Total Addressable Market (TAM)

What it is: At its core, Total Addressable Market (TAM) represents the total market demand, all potential revenue, for a product or service if 100% market share were achieved. Imagine a world where your solution is the only one available, and everyone who could possibly use it, does. That is your TAM.

Why it matters: It is the grand vision, the ultimate revenue ceiling for your offering. While it is a theoretical maximum, TAM is a key indicator for investors. They use it to gauge the sheer scale of the opportunity and ensure there is enough room for exponential growth, typically looking for markets with a minimum of a billion-dollar potential.

Between TAM, SAM, and SOM, TAM is probably the most critical market size and consequently we will discuss extensively how it is going to be calculated later in this post.

Serviceable Addressable Market (SAM)

What it is: SAM (sometimes also called Serviceable Available Market) is the portion of your Total Addressable Market (TAM) that your company can realistically reach and serve with its current business model, product offerings, and distribution channels.

Think of it this way: If TAM is the entire ocean of fish, SAM is the school of fish that your specific fishing boat (your business) is equipped to pursue and catch within that ocean.

Key considerations for defining SAM:

• Geographic Reach: Your SAM is limited to the regions where your product is actually available due to distribution, regulations, or language barriers.
• Customer Segments & Demographics: Your product might only appeal to specific age groups, industries, or business sizes. SAM filters TAM based on these precise customer profiles.
• Product Capabilities & Fit: Does your product genuinely address the needs of everyone in the TAM? SAM focuses on the segments that your product is designed and capable of serving (e.g., high-end enterprise software won’t have small businesses in its SAM).
• Business Model Limitations: Your operational model might naturally exclude certain market segments (e.g., a subscription-only service won’t target customers looking for one-time purchases).

How it’s calculated: SAM is a direct subset of TAM. You start with your refined TAM calculation and then apply filters based on these realistic operational constraints.

SAM = (Relevant segment of TAM) × (Your Price per customer/unit)

Serviceable Obtainable Market (SOM)

What it is: SOM (also known as Share of Market) is the most realistic and conservative estimate. It’s the portion of the Serviceable Addressable Market (SAM) that your company can realistically capture or “win” in the short to medium term, given its specific resources, current capabilities, competitive landscape, and marketing efforts.

SOM represents the actual number of fish you are genuinely likely to catch from the school of fish (SAM) in the near future, considering your crew’s skill, net size, and other boats fishing nearby. This is your most actionable market projection.

Key considerations for defining SOM:

• Competitive Landscape: Crucially, who are your direct and indirect competitors? How strong are they? What market share do they currently command? Your SOM must account for the existing competition.
• Company Resources & Constraints: This includes your marketing budget, sales team size, production capacity, brand recognition, and distribution network. A brand new startup will naturally have a much smaller SOM than an established market leader.
• Go-to-Market Strategy: Your specific plans for reaching and converting customers (e.g., direct sales, online advertising, partnerships, content marketing) will dictate how much of the SAM you can realistically obtain.
• Historical Performance (if applicable): If you have existing sales data, your past market penetration within your SAM can inform your SOM. For new businesses, this might be a small, realistic percentage (e.g., 1-5%) based on industry benchmarks and your unique competitive advantage.

How it’s calculated: SOM is a subset of SAM, typically estimated as a realistic percentage of the SAM.

SOM = (Realistic percentage of SAM) × SAM

SAM vs. SOM: Understanding the Critical Difference

The distinction between SAM and SOM is frequently blurred, leading to unrealistic short-term projections and misaligned expectations. While SAM defines what your company can realistically serve based on its product, business model, and distribution, SOM focuses on what it can realistically capture given internal limitations and external competition. SAM outlines the accessible market segment your current solution is equipped to pursue, whereas SOM considers the competitive friction and your actual execution capabilities. Investors examine SAM to understand the near-term growth potential of a specific solution, but they scrutinize SOM to understand operational realism and go-to-market effectiveness. An overly optimistic SOM, particularly for a new venture, signals naiveté, while a realistic SOM demonstrates a grounded understanding of the competitive landscape and a clear, executable plan.

The Concentric Circles: TAM, SAM, and SOM in Harmony

Think of these three market sizing metrics as concentric circles, each offering a progressively narrower and more realistic view of your market opportunity:

Presenting all three metrics credibly demonstrates a comprehensive understanding of the market, from its vast potential to your company’s immediate, actionable goals. This nuanced approach inspires confidence in investors, proving you have a clear vision of the future while remaining grounded in present realities.

Common Mistakes in TAM Calculation (And How to Avoid Them)

Many startups, eager to impress, fall into common traps when calculating their TAM. These missteps often lead to inflated, unrealistic projections that can instantly raise red flags for discerning investors, eroding trust and leading to critical internal miscalculations.

• The “Top-Down” Trap: This is perhaps the most frequent error. Startups often declare, “It’s a huge market, and if we just get 1% of it, we’ll be enormous!” This simplistic, top-down approach lacks underlying assumptions and rarely holds up to scrutiny, signaling a lack of due diligence. Solution: Always build your market size estimate meticulously from specific components using a bottom-up approach.

• Confusing “Size of the Problem” with TAM: Just because a problem is massive doesn’t mean your solution can capture all the associated revenue. For instance, stating “the opioid crisis is a $100 billion problem” is about the problem’s scale, not your product’s revenue potential within it. Your TAM must be directly tied to the market opportunity your solution creates, otherwise, you risk misallocating resources. Solution: Directly tie your TAM to the market opportunity your specific solution creates, not the problem’s overall cost.

• Claiming the Entire Market: You might be in the cybersecurity industry, but your niche cybersecurity product isn’t going to serve every single company in the $250 billion global cybersecurity market. Your TAM needs to reflect your specific market, the segment that your product truly addresses, not the industry’s behemoth total. Overstating your market leads to unfocused strategies. Solution: Define your specific market segment that your product truly addresses, reflecting your niche.

• Inaccurate or Unvalidated Pricing: Your TAM is calculated as “Number of Customers × Your Price.” If the price you’re using is hypothetical, or worse, borrowed from a high-charging competitor when you plan to be a low-cost alternative, your entire calculation will be fundamentally flawed. This can lead to unrealistic revenue projections and poor strategic decisions. Solution: Base your TAM on your actual or rigorously tested pricing, reflecting customer Willingness to Pay (WTP).

• Lack of Documented Assumptions and Data Sources: This is where transparency comes in. Investors aren’t looking for magic; they’re looking for logic. Failure to show your work, detailing where your customer numbers come from and how your pricing is validated, instantly erodes credibility and suggests a superficial understanding of your market. Solution: Clearly document every assumption, data source, and step of your calculation to build credibility.

• Unrealistic Customer Targeting and Geography: A broad, undifferentiated customer base like “all companies globally” leads to an inflated TAM. You need to precisely define your ideal customer profile and the geographic regions you genuinely plan to serve. This lack of focus can lead to wasted marketing and sales efforts. Solution: Precisely define your ideal customer profile and the realistic geographic regions you plan to serve.

How to Calculate TAM the Right Way: The Bottom-Up Approach

The only credible way to calculate TAM is through a bottom-up approach. This involves building your market size estimate from specific, well-defined components. This method is not just about arriving at a number; it serves as a proxy for your overall market understanding and operational discipline. Investors, in particular, use this calculation as a diagnostic tool, scrutinizing the underlying assumptions and data integrity to grasp your team’s due diligence, understanding of the ideal customer, and commitment to transparency. The rigor of this approach compels critical market research and customer validation early in your journey, leading to a more accurate product-market fit and a robust overall business strategy.

The fundamental formula is straightforward:

TAM = Number of Target Customers × Your Price (per customer or unit)

Here’s how to break down the process:

1. Define Your Target Customers with Precision
   Specificity is Key: Avoid vague terms like “all companies” or “everyone.” Instead, think about the precise characteristics of your ideal customer. Are they small-to-mid-size businesses? Fortune 500 enterprises? Early-stage startups? Specific demographics like Gen Z gamers or senior citizens?
   Narrow the Scope: Identify the precise industry, business size, or user behavior that defines your core customer base.
   Specify Geography: Are you targeting the US market? North America? A specific city? Or truly global? Be realistic about your current and immediate future reach.
   Cite Your Data: Crucially, document where you found the numbers for your customer base. Reliable sources could include industry reports, government statistics, market research firms, or even your own primary research.
2. Determine Your Price with Accuracy
   Use Your Actual/Tested Pricing: If you’re an early-stage startup, you must have rigorously tested your pricing with potential customers to validate their Willingness to Pay (WTP). For established businesses, use your proven price points.
   Define the Unit of Measure: What does your price apply to? Per month? Per user? Per branch? Per device? Per transaction? Be explicit.
   Your Price, Not Competitors’: Your TAM must reflect your revenue potential. If your pricing strategy differs significantly from competitors, use your own projected price, not theirs.
3. Show Your Work
   Document Everything: Every assumption, every data source, and every step of your calculation must be clearly documented. For presentations, using footnotes on your slides is highly recommended. This transparency builds trust and demonstrates a thorough understanding of your market.
   This rigorous, bottom-up methodology is vital. Why? Because multiplying two numbers that are based on inaccurate assumptions (e.g., an inflated customer count and a speculative price) will inevitably result in an unrealistic TAM. This process of “narrowing the TAM funnel” ensures your calculation is grounded in reality.

With this approach TAM, SAM, and SOM become very correlated with TAM been the first to be calculated. SAM and SOM are a consequence of the first (TAM).

Example: Right-Sizing the Cybersecurity Market

Let’s illustrate the bottom-up approach with a common scenario: a cybersecurity solution.

The Initial (Mistaken) Approach:

A common misstep is for a company to proudly announce, “Our ‘Swiss Army knife’ cybersecurity product crushes nearly every problem a company has! The cybersecurity market is a trillion-dollar global problem, and the market size is $250 billion annually!”

This is a classic top-down, problem-focused, and overly broad estimate: a significant red flag for any seasoned investor or strategic planner. It lacks specificity and fails to demonstrate a true understanding of where the company fits within that vast market.

The Correct (Bottom-Up) Calculation Process:

This process involves a rigorous “funneling” of the market, moving from a broad concept to a highly specific, addressable segment.

1. Narrowing the Customers:

• Initial claim: “Every company globally” (e.g., hundreds of millions of potential customers).
• Drill down: “Not every company, but primarily financial institutions.” (This might narrow it to, say, hundreds of thousands of institutions worldwide).
• Further refinement: “Not all financial institutions (e.g., not large insurance, credit card, or mortgage companies), but rather solutions fine-tuned for bank-specific cybersecurity issues.” (This could reduce the target to tens of thousands of banks).
• Even deeper: “Not massive global banks (like JP Morgan, Citibank), but specifically small to mid-size banks that don’t have a dedicated Chief Information Security Officer (CISO) and rely on a VP of IT or CIO for security.” This is where their value proposition truly shines, potentially narrowing the focus to thousands of such institutions.
• Geographic scope: “We currently operate and support customers in North America, South America, and Europe. Asia and Africa would be future expansion opportunities.” This further refines the number of target institutions to a specific, manageable count (e.g., ‘X’ thousand relevant bank branches in the defined regions).

Resulting Target Customer: Small to mid-size bank branches in North America, South America, and Europe without a dedicated CISO. The next step is to accurately quantify the number of such branches.

2. Determining the Price:

• Initial claim: “Around $10,000.”
• Drill down: “$10,000 per what unit?”
• Clarification: “$10,000 per branch per month.” (This price must be rigorously tested and validated with potential customers).

3. Calculating TAM:

Once the specific number of target customer units (e.g., “X” number of relevant bank branches in the defined regions) is identified, and the validated price ($10,000 per branch per month) is established, the TAM can be calculated:

TAM = (Number of relevant bank branches) × ($10,000/branch/month) × (12 months/year)

By undergoing this rigorous narrowing process, the initial, grandiose “$250 billion market” often transforms into a more realistic, yet still substantial, “$250 million a year” market. This provides a much more accurate and credible TAM, showcasing a grounded understanding of the market.

The Strategic Advantage of Right-Sizing Your Market

For many, seeing a massive market shrink to a more realistic figure can feel like a reduction in ambition. However, this “right-sizing” is, in fact, a profound strategic advantage. A smaller, precisely defined market allows for:

• Hyper-focused Product Development: You can tailor your solution to the exact needs of a specific customer segment, achieving a stronger product-market fit.
• Targeted Marketing & Sales: Your marketing budget and sales efforts become far more efficient, reaching the right customers with the right message, leading to higher conversion rates.
• Optimized Resource Allocation: Precious early-stage resources (time, money, talent) are not wasted chasing an impossibly broad market.
• Investor Confidence: Investors often prefer a credible, focused plan for a well-understood, albeit smaller, market over a vague, inflated claim for a massive one. A realistic TAM, and subsequently a realistic SOM, demonstrates a clear, executable path to market dominance within a specific niche. This “land and expand” strategy is frequently more appealing and less risky than attempting to capture a tiny percentage of an impossibly large market, signaling a mature and disciplined approach to growth.

Beyond TAM: Introducing SAM and SOM

While TAM paints the big picture, it’s just the starting point. To provide an even clearer and more actionable view of your market opportunity, you need to consider Serviceable Addressable Market (SAM) and Serviceable Obtainable Market (SOM). These two metrics offer increasingly realistic perspectives on the portion of the market you can genuinely target and capture. The video below illustrate how to calculate TAM and I used it as a key reference for this part of the post:

Final note on Mastering TAM, SAM, and SOM

Think of these three market sizing metrics as concentric circles, each offering a progressively narrower and more realistic view of your market opportunity. In short TAM, SAM, and SOM are:

TAM (Total Addressable Market): The outermost and largest circle. This is the entire ocean, the theoretical maximum revenue if you captured 100% of the entire market globally, without any competition. It sets the ceiling and signals the overall market potential.

SAM (Serviceable Addressable Market): A smaller circle within TAM. This represents the portion of the ocean you can actually reach with your current fishing boat and equipment (your business model, product, and distribution). It defines the segment you are capable of serving.

SOM (Serviceable Obtainable Market): The innermost and smallest circle within SAM. This is the number of fish you can realistically expect to catch given your current crew, bait, and competition. It’s your realistic, near-term revenue target.

Presenting all three metrics (TAM, SAM and SOM) credibly demonstrates a comprehensive understanding of the market, from its vast potential to your company’s immediate, actionable goals. This nuanced approach inspires confidence in investors, proving you have a clear vision of the future while remaining grounded in present realities.

In conclusion, mastering TAM, SAM, and SOM is not merely an academic exercise but a fundamental discipline for anyone seeking to understand and capitalize on market opportunities. By diligently applying these metrics, you gain unparalleled strategic clarity, optimize your resource allocation, and build a robust foundation for sustainable growth. Embrace the rigor of accurate market sizing, and you will not only articulate your vision with greater precision but also navigate the complexities of the market with confidence, transforming potential into tangible success.

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Beyond the Hype: Mastering #TAM, #SAM, and #SOM
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Don't let inflated market projections sink your venture. Get grounded in reality with #TAM, #SAM, and #SOM. It's about building a viable business, not just chasing #hype ! #MarketSizing #BusinessStrategy #StartupTips #MarketAnalysis…
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Thanks for taking the time to read this blog post on TAM, SAM and SOM. Other related posts includes:

• How to build a Two Side Marketplace
• Marketplaces for Buying and Selling Internet Business

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The job of a revenue manager is a classic example of a digital decision-making (DDM) problem because it involves the use of complex information and communication technologies (ICT) to make informed decisions that significantly impact the organization. Here’s how it aligns with the principles of DDM:

• Data Collection and Analysis: Revenue managers collect vast amounts of data from various sources, including historical booking data, market trends, competitor pricing, and customer behavior. This data is then analyzed to identify patterns and trends that inform pricing and inventory decisions.
• Modeling and Forecasting: Using sophisticated algorithms and models, revenue managers forecast future demand and set optimal prices. This involves modeling human behavior (e.g., booking patterns) and system-generated data (e.g., market conditions) to predict outcomes and make strategic decisions.
• Human-Machine Cooperation: Revenue management systems (RMS) leverage both human expertise and machine intelligence. While automated systems provide real-time data and pricing recommendations, human revenue managers interpret this information, make final decisions, and adjust strategies as needed. This cooperation ensures that decisions are both data-driven and contextually appropriate.
• Optimization and Implementation: Revenue managers continuously optimize pricing and inventory strategies to maximize revenue and occupancy rates. This involves filtering, segmenting, and augmenting information to refine models and improve decision-making processes. The goal is to devise techniques that support human-driven tasks (e.g., setting prices) and design systems that can autonomously adjust prices based on predefined criteria.
• Impact on Organizations and Society: Effective revenue management promotes significant changes within the organization by improving financial performance, enhancing customer satisfaction, and maintaining competitive advantage. These changes can also have broader societal impacts, such as influencing market dynamics and consumer behavior.

In summary, the role of a revenue manager exemplifies digital decision-making by integrating data collection, analysis, modeling, human-machine cooperation, and optimization to make strategic decisions that drive organizational success.

Note on Software and Human Machine Cooperation:

Revenue Management Systems (RMS) are specialized software tools designed to help businesses optimize their pricing strategies, manage inventory, and maximize revenue. These systems use data analysis, market trends, and forecasting to make informed decisions about pricing and availability. For short-term rental businesses, RMS can be particularly valuable in adjusting rates dynamically based on demand, seasonality, and other factors.

Examples of RMS for Short-Term Rentals

1. Beyond Pricing: This tool offers dynamic pricing solutions that automatically adjust rental rates based on market demand and other factors.
2. PriceLabs: Known for its user-friendly interface, PriceLabs provides dynamic pricing and revenue management solutions tailored for short-term rentals.
3. Wheelhouse: Wheelhouse offers advanced pricing algorithms and analytics to help property managers optimize their revenue.
4. AirDNA: While primarily a market data provider, AirDNA also offers tools for dynamic pricing and revenue management.
5. Lodgify: This all-in-one vacation rental software includes revenue management features to help optimize pricing and increase bookings.
6. Hostfully: Hostfully integrates with various RMS tools and provides dynamic pricing options to maximize revenue.
7. Rentals United: This platform offers channel management and revenue optimization tools for short-term rental businesses.
8. Guesty: Guesty provides a comprehensive property management solution with built-in revenue management features.

These RMS tools help short-term rental businesses, and their revenue managers stay competitive by ensuring their pricing strategies are aligned with market conditions and demand trends. Consequently, by leveraging these systems, property managers can increase occupancy rates and overall profitability.

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Department: Information Systems and Operations Management (ISOM) and Marketing Division

Location: Tilburg University and Remote

Job Description:

The University of Tilburg is seeking a motivated student assistant to join a collaborative project between the Department of Information Systems and the Marketing Division. This role offers a unique opportunity to gain hands-on experience with Google Tag Manager, JavaScript, and pixel technologies such as Facebook Pixel.

Responsibilities:

• Implement and manage Google Tag Manager for various web projects.
• Develop and maintain JavaScript code to enhance website functionality.
• Set up and optimize Facebook Pixel for tracking and analytics.
• Collaborate with team members from both the Information Systems and Marketing Division.
• Assist in analyzing data and generating reports to improve marketing strategies.

Requirements:

• Current student at the Tilburg University.
• Experience (or desire to learn) with Google Tag Manager and JavaScript.
• Familiarity with pixel technologies (or desire to learn), particularly Facebook Pixel.
• Strong analytical, problem-solving skills as well as capability to proactively solve problems
• Ability to work independently and communicate your results to a multidisciplinary team.
• Good communication skills.

Benefits:

• Earn money while gaining valuable work experience.
• Opportunity to earn academic credits.
• Work on real-world projects that enhance your resume.
• Flexible working hours to accommodate your academic schedule.

How to Apply: Interested candidates should submit their resume and a brief cover letter detailing their experience with Google Tag Manager, JavaScript, and pixel technologies via email to Francesco Lelli (see Contacts page). Interested candidates may also continue this experience as part of their master thesis (see Data Driven Student Journey).

Are you interested? This is really a unique opportunity! Don’t miss this chance to gain valuable experience and contribute to exciting projects. Apply now and take the next step in your academic and professional journey in a fun and multidisciplinary environment!

The post Student Assistant – Google Tag Manager and Pixel Technology at Tilburg University appeared first on Francesco Lelli.

• User agency: The self-perceived abilities of users to operate and control their devices.
• Device agency: The capacities that users attribute to their smart devices.

The study introduces four agency profiles that characterize different types of human-smart device relationships:

1. Controller: High user agency, low device agency
2. Collaborator: High user agency, high device agency
3. Victim: Low user agency, high device agency
4. Detached: Low user agency, low device agency

Figure below presents an intuitive idea of the research:

The researcher is validated by conducting an exploratory factorial analysis, tag cloud analysis, and sentiment analysis on survey data to investigate these agency profiles. In this work we examined also how various background variables (such as age, gender, education, and device type) relate to user and device agency.

Key findings include:

1. The type of smart device significantly affected user agency, with household cleaning device users reporting the highest user agency and personal assistant device users reporting the lowest.
2. Most background variables (age, gender, education, professional field, work experience) did not significantly impact user or device agency.
3. The tag cloud analysis revealed distinct patterns of language use associated with each agency profile, providing insights into how users perceive their relationships with smart devices.

This research contributes to the fields of Human-Computer Interaction (HCI) and Information Systems (IS) by offering a framework for understanding the varied ways humans interact with and perceive smart devices. The findings have implications for designing more effective and user-friendly smart device interfaces and experiences. The impact of this paper on human-smart device relationships and related fields includes:

1. Theoretical contribution: The study introduces a novel framework for understanding human-smart device relationships through the lens of user and device agency. This conceptualization provides a new perspective for researchers in Human-Computer Interaction (HCI) and Information Systems (IS).
2. Design implications: The findings can inform the design of smart devices and their interfaces. By understanding different agency profiles, designers can create more user-friendly and effective devices that cater to various user perceptions and needs.
3. User experience enhancement: The research helps in identifying factors that influence user satisfaction and engagement with smart devices. This knowledge can be applied to improve overall user experience.
4. Industry relevance: The study’s insights are valuable for companies developing and marketing smart devices. Understanding user perceptions can guide product development and marketing strategies.
5. Educational impact: The research provides a foundation for educating users about their relationship with smart devices, potentially leading to more informed and empowered consumers.
6. Future research directions: The paper opens up new avenues for research in human-smart device interactions, encouraging further exploration of agency concepts in technology adoption and use.
7. Societal implications: By shedding light on how people perceive and interact with smart devices, the study contributes to broader discussions about the role of technology in society and its impact on human behavior.
8. Methodological contribution: The combination of exploratory factorial analysis, tag cloud analysis, and sentiment analysis provides a multi-faceted approach to studying human-smart device relationships, which can be applied in future studies.

This research has the potential to influence how we design, use, and think about smart devices, ultimately advancing our understanding the human-technology interactions. Overall, you should read this paper for several compelling reasons:

• Novel perspective: The paper introduces a unique framework for understanding human-smart device relationships through the concepts of user agency and device agency. This approach offers a fresh perspective on how people interact with and perceive their smart devices.
• Practical implications and industry relevance: The research has direct applications for designers and developers of smart devices. By understanding different agency profiles, they can create more user-friendly and effective products that cater to various user perceptions and needs. Thus facilitating the development and marketing of smart devices.
• Insight into user experience: The study provides valuable insights into factors that influence user satisfaction and engagement with smart devices. This knowledge can be applied to enhance overall user experience across different types of smart devices.
• Societal impact: The research contributes to broader discussions about the role of technology in society and its impact on human behavior. It helps us understand how people perceive and interact with increasingly prevalent smart devices.
• Methodological innovation: The paper employs a multi-faceted approach combining exploratory factorial analysis, tag cloud analysis, and sentiment analysis. This methodology provides a comprehensive understanding of human-smart device relationships and could be applied in future studies.
• Future research directions: The study opens up new avenues for research in human-smart device interactions, encouraging further exploration of agency concepts in technology adoption and use.

In summary, by reading this paper, you’ll gain a deeper understanding of the complex relationships between humans and smart devices, which is increasingly relevant in our technology-driven world. The insights provided can be valuable whether you’re a researcher, designer, industry professional, or simply interested in how technology shapes our daily lives.

Link to the paper: https://doi.org/10.3390/fi16030090

Dataset for the study of Human and Device Agency

The relationship we have with our smart devices is more complex than we think! This research reveals how user and device agency influence our interactions. #TechResearch #HCI #IoT #AI
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Human-computer interaction (HCI) is a multidisciplinary field that explores the interaction between humans and computers, emphasizing especially the design and use of computer technology. Within this domain, the notion of sense of agency, usually defined as the users perceiving their actions as influencing the system, is of crucial importance. Another central notion is that of socio-technical systems, referring to the interplay between social and technical elements. In this work, we delve into users’ perceptions of their interactions with smart devices, drawing insights from a dataset collected during the COVID-19 outbreak in 2020.

The dataset, gathered through an expansive survey, was distributed in professional and personal networks, offers a window into the perspectives of 587 respondents, predominantly highly educated professionals with significant technological familiarity. While the sample’s specificity limits the generalizability of findings, it offers valuable insights into how tech-savvy users perceive their interactions with smart devices, illuminating the nuanced dynamics of HCI within socio-technical contexts.

One of the survey’s notable aspects was its focus on users’ choice of a single smart device to consider throughout their responses. This deliberate framing encourages a deeper exploration of users’ relationships with specific technologies, shedding light on the intricacies of their interactions. For respondents who did not own multiple smart devices, the instruction was to choose their primary device, such as a computer or mobile phone, thus ensuring consistency in the examination of user experiences.

Within the dataset, various dimensions of users’ interactions with smart devices emerged, encompassing cognitive, emotional, and behavioral aspects. Users’ perceptions of agency, defined as their sense of control and influence over the device, are particularly intriguing. Analysis of the dataset reveals diverse manifestations of agency, ranging from feelings of empowerment and efficiency to frustrations stemming from perceived limitations or complexities in device operation.

Moreover, the socio-technical lens allows us to delve deeper into the contextual factors shaping users’ interactions. The dataset captures not only users’ individual experiences but also the broader socio-cultural and technological landscapes influencing these interactions. Factors such as educational background, professional expertise, age, and gender are among the variables examined, offering insights into how these socio-demographic factors intersect with users’ perceptions of smart device interactions.

The dataset’s utilization in previous research endeavors underscores its significance in advancing our understanding of human-computer interaction, user behavior analysis, and agency. By building upon existing research and leveraging the data available, researchers can uncover deeper insights into the complexities of smart device interactions and contribute to the development of more user-centered technologies.

The COVID-19 context during which the data were collected adds another layer of complexity to the analysis. Due to the increased time spent at home and the increase in remote work, the global pandemic catalyzed increased reliance on technology for work, education, and socialization. Understanding users’ interactions with smart devices within this unique context provides valuable implications for designing technologies that accommodate evolving user needs and preferences.

In conclusion, users’ notions of their interactions with smart devices offer a rich tapestry of insights that intersect with key concepts in HCI, including agency and socio-technical systems. Through the analysis of a dataset collected during the COVID-19 pandemic, we gain valuable insights into users’ perceptions, behaviors, and experiences with smart devices within the context of a rapidly evolving technological landscape. This dataset serves as a springboard for further exploration in the field, driving forward our understanding of human-computer interactions in an increasingly digital world.

Where to find more:

• Agency in Human-Smart Device Relationship: A work in progress description of a research done with this dataset

Initial call for collecting the responses

Link to the dataset (Open Access)

https://doi.org/10.3390/data9040056

https://doi.org/10.34894/traony

Main Personal Related Publication

https://doi.org/10.3390/fi16030090

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In short, three different fellowships are available:

1. Research Fellowship: For individuals without a PhD who are considering pursuing research studies.
2. PhD Scholarship: For those who wish to pursue a PhD in Switzerland for a period of 3+ years.
3. Post-Doc Scholarship: For individuals with a PhD who are looking to spend one year in Switzerland conducting postdoctoral research.

Please note that all fellowships are intended for individuals who have not previously spent time in Switzerland. Consequently, if you are awarded one of these fellowships, you will be ineligible to apply for the others in the future as you technicaly already have a Phd or Postdoc in Switzerland.

Let’s see each one of them in dettail:

Research Fellowship

Research Fellowships are 12-month programs aimed at highly qualified postgraduate researchers from all academic disciplines. Applicants must have obtained a master’s degree or equivalent by July 31, 2024, except for ETH Zurich applicants, who must meet this requirement by June 30, 2024. Candidates should be born after December 31, 1988. The application requires a support letter from an academic host professor at a Swiss university, including a brief CV (maximum 2 pages), explaining their willingness to supervise and support the research. This letter differs from the confidential recommendation letter also required. Additionally, applicants must submit a research proposal with a defined time frame. Notably, this fellowship is not available to those who already hold a PhD degree

PhD Scholarship

PhD Scholarships are designed for highly qualified PhD students from all academic disciplines, offering funding for 12 to 36 months, extendable in three 12-month increments based on academic progress. Applicants must have obtained a master’s degree or equivalent by July 31, 2024, except for ETH Zurich applicants, who must meet this requirement by June 30, 2024. Eligible candidates should be born after December 31, 1988. The application requires a support letter from an academic host professor at a Swiss university, including a brief CV (maximum 2 pages), detailing their commitment to supervise and support the PhD throughout its duration. If an assistant professor supports the application, a full professor must co-sign the letter. This support letter is distinct from the confidential recommendation letter also required. For PhD studies at a University of Applied Sciences, an additional support letter from a faculty member of the host research university is mandatory. The doctoral degree must be awarded by a research university. A detailed research proposal with a defined time frame is the centerpiece of the application and requires thorough attention.

Post-Doc Scholarship

Postdoctoral Scholarships are 12-month programs aimed at highly qualified early career scientists from all scientific fields. Eligible candidates must have completed their PhD between December 31, 2020, and July 31, 2024 (June 30, 2024, for ETH Zurich), and primarily for those who have not previously studied in Switzerland. The program does not fund sabbaticals. Applicants need a support letter from an academic host professor at a Swiss university, including a brief CV (maximum 2 pages), explaining their willingness to supervise the research. Former PhD mentors and co-directors for co-tutelle PhDs cannot be supervisors. A detailed research proposal with a timeframe is the centerpiece of the application. This scholarship is exclusively for full-time postdoctoral research in Switzerland, prohibiting fieldwork outside the country, and recipients are expected to reside in Switzerland during the grant period. For double doctorates, the date of the first degree is considered. The scholarship is not valid for mixed research conducted partly in Switzerland and partly in another country.

Where to find more Information

The official link of the scholarship contains all the relevant information and is the “GO TO” resources:

• https://www.sbfi.admin.ch/sbfi/en/home/education/scholarships-and-grants/swiss-government-excellence-scholarships.html

Additionally, each country has specific information and details regarding the application procedure, which can be found at the following link.

• https://www.sbfi.admin.ch/dam/sbfi/en/dokumente/2022/07/all-countries.pdf.download.pdf/2023_ESKAS_all_country_contacts_A_to_Z_for_application_documents.pdf

I recommend relying on the official information provided at the link above. The program itself warns about potential scams, and while there are numerous YouTube videos on the topic, some are generated by ChatGPT and others are, for lack of a better word, “dubious.” Here, I share videos from two YouTubers who, in my opinion, offer a reliable overview of the program. From Jama, part 1:

And part 2:

As well as from Aatiya:

My personal suggestions:

If you are interested in applying for a Phd or Postdoc in Switzerland, here are my personal suggestions:

1. Start Early (Paperwork): The application process is deliberately complex to filter out unmotivated candidates. The responsibility for managing the paperwork falls entirely on you, so begin early to ensure you have ample time to complete everything accurately.
2. Start Early (Connections in Switzerland): A critical aspect of the application is obtaining a support letter from a professor in Switzerland who is willing to endorse your candidacy and confirm that your proposal aligns with their and their institution’s research. Building these connections early is essential.
3. Start Early (Your Research Proposal): Develop a meaningful and well-written research proposal as soon as possible. Without a solid proposal, it will be challenging to secure support from a professor. You are solely responsible for writing this proposal. Once you have a polished version formatted according to the required templates, use it as your business card when reaching out to potential professors in Switzerland. Without this document, your communication is likely to be ignored or flagged as spam.

In other words, your commitment and the importance of contacting your potential supervisor as early as possible, armed with all the relevant information, cannot be overstated. This official video from the program provides additional valuable suggestions:

In addition, please note that each professor will not/cam not endorse more then one (or two) candidates per year. Therefore many of the request may simply be not answered.

Can I help you further for your Phd or Postdoc in Switzerland?

It really depends. For this specific grant you (the applicant) are really responsible for doing the heavy lifting as well as for dealing with the dettails. As for the rest, (i) if you believe that you have what it takes for this fellowship, (ii) you fulfill all the requirements, (iii) you have a nice idea to pursue and (iv) it is related to one of the following areas:

1. Internet of Things (IoT): Refers to the network of interconnected devices and objects that can communicate and share data. IoT enables smart homes, industrial automation, and more.
2. Extended Reality (XR): Encompasses virtual reality (VR), augmented reality (AR), and mixed reality (MR). XR technologies blend digital content with the real world, enhancing user experiences.
3. Human Behavioral Informatics: The study of human behavior using data analytics and computational methods. It involves understanding patterns, preferences, and decision-making based on digital interactions.
4. Computer Vision: A field of artificial intelligence that focuses on enabling machines to interpret visual information from images or videos. Applications include facial recognition, object detection, and medical imaging.
5. Language Processing: Also known as natural language processing (NLP), it involves teaching computers to understand and generate human language. NLP powers chatbots, language translation, and sentiment analysis.
6. Security and Privacy: Addresses safeguarding data, systems, and networks from unauthorized access or breaches. It includes encryption, authentication, and privacy-preserving techniques.
7. Big Data Processing: Deals with managing and analyzing large volumes of data. Technologies like Hadoop and Spark enable efficient processing of massive datasets.
8. Distributed Systems: Involves designing and managing systems that run on multiple interconnected computers. Examples include cloud computing, peer-to-peer networks, and distributed databases.
9. Serverless Computing: A cloud computing model where developers focus on writing code without managing servers. Serverless platforms automatically scale resources based on demand.
10. Digital Decision Making: The process of using data-driven insights and algorithms to make informed choices in various domains, such as business, healthcare, and finance.

You can consider dropping me a line! Are you still finishing your thesis instead? These guides may help!

Good Luck!

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In the realm of business and AI, Supervised Learning (yet another AI technique that is becoming a bit old fashioned nowadays) and Generative AI emerge as pivotal techniques offering transformative potential. They are effective especially when approached as development tools tailored to specific domains rather than mere products or services to be integrated into existing business frameworks. This perspective advocates for leveraging AI technology not only as a tool but as a toolbox containing customizable instruments for domain-specific innovation. By understanding the intricacies of these techniques, businesses can harness their capabilities more effectively, thereby maximizing their impact on society and fostering sustainable growth. In essence, it’s about not just using the tool but understanding and utilizing the toolbox itself for the betterment of society and business alike.

The video below presents the business and AI view according to the AI Fund perspective:

Moreover, I would personally (i.e. this is my opinion) advocate that the combination of both, the “old fashion” supervised learning and the popular generative AI (with LLM leading the pack), coupled with sound understanding of information enrichment techniques will probably offer the best cocktail for a successful venture capable to create value for society.

In the rest of this post I will try to expand on this point by first looking at what Large Language Models are and how they function. Next, I will share a pointer to a comprehensive (and free!) resource for familiarizing with deep learning tools and techniques.

Large Language Models: What They Are, How to Make Your Own, and How to Engineer an Application

Let’s start by looking at Large Language Models using the following two videos. LLM are text manipulation tools that are capable of both summarizing and creative writing (witting code can be considered as a creative endeavor). Thanks to recent progresses (that we can date with the launch of ChatGPT), the structure and the consistency of such generated text is increasing in accuracy and, consequently, in usefulness. The video below can serve as a good introduction to how Large Language Models work and their capability of guessing the next word:

However, there is a notable gap in research concerning the integration of such approaches into everyday industrial practices. For example (to name the one that I hinted at before), the potential fusion of structured knowledge graphs that are typical of databases-oriented information systems with AI-based semantic embedding, remains largely untapped. Furthermore, the exploration of multiagent aspects and memory-resilient LLMs holds promise for improving business processes, yet systematic empirical validation of their efficacy is lacking. The video below is an introduction to how to engineer Large Language Models in order to perform tangible tasks of value:

Promising directions of investigation include:

• Exploring diverse applications of Large Language Models (LLMs) tailored to specific subtasks in composing a comprehensive global capability model.
• Investigating optimal development configurations and orchestrating multiple-agent LLMs to enhance solution effectiveness.
• Assessing the potential of memory-based agents in facilitating the synthesis of various capabilities.
• Establishing best practices for presenting semantically enriched data to LLMs in a meaningful manner.
• Integrating embeddings and implicit semantics with explicit knowledge from knowledge graphs to enrich the understanding and inference capabilities of LLM.

While numerous methods exist for grasping the utility of a tool, I contend that learning its construction can accelerate mastery and unlock its full potential. This video provides an insightful overview on (re)implementing a transformer architecture, as detailed in the seminal paper “Attention is all you need,” which underpins the success of ChatGPT.

This concludes the conversation on Large Language Models that are a part of the “Generative AI” family. What follows is an introduction to the old fashioned deep learning that, as I mentioned at the beginning of this post, will still probably cover an important role in the next years

A Few Notes on Deep Learning

Perhaps now that we know the details of the transformer architecture (and everything else related to LLM), we can zoom out from Generative AI (and LLM) and take a look at a larger context taking into account other aspects of AI. Deep Learning and Generative AI are intertwined fields within artificial intelligence, each serving distinct yet complementary purposes. Deep Learning, a subset of machine learning, employs neural networks with multiple layers to learn representations from data, excelling in tasks like classification, regression, and pattern recognition. Generative AI, on the other hand, focuses on creating new data samples that resemble those in the training data, utilizing techniques such as generative adversarial networks (GANs) and variational autoencoders (VAEs). The relationship between Deep Learning and Generative AI is evident in how Deep Learning techniques, like convolutional and recurrent neural networks, form the foundation for building generative models. For instance, GANs employ adversarial training between a generator and discriminator network, while VAEs use encoder-decoder architectures, both rooted in Deep Learning principles. Together, Deep Learning and Generative AI enable the development of sophisticated models capable of learning from data, generating new insights, and advancing artificial intelligence across various domains.

The video below presents the book “Understanding Deep Learning”. It has been published in December 2023 by MIT Press and is presenting itself as a comprehensive guide for learning modern machine learning.

However, as mentioned in the video, the field is currently growing at the rate of 4000 papers a month. Therefore, is almost impossible to be able to cover all the relevant aspects. However, the book is free and you download it at the following link:

https://udlbook.github.io/udlbook/

A Final Note on AI, LLM, Generative AI, Supervised Learning and Everything in Between

In conclusion, the surge of interest in Generative AI and Large Language Models (LLMs) across academic and industrial spheres underscores their potential to revolutionize various sectors. Embracing Supervised Learning and Generative AI as developmental tools tailored to specific domains, rather than mere commodities, holds promise for driving transformative innovation in business and beyond. By comprehending the intricacies of these techniques, businesses can harness their capabilities effectively, thereby maximizing societal impact and fostering sustainable growth. The exploration of diverse applications, optimal configurations, memory-based agents, semantic data presentation, and knowledge integration mark promising directions for future research. While understanding the construction of tools accelerates mastery, the broader context of Deep Learning and Generative AI highlights their intertwined roles in advancing artificial intelligence.

A No-#Nonsense Approach to #deeplearning , #LLM (#LLMs), Supervised Learning, #GenerativeAI, and Everything in Between
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Assessing a brokerage firm is crucial in the process of buying or selling internet business. The evaluation involves scrutinizing the firm’s credibility, reputation, and track record to ensure a positive and trustworthy experience. Industry expertise, client satisfaction, and the firm’s ability to maintain confidentiality are vital considerations. The range of services provided, transparency in communication, and the adoption of modern technology also contribute to the assessment. A brokerage’s global reach, user-friendliness, negotiation support, and post-sale assistance further influence the decision-making process. Overall, a comprehensive evaluation helps mitigate risks, aligns the chosen firm with specific needs, and increases the likelihood of a successful and efficient transaction.

What follows is my personal scorecard for assessing a particular brokerage firm. I will share each voice but in this post I will not evaluate each firm using it as this blog is explicitly develop for academic purposes.

1. Credibility and Reputation: Assessing a brokerage’s track record helps verify its credibility and reputation within the industry. A firm with a positive reputation is more likely to provide reliable and trustworthy services.
2. Transaction Success and Experience: Understanding the brokerage’s transaction volume and success rate provides insights into its experience. A firm with a proven track record of successful deals is more likely to navigate transactions effectively.
3. Industry Expertise: Different brokerage firms specialize in various industries or business models. Assessing industry expertise ensures that the firm understands the nuances of the specific type of online business you are buying or selling.
4. Client Satisfaction: Client reviews and testimonials offer valuable perspectives from individuals who have previously engaged with the brokerage. Positive feedback indicates a client-centric approach and effective service delivery.
5. Confidentiality Measures: When buying or selling a business, confidentiality is crucial. Assessing how a brokerage handles sensitive information ensures that your business details are protected during the process.
6. Services Offered: Understanding the range of services a brokerage provides helps ensure that your specific needs are met. Whether you require valuation services, marketing support, or due diligence assistance, a comprehensive set of services can be beneficial.
7. Transparency: Transparent communication about fees, processes, and the quality of information provided is essential. A transparent brokerage helps build trust and ensures that both buyers and sellers are well-informed.
8. Global Reach: The global reach of a brokerage may be important depending on your geographic preferences. Some businesses may benefit from exposure to an international audience, while others may prefer a more localized approach.
9. Negotiation and Deal Support: A reputable brokerage provides support during negotiations and throughout the deal-making process. This support can be crucial in securing favorable terms and closing the transaction successfully.
10. Post-Sale Support: Consideration of post-sale support is important, especially for sellers transitioning out of their businesses. A brokerage that offers assistance beyond the transaction can contribute to a smoother transition.

Scoring Key:

• 1-2: Weak
• 3-4: Moderate
• 5-7: Strong
• 8-10: Excellent

After evaluating each criterion and assigning scores, you can compare the scores to categorize the brokerage firms. Keep in mind that the weight assigned to each criterion may vary based on your specific priorities and preferences. This scorecard is a starting point for Buying and Selling Internet Business, and you can customize it based on your unique needs and considerations.

Flippa

Flippa is an online marketplace that facilitates the buying and selling of digital assets, primarily focusing on websites, domain names, and online businesses. Launched in 2009, Flippa has become one of the largest and most well-known platforms in the industry. Sellers can list their digital assets for sale, including e-commerce stores, blogs, mobile apps, and domain names, and potential buyers can browse through the listings to find opportunities. Flippa often features auction-style listings where sellers set a starting bid, and buyers can place bids over a specified time period. The platform provides a range of tools and services, including a due diligence process, to assist buyers and sellers in making informed decisions. Flippa caters to a diverse user base, from individual website owners to investors and entrepreneurs looking to enter or exit the online business space.

Empire Flippers

Empire Flippers is an online brokerage firm that specializes in the buying and selling of established and profitable online businesses. Founded in 2011, Empire Flippers focuses on facilitating transactions for a higher-end market, dealing with businesses that have a track record of generating revenue. The platform primarily handles the sale of various online assets, including content-based sites, e-commerce stores, and Software as a Service (SaaS) businesses. Unlike some other marketplaces, Empire Flippers employs a rigorous vetting process for both buyers and sellers, aiming to ensure the quality and legitimacy of the listings. Sellers often provide detailed financial information, and the platform assists in due diligence to give buyers confidence in their purchases. Empire Flippers has gained a reputation for transparency, professionalism, and its commitment to serving the mid-to-high-range online business market.

WebsiteClosers

WebsiteClosers is a brokerage firm specializing in the sale of online businesses, including e-commerce stores, technology companies, and other digital ventures. Founded in 1998, the company offers a range of services to both buyers and sellers involved in online business transactions. WebsiteClosers assists clients in various stages of the buying or selling process, providing services such as business valuation, exit strategy planning, marketing, and negotiation support. The firm emphasizes confidentiality during transactions to protect sensitive business information. WebsiteClosers positions itself as having experienced professionals with backgrounds in finance, law, and entrepreneurship, contributing to their ability to guide clients through the complexities of selling or acquiring online businesses. The focus on a personalized approach and a diverse range of services makes WebsiteClosers a choice for those looking to navigate the sale or purchase of established online ventures.

Quiet Light

Quiet Light is a brokerage firm specializing in the sale of online businesses. Founded in 2007, Quiet Light focuses on facilitating the sale of mid-market and high-value online businesses, including e-commerce stores, SaaS (Software as a Service) companies, content-based sites, and other digital assets. The company places an emphasis on providing a personalized and consultative approach, guiding clients through the entire selling process. Quiet Light is known for its experienced team of advisors, many of whom have a background in entrepreneurship and online business ownership. They offer services such as business valuation, exit strategy planning, marketing, and negotiation support. The firm is particularly known for its thorough due diligence process, ensuring that both buyers and sellers have confidence in the accuracy and completeness of the information provided during transactions. Overall, Quiet Light is recognized for its commitment to the mid-market segment and its expertise in navigating complex online business transactions.

FE International

FE International, founded in 2010, is a prominent M&A (mergers and acquisitions) advisory firm specializing in the sale of online businesses. The company focuses on facilitating the acquisition and sale of high-value digital assets, including Software as a Service (SaaS) businesses, e-commerce stores, and content-based websites. FE International has gained recognition for its expertise in the technology and online business space. The firm provides a range of services, including business valuation, exit strategy planning, marketing, and comprehensive support throughout the sales process. What sets FE International apart is its team of experienced advisors who possess a deep understanding of the online business landscape. The company is known for its thorough due diligence processes, transparent communication, and dedication to achieving optimal outcomes for both buyers and sellers in the online business market. Overall, FE International is a reputable player in the M&A advisory space, catering to a clientele focused on mid to high-value digital transactions.

Website Properties

Website Properties is a brokerage firm specializing in the sale of online businesses and website properties. Founded in 2002, the company facilitates the buying and selling of various online ventures, including e-commerce stores, affiliate marketing websites, software businesses, and other digital assets. Website Properties provides services such as business valuation, marketing, and negotiation support to both buyers and sellers involved in online business transactions. The firm caters to a diverse range of clients, from individual entrepreneurs to larger companies looking to acquire or divest digital assets. Known for its experienced team and commitment to client satisfaction, Website Properties aims to streamline the process of buying or selling online businesses by offering personalized guidance and comprehensive services. The firm’s focus on digital properties makes it a valuable resource for those navigating the complexities of the online business marketplace.

BizBuySell

BizBuySell, established in 1996, stands as a prominent online marketplace for the buying and selling of small to medium-sized businesses. It accommodates a diverse array of industries, providing a platform for sellers to list businesses with details such as financial information and history. Buyers can explore listings based on various criteria to find businesses that align with their interests and investment preferences. The platform features business valuation tools to help sellers set realistic prices and assists buyers in evaluating potential acquisitions. BizBuySell offers a broker directory for professional assistance, educational resources to navigate transactions, and periodic market insights on business trends. Its user-friendly interface and comprehensive features make it a favored choice for entrepreneurs and investors involved in the small to medium-sized business market. It is important to note that while BizBuySell caters to a broad range of businesses, it may not specialize in digital or online businesses as extensively as platforms focused solely on the online business market.

Latona’s

Latona’s is a brokerage firm that specializes in the sale of online businesses, domain names, and internet properties. Established in 2008, Latona’s provides a platform for buying and selling a variety of digital assets, including websites, e-commerce stores, and domain names. The company caters to a diverse clientele, from individual entrepreneurs to corporate entities looking to acquire or divest digital assets. Latona’s offers services such as business valuation, marketing, and negotiation support to facilitate successful transactions. The platform may also feature auction-style listings where potential buyers can bid on listed assets. Latona’s commitment to confidentiality and professionalism, along with its experience in the online business space, positions it as a reputable player in the digital asset brokerage industry.

Acquisitions Direct

Acquisitions Direct is a brokerage firm specializing in facilitating the buying and selling internet businesses. Established in 2003, Acquisitions Direct operates as a business intermediary, connecting buyers and sellers in various industries. The firm focuses on the sale of mid-sized companies, including online businesses, e-commerce stores, and other digital ventures.

Acquisitions Direct provides a range of services to both buyers and sellers involved in online business transactions. These services may include business valuation, marketing, and negotiation support. The firm aims to streamline the buying and selling process, offering personalized guidance and expertise to clients. Its approach may involve a thorough understanding of the unique aspects of each business, aiding in the successful completion of transactions.

Motion Invest

Motion Invest is a platform specializing in the buying and selling of content-based websites. Launched in 2020, Motion Invest focuses on smaller to mid-sized online businesses, particularly those with a focus on content creation, such as blogs and affiliate marketing sites.

Motion Invest streamlines the process of acquiring or selling websites by offering a marketplace where sellers can list their online properties and potential buyers can explore and purchase them. The platform provides a straightforward and transparent approach to the transaction process.

One distinctive feature of Motion Invest is its emphasis on simplicity and efficiency, making it attractive for individuals looking to enter or exit the online business space with relatively smaller-scale ventures.

Other Brokerage firms for Buying and Selling Internet Business

• Digital Acquisitions Digital Acquisitions is a brokerage firm that specializes in the sale of online businesses, including e-commerce stores, SaaS businesses, and content sites. They offer services for both buyers and sellers
• Investors Club Investors Club is a private marketplace that connects investors with online businesses. It requires an application process for membership to ensure a level of quality and seriousness among its users.
• Digital Exits Digital Exits is a brokerage firm that specializes in the sale of online businesses, including e-commerce stores, SaaS businesses, and content sites. They work with both buyers and sellers to facilitate transactions.
• BuySellEmpire BuySellEmpire is a marketplace for buying and selling online businesses. They offer a platform for users to list and browse businesses for sale, including e-commerce stores, affiliate sites, and more.

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In short Coupling, Cohesion and Granularity are part of SOA Design Principle and they can be summarized as follows:

• Service Coupling:
  • Definition: Refers to the degree of interdependence between two business processes.
  • Preferable State: Weak coupling is favored in SOA, indicating lower dependency for increased flexibility and maintainability.
• Service Cohesion:
  • Definition: Represents the degree of functional relatedness and focus of operations within a service.
  • Preferable State: Strong cohesion is preferred in SOA, indicating that a service should encapsulate closely related and well-defined functionalities.
• Service Granularity:
  • Definition: Denotes the scope of functionality exposed by a service.
  • Preferable State: Coarse granularity is recommended in SOA, suggesting that services should provide broad functionalities to address specific needs, promoting reusability.

Service Coupling:

Service Coupling is a critical design principle that examines the degree of interdependence between any two business processes or services within a system. It essentially evaluates how tightly or loosely these services are connected and the implications of changes in one on the others.

Characterized by the level of dependency, tight coupling signifies a strong interconnection where modifications in one service may necessitate corresponding changes in others. Conversely, weak coupling is the preferred state in SOA design. Weak coupling implies a lower level of dependency, allowing for greater flexibility, scalability, and ease of maintenance. Services with weak coupling can evolve independently, reducing the risk of unintended consequences when modifications or updates are made.

The benefits of weak coupling extend to the adaptability of the architecture to changes in business requirements. Services that are loosely coupled are more resilient to alterations, ensuring that adjustments in one part of the system do not propagate unexpectedly to other interconnected services.

For example, a service employing standardized interfaces and protocols can interact with other services more loosely, minimizing the impact of changes in one service on others. Achieving weak coupling often involves defining clear service interfaces, minimizing direct dependencies, and utilizing standards to facilitate interoperability between services. In essence, Service Coupling is a foundational principle in SOA design that promotes a modular, adaptable, and scalable architecture.

Service Cohesion:

Service Cohesion stands as a pivotal design principle governing the internal organization and functional relatedness within an individual service. It delves into the degree to which the operations encapsulated within a service are logically connected and focused on a specific set of functionalities.

Characterized by its emphasis on functional relatedness, strong cohesion is the ideal state for services in SOA. A service exhibiting strong cohesion encapsulates a well-defined and closely related set of functionalities, ensuring that its operations are united by a common purpose. This design approach enhances the service’s clarity, maintainability, and usability, as all contained functionalities contribute to a coherent and unified business capability.

In contrast, weak cohesion suggests a service with disparate functionalities that may not be logically connected, leading to a lack of clarity and potentially hindering the service’s effectiveness. Strong cohesion, on the other hand, facilitates a service’s reusability, as the encapsulated functionalities are designed to work together seamlessly, promoting a modular and extensible architecture.

For example, a service responsible for order processing should ideally encapsulate functionalities such as order validation, payment processing, and inventory management, exhibiting strong cohesion by focusing on a cohesive set of operations related to order fulfillment.

Achieving strong cohesion often involves careful analysis of the business processes and ensuring that the functionalities grouped within a service share a common context and purpose. This principle contributes to the creation of services that are not only well-defined and purposeful but also conducive to building a scalable and adaptable SOA.

Service Granularity:

The concept of Service Granularity plays a crucial role in defining the scope of functionality exposed by a service. It is essentially a measure of how broad or specific a set of functionalities needs to be in order to address a particular business need. The degree of granularity directly impacts the reusability, flexibility, and efficiency of the services within the architecture.

In SOA, the preferable state for Service Granularity is characterized by a tendency towards coarse granularity. Coarse-grained services encapsulate broader and more encompassing functionalities, capable of addressing significant business requirements in a single service invocation. This design approach promotes simplicity, reduces the number of service interactions, and facilitates easier maintenance.

Conversely, fine-grained services expose narrower sets of functionalities, potentially requiring multiple service invocations to fulfill a specific business operation. While fine granularity may seem intuitive for certain scenarios, it can lead to increased communication overhead and complexity, making the system more susceptible to performance issues and maintenance challenges.

For example, a coarse-grained service responsible for customer management might encompass functions such as creating, updating, and deleting customer profiles, providing a comprehensive set of functionalities within a single service interface.

Achieving the right level of granularity involves careful consideration of the business context and requirements. Coarse granularity is favored as it enhances the potential for service reuse, reduces the impact of changes on service interfaces, and aligns with the overarching goal of creating a modular and scalable architecture in SOA. Striking a balance between granularity and functionality is key to building an effective and adaptable service-oriented system.

Unlock the power of Service Oriented Architecture (#SOA )! Learn how #service Coupling Cohesion, and Granularity influence flexibility and #scalability in your #System . #softwaredevelopment #programming #API
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Service Oriented Architecture (SOA) Design Principle and Application Programming Interfaces (APIs)

The concepts of Service Coupling, Service Cohesion, Service Granularity, and APIs (Application Programming Interfaces) are intricately related in the context of designing and implementing a Service Oriented Architecture (SOA) or any modular and scalable system. APIs act as the external interface for services, and their design choices have a direct impact on the level of coupling, the cohesion within services, and the granularity of functionalities exposed. A well-thought-out API strategy is crucial for realizing the principles of a modular, scalable, and adaptable Service Oriented Architecture.

APIs and SOA operate at different levels of abstraction and have distinct purposes within the realm of software architecture. The distinction in their level of abstraction is rooted in their design principles, scope, and intended use. APIs are considered more low-level than SOA because they provide detailed interfaces for developers to interact with specific functionalities, while SOA operates at a higher level, focusing on the overall organization and alignment of services within a system. APIs are tools that facilitate the implementation of SOA principles at a more granular level.

This specific article focus on SOA design principles; if you want to know more on how to design a good API you can find more information here.

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Design patterns serve several important purposes in software development:

1. Reusability: They encapsulate proven solutions to common problems, making it easier for developers to reuse these solutions in different parts of their code or in different projects.
2. Abstraction: Design patterns provide a higher-level abstraction that helps developers think about and communicate the structure of their code more effectively. They provide a common language and framework for discussing design decisions.
3. Maintainability: Using design patterns can improve the maintainability of software because they promote well-structured and organized code. Developers can more easily understand and modify code that follows established patterns.
4. Scalability: Patterns can help ensure that software is designed in a way that allows it to scale and adapt to changing requirements without requiring major redesigns.
5. Community Knowledge: Design patterns are well-documented and widely recognized within the software development community. This means that developers can leverage the collective wisdom and experience of the community when applying these patterns.

As the term software embrace a very large domain, there are several categories of design patterns. Each one is specialized for a particular portion portion of the code. These patterns includes:

1. Creational Patterns: These patterns deal with object creation mechanisms, trying to create objects in a manner suitable to the situation. Examples include the Singleton, Factory Method, and Abstract Factory patterns.
2. Structural Patterns: Structural patterns focus on defining the composition of classes and objects. Examples include the Adapter, Bridge, and Decorator patterns.
3. Behavioral Patterns: Behavioral patterns are concerned with the interaction and communication between objects. Examples include the Observer, Strategy, and Command patterns.
4. Architectural Patterns: These are high-level patterns that deal with the overall structure of an application. Examples include the Model-View-Controller (MVC), Model-View-ViewModel (MVVM), and Layered Architecture patterns.
5. Concurrency Patterns: These patterns address issues related to managing concurrent access to shared resources in multi-threaded applications. Examples include the Mutex, Semaphore, and Read-Write Lock patterns.

Design patterns are not one-size-fits-all solutions, and they should be applied judiciously based on the specific requirements and constraints of a software project. Experienced developers use their knowledge of design patterns to make informed decisions about when and how to apply them to solve particular problems.

The list of pattern is long and is also evolving as the software developer community evolve. At the Some time pattern like facade, adapter/wrapper, proxy and observer stand the test of time and are the most known among developers. Let’s see each one of them

Facade Pattern:

• Purpose: The Facade pattern provides a simplified, high-level interface to a complex subsystem or set of classes. It acts as a “facade” to shield clients from the underlying complexities of the system.
• Use Cases: It’s useful when you want to provide a simplified and unified interface to a complex system, making it easier for clients to interact with it. It promotes loose coupling between the client code and the subsystem.

The image below present the pattern using a Class Diagram:

Example of Use: Multimedia Player Facade

Suppose you are building a multimedia player application that can play various types of media, such as audio and video files. The underlying multimedia framework is complex, with different classes and interfaces for handling codecs, rendering, audio output, and video display.

In this example, we could consider implementing a class of name MultimediaPlayerFacade that acts as a simplified interface to the complex multimedia framework. It encapsulates the initialization and usage details of the audio and video players, making it much easier for client code to play multimedia. The client code is simplified and less coupled to the underlying multimedia framework, which can change without affecting the client code.

The Facade pattern here provides a cleaner and more maintainable way to interact with a complex subsystem, shielding the client code from its intricacies.

Adapter Pattern also known as Wrapper Pattern::

• Purpose: The Adapter pattern allows two incompatible interfaces to work together by providing a wrapper around one of them. It acts as a bridge between two interfaces, making them compatible without changing their source code.
• Use Cases: It’s used when you have existing code with interfaces that are not compatible with the interfaces you need to use. The adapter pattern allows you to reuse existing code without modification.

The image below present the pattern using a Class Diagram:

Example of use: Legacy System Integration

Imagine you are working on a modern e-commerce application that needs to integrate with a legacy payment gateway. The legacy payment gateway has an outdated interface that doesn’t match the modern payment service interface your application uses. You can use the Adapter pattern to bridge the gap.

To integrate the legacy payment gateway with your modern payment service, you can create an adapter class. In this way you can use the Legacy Payment Adapter to make payments through the legacy payment gateway using the modern payment service. In this way the adapter acts as a bridge between the modern payment service interface and the legacy payment gateway.

Proxy Pattern:

• Purpose: The Proxy pattern provides a surrogate or placeholder for another object to control access to it. It can be used to add an additional layer of control over an object, such as lazy loading, access control, or caching.
• Use Cases: It’s useful when you want to control access to an object, especially in situations where the creation or access to the object is expensive or where you need to add functionality around object access.

The image below present the pattern using a Class Diagram:

Example of use: Image Loading Proxy

Suppose you are building an image viewer application, and you want to implement a feature that loads and displays high-resolution images from a remote server. However, loading these high-resolution images can be slow and resource-intensive. To improve user experience and optimize resource usage, you can use the Proxy pattern. You can use the ImageProxy to load and display images in your application without loading the high-resolution image until it’s actually needed. Consequently you can delay the creation and loading of resource-intensive objects, improving the performance and resource utilization of your application.

Observer Pattern:

• Purpose: The Observer pattern defines a one-to-many dependency between objects so that when one object changes state, all its dependents (observers) are notified and updated automatically.
• Use Cases: It’s commonly used for implementing distributed event handling systems, where multiple objects need to react to changes in another object’s state. Examples include GUI frameworks where UI elements update when underlying data changes.

The image below present the pattern using a Class Diagram:

Example of use: Weather Monitoring System

Imagine you are building a weather monitoring system that collects weather data from various sensors and notifies multiple display devices when the weather conditions change. In this scenario, you can apply the Observer pattern to implement the system. You could imagine a class of name ConcreteWeatherStation as the subject that collects weather data and notifies its observers (in this case, maybe various display objects) when the data changes. The Observer pattern allows multiple display devices to receive and react to updates without being tightly coupled to the weather station.

Summary of Software Patterns or Design patterns

Software patterns, also known as design patterns, are widely recognized and essential approaches for organizing code and solving recurring design problems in software development. Among these patterns, the Facade pattern simplifies complex subsystems by providing a unified interface, making it easier for clients to interact with intricate systems. The Adapter pattern bridges the gap between incompatible interfaces, allowing different components to work seamlessly together. The Proxy pattern acts as a surrogate for an object, controlling access and adding functionality, which can be valuable for resource optimization and security. Lastly, the Observer pattern facilitates the creation of distributed event handling systems, enabling one object to notify multiple observers about changes, enhancing code modularity and flexibility. These patterns represent just a subset of the many design patterns available, and they serve as valuable tools in the developer’s toolkit for building maintainable, scalable, and efficient software systems.

In the case you would like to learn more I would recommend you to have a look at the following book from “the gang of 4“

It is a classic and several libraries will probably have a copy.

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