Monthly Archives: September 2024

What is Product Management, anyway?

Defining Product Management as a discipline can be hard, here’s everything you ever needed to know!

by Eric Picard

Product Management resides within the “builder” organization, the team responsible for creating products, services, or media offerings to be sold to customers. These offerings are typically developed by engineers, powered by scientists, and designed by product designers. The entire process is orchestrated by Product Managers, who are ultimately accountable for ensuring products are properly defined, developed efficiently, launched effectively, and operate profitably.

The Spectrum of Product Management Work

Product Management spans a wide range, covering everything from what a sales team sells to what an engineering team builds. The work in between encompasses various roles, and in different companies, these roles may be divided differently. Below is a breakdown of the interrelated disciplines, illustrating why Product Management is challenging to define and execute. Note that while each discipline is described separately, Product Managers in many companies may be responsible for multiple (or even all) of these areas in their day-to-day work.

If we move from sales towards engineering, the breakdown sort of looks like this (note that the path isn’t truly linear, so it’s just a loose framework.)

1. Product Marketing / Go-to-Market

Product Marketing involves preparing products for market consumption, ensuring they are presented in a consumable form that sales and marketing teams can utilize to reach customers. Product Marketing is a distinct discipline closely aligned with the builder organizations, although sometimes it reports into Marketing. Product Marketing is not Marketing Communications, and is much more closely aligned to Product Management. Responsibilities include: Developing compelling product positioning that clearly defines the product’s value proposition. Crafting messaging that highlights key differentiators and resonates with target customers. Managing the end-to-end launch process, including internal training, external communications, and post-launch analysis. Sales enablement functions such as creating sales materials, presentations, and demos. A critical deliverable is the Product Positioning document, which includes messages or claims about the product that can be used broadly across the organization and easily transformed into marketing or sales materials. For example, a product positioning statement might be, “Renovate your home with confidence using our easy-to-use device that scans walls for wiring before you begin a project.”

2. Product Strategy / Product Planning

This discipline focuses on determining what to build, requiring extensive research to understand market needs. It includes competitive analysis to understand the market landscape, customer research to gather insights from current, potential, and former customers, and market requirements analysis. The long-term vision, ideally spanning three years, often resides here. A key deliverable is the Market Requirements Document, distinct from Product Requirements Documents, which emphasizes customer needs without detailing implementation specifics. For instance, a market requirement could be, “Customers need a way to find wires inside walls to avoid accidentally cutting them during renovations.”

3. Product Operations

Product Operations might fall under the Product Manager’s purview or exist as a separate discipline, especially in larger companies. This role manages the business of existing products in the market, ensuring product health, profitability and alignment across teams. It involves defining and measuring metrics to ensure product success, optimizing to increase profitability, and collaborating across disciplines to maintain clear communication and alignment. Product Operations is often responsible for pricing, performance measurement, and ensuring products meet KPIs and business goals. Product Operations Specialists work closely with Product Management, Engineering, Marketing, Sales, and Customer Support to align all aspects of the product effectively. A primary task is defining and implementing product dashboards to measure performance against KPIs.

4. Technical Product Management

Often considered the “core” of Product Management, Technical Product Management is responsible for creating and managing a product roadmap. This includes developing a Product Requirements Document that spans multiple releases, breaking it into individual features, and writing specifications. It involves collaborating with Product Design for user-facing features to develop prototypes, design systems, and screen layouts. Technical Product Managers work closely with Engineering to ensure requirements are clear, fostering debate and improvement within a healthy organization. They also collaborate with Sales and other customer-facing teams to ensure feature prioritization aligns with business needs and work with Marketing to ensure demand creation. They often manage daily coordination efforts like standups or scrums, or other mechanisms for coordinating the day-to-day work related to building and releasing software (unless a Technical Project Manager is assigned to the project, in which case they do those things). A primary deliverable of a Technical Product Manager is the Product Requirements Document, which details necessary features to solve customer problems while avoiding implementation specifics, which are handled by Engineering. Another important deliverable is the Product Roadmap, which can take a variety of forms. Ultimately PMs are responsible for defining the product and getting the right product built, and writing requirements is a core function. A good high level requirement could be, “Create a small handheld device that clearly alerts a customer that active or inactive electrical wires are inside of a given wall.”

5. Technical Project Management

Technical Project Management focuses on coordinating engineering and other resources across a complex organization with varying timelines and levels of effort. The purpose is to keep projects on track and ensure complete transparency and coordination across teams. A key responsibility is maintaining a unified Gantt Chart, detailing team deliverables and timelines. Unlike other roles, Project Managers aren’t tasked with deciding what to build or communicating product details; they ensure that all teams understand their responsibilities, deadlines, and resource allocation, optimizing the efficiency of the overall process.

6. Product Design

Product Designers, sometimes called User Experience Designers, have a unique discipline within the world of design. Very often Product Designers are misunderstood by people outside of their day-to-day working chain as Graphic Designers. While Product Designers do sometimes create work that feels a bit like Graphic Design, their work is fundamentally different. The discipline ranges broadly from Research to Usability. User Experience Research includes deliverables like User Journeys, User Personas, Competitive Analysis Reports (from a design perspective), and Usability Research. They own the look and feel of the product by implementing design system that define how all aspects of the product look, and what the user interaction model of the product will be. This includes colors, fonts, and various elements of the styling of the product (buttons look like this, form fields look like that, there should be x many pixels buffer around images, etc…). Execution oriented deliverables include Information Architectures, proof of concept Wireframes, Pixel-Perfect Wireframes, and clickable prototypes. Product Designers, especially those building digital products or software, establish a design system for all aspects of the user experience. Product Designers are also responsible for usability testing and talking to customers about the products that they use to understand what works, and what doesn’t work. They sometimes do this with quantitative methods, and sometimes qualitative methods. Often they will also use automated tools that provide heatmaps and click-tracking analysis to show what users are looking at on the page, and what paths they are taking through the product or site.

A Complex Discipline

As you can see, the discipline of Product Management is complex and comprehensive. The work extends across a lot of discrete skills, and requires many different capabilities. Finding a product person who excels in all of these various disciplines is hard, we usually refer to those people as “Unicorns.” At early stage startups, the product managers are generally responsible for several, if not all, of these disciplines. As companies mature, the work breaks down into specific roles and responsibilities. When I was at Microsoft each of these were very clearly defined and separate roles, albeit sometimes with different names. Unicorns do exist, you can find people with the ability to effectively do all of this work, and to do it well. But they are extremely rare. My advice to most companies is to figure out what your teams do well, and what they need help with. Then find a logical breakpoint between the disciplines and establish the work in a way that everyone involved celebrates, rather than feeling like the work that they are good at and enjoy is being ‘taken away’ from them.

To show you how these teams interact, see the chart below. We’re looking at the product development life cycle from Left to Right, and the interaction between Product Marketing, Product Strategy and Product Management. You can see that Product Strategy starts the process off, and then hands off to Product Management. As the life cycle progresses, different roles are more or less engaged.

This is a product development life cycle map from a very mature company with a global footprint. Smaller companies will have less complexity in their map, and frankly, the roles will condense down to one or two rather than all three.

This chart doesn’t include software developers, scientists, researchers, technical operations, release management, product designers, or product operations. You can imagine how complex things might get in a fully resourced organization – and why a team might need a Technical Project Manager to coordinate it all.

I hope this gives you some sense of what Product Management as a discipline is all about. It takes a lot of work to build a product. Whether you’re a 3 person team or a 300,000 person organization, the work is the work, is the work. It all needs to be done in order to deliver a great product. It’s just a question of how large the offering is, and how streamlined of a process the team can use in order to deliver the product in a reasonable amount of time.

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How AI is actively transforming advertising

by Eric Picard

Artificial Intelligence (AI) and its subset, Machine Learning (ML), have been integral to the advertising industry for decades. These technologies have transformed how businesses connect with consumers, optimizing many of the dollars spent and ensuring targeted engagement. But as AI evolves, particularly with the advent of Large Language Models (LLMs) and generative AI, we’re entering a new era where smart automation is a reality. Let’s explore how AI, ML, and LLMs are reshaping advertising, untangling the complexities of these technologies, and understanding their distinct roles and applications.

To begin with, it’s important to differentiate AI and ML. AI is the broader concept of machines performing tasks that typically require human intelligence, such as decision-making and pattern recognition.

Machine Learning (ML) is a subset of AI, and has been the engine that powers many of the smart decisions in today’s advertising landscape. At its core, ML is about teaching computers to learn from data, much like how we learn from experience. There are several approaches to ML, each with their own unique applications and strengths in advertising.

One of the most prevalent techniques is supervised learning. This approach involves training an ML model on a dataset that includes both inputs and the correct outputs—sort of like coaching a sports team with the playbook in hand. In advertising, supervised learning is often used for predictive targeting. By analyzing historical data, these models can forecast which segments of an audience are most likely to respond to a specific ad campaign. This allows advertisers to allocate resources more effectively and maximize return on investment.

Unsupervised learning, on the other hand, is a bit like sending a detective into a room full of clues without any instructions. The model explores the data, finding patterns and relationships on its own. This technique is ideal for audience segmentation, helping advertisers discover new and potentially valuable consumer groups based on shared behaviors or characteristics. It’s akin to discovering hidden subcultures within a larger community, providing insights that can drive more personalized marketing strategies.

Reinforcement learning is another fascinating ML approach, where models learn by trial and error—similar to training a pet with rewards and consequences. In the dynamic world of real-time bidding, reinforcement learning algorithms adjust bidding strategies on the fly, learning which actions yield the best results. This adaptability is crucial in environments where market conditions and consumer behavior can change rapidly.

It’s also worth mentioning neural networks, a type of ML model inspired by the human brain’s structure and function. These networks are particularly powerful in tasks involving complex pattern recognition, such as image and speech recognition. In advertising, neural networks can enhance programmatic buying by evaluating vast datasets to identify subtle patterns in consumer behavior, enabling more precise targeting and personalization.

While these examples illustrate some of the common ML techniques used in advertising, the field is vast and continually evolving. Each method brings its own set of tools to the table, contributing to a more nuanced and sophisticated advertising ecosystem. As ML technology advances, its role in crafting more targeted, efficient, and engaging advertising experiences will only grow, pushing the boundaries of what’s possible in the digital marketing space.

Now, let’s address the role of LLMs in advertising. LLMs, such as GPT-4o, are advanced AI models that excel in understanding and generating human-like text. They are not primarily designed for data analysis or real-time decision-making—which are traditional ML use cases—but rather excel in tasks that involve language processing and text-based interactions. LLMs are being leveraged to automate processes that require a nuanced understanding of language and context, such as drafting personalized ad copy, facilitating customer service interactions, and enhancing chatbots.

In media buying and selling, LLMs are being applied to automate complex processes that traditionally required human intervention. By programming LLMs to think with a specific viewpoint and set of instructions, they can streamline tasks like scheduling and orchestrating campaigns, crafting and refining ad messages, and even generating comprehensive reports. These models act more as strategic partners, assisting human teams in managing and executing processes efficiently.

The application of ML in advertising remains robust, focusing on data-driven decision-making. ML algorithms excel in predictive targeting, analyzing vast datasets to identify optimal audiences, and optimizing bids in real-time to maximize ad spend efficiency. These capabilities are essential for real-time bidding environments and dynamic pricing models, where decisions must be made swiftly and accurately based on ever-changing data inputs.

Generative AI, closely related to LLMs but with distinct applications, is making significant impacts in creative advertising. While LLMs are adept at processing language, generative AI models are designed to create new content—be it text, images, or even video. In advertising, generative AI can automate the creation of ad visuals or video content, generating variations tailored to different audience segments. This capability not only enhances creativity but also accelerates the production process, allowing for rapid experimentation and iteration.

The distinction between generative AI and LLMs is important. While both can be used in the creative process, LLMs focus on language and dialogue, whereas generative AI extends to producing varied forms of media content. Together, they offer a comprehensive toolkit for advertisers looking to innovate and engage audiences more effectively.

As AI continues to advance, ethical considerations around data privacy and algorithmic bias abound. Transparency in how AI systems operate and make decisions is essential to maintain consumer trust. Balancing automation with human creativity and insight is also crucial. While AI can handle data-driven processes, human expertise is irreplaceable in crafting compelling narratives and understanding the subtleties of consumer emotions.

Looking ahead, the fusion of AI technologies promises even more sophisticated advertising solutions. We can anticipate AI models that predict consumer needs with remarkable precision, integrating seamlessly into the consumer journey. This future of advertising is not just about efficiency—it’s about creating meaningful, anticipatory experiences that resonate with consumers on a personal level.

By automating routine tasks and augmenting human capabilities, AI is enabling advertisers to deliver more personalized, effective, and efficient campaigns. The key to success will be embracing AI as a partner, leveraging its strengths while preserving the creativity and empathy that only humans can provide.

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Programmatic Ads in 2024

A privacy-centric new world

By Eric Picard

I’ve been writing about advertising technology and digital advertising since 1999. Every five to seven years we go through a new wave of transformation. The current wave is the Privacy wave, and it’s transforming the way that the infrastructure of the ad technology space functions. Previously we were reliant on digital identifiers, generally enabled by cookies on the web, and AdIDs in the mobile app space, but today these mechanisms have been deprecated or are in the process of being deprecated. This has decimated a huge swath of the industry that relied on these IDs to make all sorts of decisions, including linking together multiple tracking methodologies – to power 3rd party data. That industry has shrunk significantly, since users are becoming impossible to track that way.

This transformation is largely driven by increasing consumer demands for privacy and stringent regulatory requirements. Today, the focus has shifted towards privacy-centric methodologies, with first-party data taking center stage. In this comprehensive overview, we’ll explore the current state of programmatic advertising, delve into the innovative strategies employed to maintain effectiveness while upholding privacy, and highlight the strategic implications for advertisers.

Understanding Programmatic Advertising Today

At its core, programmatic advertising is the automated buying and selling of online ad space, allowing brands to target specific audiences at scale. This process involves real-time bidding (RTB), where ad inventory is bought and sold on a per-impression basis in a few hundred milliseconds. Programmatic platforms use sophisticated algorithms to analyze vast amounts of data, enabling advertisers to reach their desired audiences with precision.

Without cookies and AdIDs, the whole industry is in the process of retooling.

The Role of First-Party Data

First-party data refers to information collected directly from consumers either through brand-owned channels such as websites, apps, and loyalty programs on the advertiser side, or through the publisher’s relationship with the consumer. This data is gathered with explicit user consent, making it both reliable and compliant with privacy regulations like the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA).

The importance of first-party data cannot be overstated. It provides a comprehensive view of consumer behavior, preferences, and demographics, enabling advertisers to create personalized and relevant ad experiences. By leveraging first-party data, brands and publishers can build direct relationships with their customers. This data can be used in many valuable ways, but importantly can be used to deliver the correct advertising to consumers, and to measure effectiveness of advertising.

Privacy-Centric Targeting Methodologies

In response to the growing emphasis on privacy, advertisers have adopted several innovative targeting methodologies that prioritize user rights while maintaining effectiveness:

Contextual Targeting: In a world where user data is increasingly protected, contextual targeting offers a privacy-friendly alternative. This method involves placing ads based on the content of the web page rather than individual user data. For example, an ad for hiking gear might appear on an article about nature trails. By focusing on the context rather than the person, advertisers can maintain relevance without compromising privacy. Contextual has been around forever, but new approaches to contextual are being used in much more sophisticated ways. What’s old is new again.

Cohort-Based Targeting: Google’s Privacy Sandbox initiative popularized the concept of privacy-first approaches to targeting users, their first broadly discussed version was called Federated Learning of Cohorts (FLoC), which groups users into cohorts based on similar browsing patterns. Google has reiterated and revised their approach several times, and hasn’t really gotten traction. But that’s mostly because they’re Google, and the industry isn’t sure they trust the approach they have come up with. While Google’s approach isn’t gaining traction, the overall approach of building privacy-safe cohorts of users for anonymous targeting is sound, and may well get cracked at the industry level sooner or later. This approach allows advertisers to target clusters of users with shared interests, removing the need for individual tracking.

Identity Solutions: With the decline of third-party cookies, identity solutions have emerged as a viable alternative. These solutions use hashed identifiers like email addresses or phone numbers to create a persistent identity across different platforms. The success of these solutions depends on user consent and robust data protection measures, offering a way to recognize users while respecting their privacy. While these approaches work technically, getting to scaled anonymous identity is a real challenge that still is being overcome.

Data Clean Rooms: These secure environments allow brands and publishers to collaborate and combine their first-party data without exposing user identities. Data clean rooms enable advertisers to gain insights and enhance targeting precision while adhering to privacy regulations. By facilitating data collaboration in a controlled setting, clean rooms offer a solution to the challenges posed by privacy laws. Note that while this approach overcomes the legal issues, it’s still not quite clear that consumers will be accepting of the approach as it becomes normalized and written about in the press.

Beyond Basic Targeting: Curated Audiences and Inventory

Publishers lost a lot of ground in the first wave of programmatic advertising, which pushed all the power to the media buyer. This created a long period of data asymmetry where publishers didn’t know why an advertiser was buying any given impression – and the knowledge of their own audience was ignored by the buyer. Things have changed with the loss of 3rd party data, cookies and IDs.

The industry has responded by rallying around the sell-side of the market for the first time in many years. The outcome is what is being called Curated Audiences and Curated Inventory. Effectively the sell-side of the market has access to an immense amount of information on their side of the fence, about the consumers visiting their sites, and about the behavior of those consumers. This all is Publisher First Party Data, and is able to be blended with other data sources by the Publisher to create large pools of inventory that are curated to the needs of the buyer. Vendors and the publishers themselves have found ways to build high scale and highly effective targeting and optimization technologies based on these approaches, and package the inventory into programmatic deals (PMPs):

Curated Audiences: Publishers can create pre-defined audience segments based on aggregated data insights and consumer behaviors. These segments are crafted using a combination of consented user data (the consumer said the publisher can use it) and non-personal data, ensuring compliance with privacy regulations. By curating audiences, advertisers can buy advertising at scale that allows them to reach relevant consumers without relying on intrusive data collection methods.

Curated Inventory: Publishers can package their ad inventory using their own data and intelligence, often with the support of sophisticated external data providers. This approach allows advertisers to target ads to appropriate audiences without using personal data. For instance, sophisticated geo-targeting can be employed to deliver regionally relevant ads, enhancing user engagement without compromising privacy. Some vendors use decades old approaches in new ways, using census and other forms of data that allow demographic, psychographic, and other types of targeting by overlaying the location of the user when they receive the ad against known information about that location. For instance, assuming someone on a golf course at 3PM is likely a golfer.

These curated solutions provide advertisers with powerful tools to reach their desired audiences while navigating the complexities of privacy regulations.

Strategic Implications for Advertisers

The shift towards privacy-centric methodologies is not just a technical adjustment; it’s a strategic imperative. Advertisers must align their programmatic strategies with these approaches to maintain consumer trust and comply with legal requirements. This involves investing in data infrastructure, nurturing direct customer relationships, and staying informed about regulatory changes.

Moreover, as privacy becomes a selling point, brands that demonstrate a commitment to safeguarding user data can differentiate themselves in a crowded marketplace. Transparent data practices and clear communication about how consumer data is used can build loyalty and encourage engagement.

Overcoming Challenges in the Programmatic Space

While the transition to privacy-centric advertising offers numerous benefits, it also presents challenges. Advertisers must adapt to new technologies and methodologies, requiring investment in training and infrastructure. Additionally, the loss of third-party cookies necessitates a reevaluation of measurement and attribution models, as traditional methods may no longer apply.

To overcome these challenges, collaboration within the industry is crucial. Brands, agencies, publishers and technology providers must work together to develop standards and best practices that prioritize privacy while delivering effective results. By fostering an ecosystem of transparency and cooperation, the industry can navigate the complexities of programmatic advertising in 2024 and beyond. The IAB Tech Lab is a great example of an industry organization working to drive this kind of collaborative adoption and roll out.

The Future of Programmatic Advertising

As we look to the future, programmatic advertising will continue to evolve in response to technological advancements and changing consumer expectations. The integration of artificial intelligence (AI) and machine learning will enhance targeting capabilities, allowing for even greater personalization while respecting privacy. Additionally, the rise of connected devices and the Internet of Things (IoT) will present new opportunities for advertisers to engage with consumers in innovative ways.

Ultimately, the key to success in programmatic advertising lies in embracing privacy as a core value. By doing so, advertisers not only comply with regulations but also foster trust and loyalty among their audiences. In 2024 and beyond, the challenge will be to innovate continuously while keeping consumer privacy at the forefront of every decision. As the landscape continues to evolve, staying informed and adaptable will be essential for advertisers seeking to thrive in this dynamic environment.

The current state of programmatic advertising is characterized by a delicate balance between effective targeting and stringent privacy requirements. First-party data has emerged as the linchpin of modern advertising strategies, offering a path to personalization that respects user privacy. By adopting innovative targeting methodologies and maintaining a strong commitment to privacy, advertisers can successfully navigate the programmatic landscape, building lasting relationships with their audiences in the process.

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