI Platform Implementation and Effectiveness

In the ever-evolving world of marketing and e-commerce, staying ahead of the competition means leveraging data-driven insights. This week, we're excited to share an enlightening conversation between Sean and Ori Greenberg, CEO of AlgoPix and Cluster, which delves into the critical role of data in driving performance and making informed business decisions.

Takeaways

1. AlgoPix and Cluster provide valuable data insights to brands, retailers, and marketplaces in the e-commerce space.
2. The data is obtained through partnerships with online sellers who grant access to their sales data, catalog data, and inventory level data.
3. The data is aggregated, augmented, and anonymized to provide information on sales volume, pricing, market share, and more.
4. AlgoPix is ideal for small brands and offers a visual interface, while Cluster is an API-first product designed for larger companies with data lakes and analysis capabilities.
5. The goal of both companies is to help online merchants make the right sourcing decisions and be more successful.

6. 
   

   Chapters

   00:00: Uncovering Business Problems with Tech Solutions
   00:26: Entering the Matrix
   01:22: Insights into Global Product Sales and Data Acquisition Strategies
   06:44: Discussion on Data Co-op and E-commerce Analytics
   13:08: Analyzing Market Dynamics and Data Utilization in Business
   27:03: Understanding Data Clusters and Market Strategies
   28:20: Challenges in Sales and Pricing Strategy
   29:13: Investment and Margins in Brand Strategy
   29:26: Insights on E-commerce and Data Utilization
   32:36: Future Roadmap and Vision for AI and Data Analytics in Business
   37:42: Avoiding Common Data Analysis Mistakes
   38:42: Insights on Market Adaptation and Real-Time Data Utilization

Summary

Tracer is a data intelligence platform that simplifies data analysis for businesses. It aggregates data from various sources, including marketing platforms, point of sale systems, and Salesforce, and normalizes it for easy comparison and analysis. Tracer's AI capabilities enable it to provide real-time insights and analysis, helping businesses make data-driven decisions. The platform is used by both marketing teams and non-marketing users, such as ERP providers, to centralize and understand their data. Tracer complements measurement and attribution vendors by providing structured data for their models. The platform has helped agencies and brands overcome challenges in data management and reporting, allowing them to deliver better results to clients. Tracer's ability to aggregate and analyze data from multiple campaigns and platforms simplifies the analytics process for agencies, saving time and resources. Tracer helps brands and agencies gain control over their marketing data and access real-time insights. By centralizing data and providing a user-friendly platform, Tracer enables faster decision-making and optimization. The platform allows brands to transition smoothly between agencies and maintain control over their data, eliminating the need for lengthy transitions and data handoffs. Tracer can ingest historical data as far back as needed, and its onboarding team helps clients define their goals and design their Tracer experience. The platform also provides benchmarking reports and insights into platform performance and trends.Keywordsdata intelligence, data analysis, marketing platforms, data normalization, AI, real-time insights, measurement and attribution, data management, reporting, agencies, brands, Tracer, marketing data, insights, centralization, agencies, brands, data control, onboarding, benchmarking, platform performance, trends

Takeaways

• Tracer simplifies data analysis by aggregating and normalizing data from various sources.
• The platform provides real-time insights and analysis, enabling businesses to make data-driven decisions.
• Tracer complements measurement and attribution vendors by providing structured data for their models.
• The platform helps agencies and brands overcome challenges in data management and reporting.
• Tracer's ability to aggregate and analyze data from multiple campaigns and platforms saves time and resources for agencies. Tracer helps brands and agencies gain control over their marketing data and access real-time insights.
• The platform enables faster decision-making and optimization by centralizing data and providing a user-friendly interface.
• Tracer eliminates the need for lengthy transitions and data handoffs when brands switch agencies.
• The platform can ingest historical data as far back as needed and provides benchmarking reports and insights into platform performance and trends.

Chapters

00:00 Introduction and Overview of Tracer

03:47 The Business Problem Tracer Solves

08:11 Tracer's Role in AI and Structured Data

10:27 Comparison to Measurement and Attribution Vendors

11:32 Challenges of Growing Tech Stacks for Brands

15:37 Use Cases for Agencies and Brands

21:38 Centralizing Data and Gaining Control

22:33 Seamless Transitions Between Agencies

26:04 Harnessing Historical Data for Insights

28:56 Tracking Platform Performance and Trends

29:50 Insights on TikTok Advertising

36:30 Getting Started with Tracer

38:30 Avoiding Pitfalls and Defining Goals

This discussion highlights the challenges digital marketers face with the decreasing attention spans of consumers, particularly Gen Z, and explores the necessity of leveraging new technologies to effectively engage within this limited timeframe.

Timestamp Notes & Chapters

00:00: Unveiling the Future of Ecommerce with nacelle

03:16: The Impact of Decreasing Attention Spans on Digital Marketing and Technology Evolution

07:21: Exploring the Effectiveness of Ecommerce Strategies

08:22: E-commerce Challenges and the Importance of Technology Adaptation

14:46: Understanding Headless Technology and Its Impact on Site Speed

18:41: Discussing the Impact of Page Load Speed on Conversion Rates

21:58: Understanding Bounce Rates and Optimizing E-commerce Performance

31:03: Discussion on Headless Solutions and Edge Computing for Internationalization

34:12: Challenges of Prematurely Adopting Headless Technology

34:33: Discussing Business Iteration Speed and Headless Commerce Challenges

34:57: Navigating Product Market Fit and Optimization in E-commerce

46:12: Discussion Conclusion and Future Plans