You have an idea. You can’t code. You think that stops you? Let’s talk about that.

“I have this idea, but I’m not technical. I don’t know where to start.”

I hear this almost every week. And every time, I think back to some of the most successful founders I’ve had the privilege to work with — people who couldn’t write a line of code when they started.

What they had was something equally valuable: they understood people. They understood problems. And they knew how to ask the right questions.

The Myth of the Lone Builder

We’ve been sold a story that every successful product is built by someone typing furiously at 2AM. That’s one path. It’s not the only one.

Reality: most successful ventures are built by teams where different people bring different strengths. The coder. The storyteller. The operator. The domain expert. Each one is essential.

If you understand a problem deeply — because you’ve lived it, worked in it, or cared about it for years — that’s the foundation most technical founders spend years trying to find.

What Builders Who Don’t Code Actually Need

• The ability to articulate a problem so clearly that a developer wants to solve it
• The curiosity to learn just enough about technology to ask smart questions
• The humility to partner with people who know what you don’t

Notice what’s missing? You don’t need to learn to code. You need to learn to lead. And those skills are just as buildable as any programming language.

The Question to Ask Yourself

What’s the one problem you understand better than anyone else you know?

Here’s what I’ve come to believe: not being technical is not a weakness. Not today. Not in the age of AI.

AI doesn’t replace domain knowledge. It amplifies it. If you understand a problem deeply — because you’ve lived it, because you care about it — AI can help you build around it. It can write the code you can’t. It can handle the parts that used to require a computer science degree.

The question isn’t “Can I build this?” anymore. It’s “What would I build if I had a co-founder who could code everything I described?”

That’s what AI is becoming. A thinking partner. A builder you can talk to. The limitation was never you — it was access. And that access is changing.

So the real question is this: what would you build if you could build anything?

That’s not a hypothetical. That’s your starting point.

You don’t need to be technical to build something that matters. You just need to start.

The post Build Up #3 — Non-technical dreamers · Imposter syndrome first appeared on Innovation SWAT.

You built it. And the first reaction was quiet. That’s not failure. That’s the signal you’ve been looking for.

You built it. And the first reaction was quiet. That’s not failure. That’s the signal you’ve been looking for.

You spent months — maybe years — building something that works. The code is clean. The design is polished. The features are real.

And then you launched it.

And the response was quieter than you expected.

If that’s happened to you, you’re in good company. I’ve watched brilliant engineers pour their hearts into products that deserved to find an audience — and take time to reach their first hundred users. The product was never the problem. It was doing exactly what it was supposed to do. The next step just felt unfamiliar.

The bridge between building and selling is where the real journey begins.

Here’s a story I keep coming back to — not because it’s mine, but because it taught me something I’ve seen play out in dozens of startups since.

A brand was losing relevance. The product worked perfectly. The quality was solid. But the market had stopped paying attention. The team didn’t need to change what they built. They needed to change how people saw it.

That lesson stuck. And over the years, through projects across design, brand, and technology, I kept noticing the same pattern:

Technical founders naturally focus on the build. That’s a strength. The next natural step is learning how that build connects to the people it’s meant for.

It’s not about fixing a gap. It’s about adding a new layer — one that every great builder learns along the way:

• Putting words to value in a way that lets a customer see themselves in your product
• Positioning what you’ve built so it stands out — without stretching the truth
• Building a feedback loop that tells you what matters most to build next

These aren’t “soft skills.” They’re the next set of building skills — as learnable as any framework or language you’ve already mastered.

The question that clarifies everything:

If you had to explain why your product matters to someone who doesn’t care about technology — in 30 seconds — what would you say?

Most people can’t answer it on the first try. That’s not a failure. That’s a starting point. The same way a blank file tells you where to begin coding, that moment of silence tells you exactly where your next build should focus.

Not on a new feature. On a new connection: translating what you built into why it matters to someone else.

Over the coming weeks, I’ll share what I’ve learned about how great products find their audience, how teams with different skills can build better together, what AI actually means for the way we work, and the one system I wish I’d had when I started my first venture.

Nothing academic. Just lessons learned the hard way — shared with people who are on the same journey.

You built something real. That already puts you ahead. The next step isn’t about fixing anything — it’s about building the bridge to the people who need what you’ve made.

The post Build Up #2 — Tech Founder’s Blindspot first appeared on Innovation SWAT.

Here’s to the crazy ones.

Steve Jobs said that about the people who believe they can change the world. And you know what? I think he was talking about builders.

Not just the famous ones. Not just the billion-dollar founders. The person tinkering in a garage on a Saturday night. The designer learning to code after hours. The engineer writing their first marketing sentence because they believe their product could help someone.

There’s a certain pride that comes with making something that didn’t exist before. A humility too — because every builder knows the truth: you fail more than you succeed. The question isn’t whether you’ll make mistakes. It’s whether you’ll keep learning from them.

That’s the spirit this community is built on.

Over the past 25 years, I’ve worked across design, brand, business, and technology — from sketching products at VTech to leading digital transformation at Mary Kay, from a Kickstarter campaign that shipped across the globe to coaching 45+ tech startups through HKSTP. Along the way, I’ve been fortunate to learn from incredible builders in every field.

But I’m still learning too. Every project, every startup I coach, every book and case study I read teaches me something new. The frameworks I use today were shaped as much by my failures as by my successes — and they keep evolving.

In a world changing as fast as ours, that willingness to learn matters more than ever.

AI is rewriting the rules. Old career paths are dissolving. The uncertainty can feel overwhelming — especially if you’re wondering whether your skills are enough.

Here’s what I’ve come to believe: they are. You just need the right context, the right community, and the confidence to take the first step.

That’s why Innovation SWAT exists.

This is a community for people who love making things — at any age, at any stage. Whether you’re a seasoned founder, a student finding your footing, a career-shifter reinventing yourself, or someone who’s never built anything but dreams of starting — you belong here.

We’re building something together: a space where:

• You don’t have to choose between being technical and being strategic
• Trial and error is celebrated, not hidden
• Every builder — at every level — finds inspiration, practical tools, and people who genuinely want them to succeed

What to expect from this newsletter:

• Real stories from real projects — the wins, the failures, and the messy middle
• Practical frameworks drawn from hands-on experience and the best thinking I can find
• Honest takes on AI and the future of work — not hype, not fear, just what I’ve seen working
• A growing community where we help each other build with confidence

Over the coming weeks, I’ll share what I’ve learned about why great products fail to find their audience, how cross-functional teams can stop fighting and start building, what AI actually means for the way we work, and the one system I wish I’d had when I started my first venture.

Nothing academic. Just lessons learned the hard way — shared with people who are learning too.

If you’ve ever built something — or want to — you belong here.

Welcome to Innovation SWAT.

Let’s build what matters.

The post Build Up #1 — The Starting Point first appeared on Innovation SWAT.

Feel + Act + Say + Think (eFAST)

1. Feel: This component focuses on the emotional state of the user.

• Capture the user’s emotions, feelings, and concerns related to the product or service.
• Identify pain points and moments of delight in the user experience.
• Use techniques like mood boards or emotion scales to quantify feelings.

2. Act: This aspect observes the user’s behaviours and actions.

• Document the steps users take when interacting with the product or service.
• Note workarounds or shortcuts users employ to accomplish tasks.
• Utilise techniques like user journey mapping or task analysis to capture actions systematically.

3. Say: This element focuses on the user’s verbal expressions.

• Record direct quotes and feedback from users during interviews or usability tests.
• Pay attention to the language and terminology users employ when discussing the product.
• Use techniques like voice of customer (VOC) analysis to categorise and prioritise user statements.

4. Think: This component delves into the user’s thought processes and decision-making.

• Uncover the underlying reasoning behind user actions and choices.
• Identify assumptions, expectations, and mental models users have about the product or service.
• Employ techniques like think-aloud protocols or cognitive mapping to capture thought processes.

Applying eFAST to Empathy Mapping:

When creating an empathy map based on the eFAST framework:

• Divide the map into four quadrants corresponding to Feel, Act, Say, and Think.
• Populate each quadrant with insights gathered through various research methods (e.g., interviews, observations, surveys, social listening etc.).
• Look for patterns and connections across quadrants to develop a holistic understanding of the user.

Benefits for Persona Development:

• Provides a multi-dimensional view of the user, going beyond basic demographics.
• Helps identify key pain points and opportunities for innovation.
• Facilitates the creation of more realistic and relatable personas.
• Enables better alignment of product features and marketing messages with user needs and motivations.

By systematically applying the eFAST framework and integrating the insights in empathy mapping, product teams can develop richer, more nuanced personas that truly represent their target users, leading to more user-centric and successful product innovations.

Having said that, experienced researcher will NOT take the face value of what the customer “Act” & “Say”, but leverage deeper psychological analysis to dig deep into cognitive and subconscious level “Feel” and “Think” that drives the “Act” & “Say”.

Want to know how? Stay tune for the next sharing on a proprietary NATURE principles which I learnt and consolidate from experience working with professional consumer researcher over the years.

The post Unlocking User Insights: The eFAST Framework for Empathy Mapping first appeared on Innovation SWAT.

The challenge lies in crafting personas that are not just demographic profiles, but rich, multidimensional representations of real users. These personas must capture not only basic information but also deep insights into users’ motivations, pain points, and aspirations. Moreover, they need to be flexible enough to evolve with changing market trends and user behaviours.

One of the main difficulties in developing meaningful personas is avoiding oversimplification or stereotyping. It’s easy to fall into the trap of creating personas that are too generic or based on assumptions rather than real data. This can lead to misguided product decisions and missed innovation opportunities.

Another challenge is ensuring that personas are actionable and relevant across different teams involved in the product development process. They need to be detailed enough to inform design decisions, yet concise enough to be easily understood and remembered by all stakeholders.

Furthermore, in an era of big data and rapid technological change, keeping personas up-to-date and relevant is an ongoing challenge. As user behaviours and preferences evolve, so too must the personas that represent them.

Despite these challenges, when done right, persona development can be a powerful driver of product innovation. It can lead to more user-centric designs, better-targeted features, and ultimately, products that truly resonate with their intended audience.

Persona Development: Key Techniques

Persona development is a crucial process in understanding user segments and their needs. It involves several key techniques:

• Data Integration: Combining qualitative insights from interviews and focus groups with quantitative data from surveys and analytics to create well-rounded personas.
• Demographic Profiling: Gathering basic information such as age, gender, income, and education to form the foundation of each persona.
• Psychographic Analysis: Exploring lifestyle choices, values, and attitudes to add depth to personas beyond surface-level demographics.
• Behavioural Mapping: Analysing usage patterns, preferred channels, and decision-making processes to understand how users interact with products or services.
• eFAST Framework: Implementing the Feel, Act, Say, Think approach to capture emotional responses, actions, verbal feedback, and thought processes of users. (to be elaborate in other post)

These techniques collectively contribute to creating comprehensive personas that drive targeted marketing strategies, enhance user experiences, and inform product development, ultimately leading to offerings that truly resonate with specific user groups.

Quantitive Data Analysis

Demographic:

• Objective: Identify key characteristics of distinct user groups.
• Technique: Gather data through surveys, user registrations, and third-party data sources.
• Measurement Metrics: Age, gender, income, education level, and occupation.
• Impact: Establishes a fundamental understanding of the user base, enabling targeted marketing and informed product development.

Psychographic:

• Objective: Understand users’ psychological attributes, including values, attitudes, and lifestyles.
• Technique: Employ psychographic surveys, social media analysis, and lifestyle segmentation tools.
• Measurement Metrics: Interests, hobbies, values, lifestyle choices, and personality traits.
• Impact: Facilitates deeper engagement by aligning marketing messages and product features with users’ intrinsic motivations.

Qualitative Interviews and Focus Groups

• Objective: To gain in-depth insights into user experiences, preferences, and motivations.

• Technique: Conduct one-on-one interviews and facilitate group discussions with representative users.
• Measurement Metrics: Identify themes and patterns in user narratives, assess satisfaction levels, and gather qualitative feedback on specific features or experiences.
• Impact: Provides rich, contextual insights that complement quantitative data, uncovering underlying reasons for user behaviours and attitudes.

Design Thinking’s Empathy:

• Objective: Develop a deep empathetic understanding of users’ needs and challenges.
• Technique: Utilise empathy maps, journey mapping, and user shadowing.
• Measurement Metrics: Identify pain points, unmet needs, emotional triggers, and user goals.
• Impact: Drives user-centric innovation by ensuring solutions are grounded in real user experiences and needs.

Conclusion

In conclusion, persona development is crucial for the success of product innovation and business growth for several reasons:

• It provides a deep understanding of target users, enabling more effective product design and marketing strategies.
• Well-crafted personas help align teams across an organisation, ensuring a consistent focus on user needs.
• Personas facilitate more accurate decision-making in product development, reducing the risk of creating features that don’t resonate with users.
• They enable personalised user experiences, leading to higher customer satisfaction and loyalty.
• Personas help identify new market opportunities and potential areas for business expansion.

By investing in comprehensive persona development, businesses can create products that truly meet user needs, driving innovation, improving market fit, and ultimately fostering sustainable growth.

The post The Powerful Impact of Meaningful Persona Development first appeared on Innovation SWAT.

By comprehending the distinct responsibilities and influences of each stakeholder, businesses can better address their needs and preferences, ultimately enhancing customer satisfaction and conversions.

User

The user is the individual who directly interacts with and uses the product or service. They experience the product firsthand and their feedback is crucial for understanding product performance, usability, and satisfaction. In the consumer shopping journey, the user’s needs and preferences drive the initial search and evaluation of potential products. Their interaction with the product post-purchase also provides valuable insights for future improvements and innovation.

Shopper

The shopper is responsible for making the purchase decision. This role may overlap with the user, but it can also be distinct, especially in cases where the shopper buys products for someone else (e.g., parents purchasing for children). The shopper’s journey involves researching products, comparing options, and making decisions based on factors such as price, convenience, and perceived value. Their decision-making process is influenced by both their own preferences and the needs of the user.

Influencer

Influencers are individuals who have the power to affect the purchase decisions of others. This group includes Key Opinion Leaders (KOLs), Coaches, Experts, and social media personalities. They may not directly use or purchase the product, but their reviews, recommendations, and endorsements can significantly impact the shopper’s and user’s perceptions. Influencers often bring credibility and trust to the product, making their role pivotal in shaping consumer behaviour throughout the shopping journey.

Once we aware of different stakeholders and role in a consumers purchasing decision. It is important to know how do we measure their position and impact in the procession conversion?

Positive Promoter vs Neutral Observer vs Negative WOM

To effectively understand and address the different types of stakeholders in user acquisition and conversion, it’s essential to dig deep into each category using professional techniques and measurement metrics. Each of the Users, Shoppers, and Influencer can have different Positive or Negative position about your product, service or brand.

Below are few detail analysis to find out their positioning and impact to your business.

Survey & Quantitative Analysis

Objective: To gather large-scale data on user opinions, behaviours, and satisfaction levels.

• Technique: Use structured questionnaires distributed via email, social media, or in-app prompts.
• Measurement Metrics: Net Promoter Score (NPS), Customer Satisfaction Score (CSAT), Customer Effort Score (CES), frequency of use, and purchase behaviour.
• Impact: Helps in identifying promoters, neutrals, and detractors. Promoters are likely to recommend the product, neutrals are indifferent, and detractors may spread negative word-of-mouth (WOM).

Consumer Journey Big Data Analysis

Objective: To analyse user data from various sources to uncover patterns and trends.

• Technique: Utilise data mining, machine learning algorithms, and analytics platforms (e.g., Google Analytics, AWS Data Lakes).
• Measurement Metrics: User engagement metrics (time spent on site, click-through rates), conversion rates, churn rates, and behavioural segments.
• Impact: Provides insights into user segments, predicting future behaviours, and identifying opportunities for targeted interventions.

Customer Segmentations – Best Customer vs Switcher vs Cherry Picker

Objective: To categorise customers based on their value and behaviour towards the brand.

• Technique: Use cluster analysis, RFM (Recency, Frequency, Monetary) analysis, and customer lifetime value (CLV) calculations.
• Measurement Metrics: Purchase frequency, average order value, recency of last purchase, and total revenue generated.
• Impact: Identifies high-value customers (Best Customers), those who switch between brands (Switchers), and those who make occasional purchases based on offers (Cherry Pickers). Tailoring strategies for each segment can enhance retention and conversion rates.

In summary, employing a combination of these professional techniques and measurement metrics will offer a holistic view of stakeholders’ positions and their impact on consumer journey, user acquisition and conversion. This multi-faceted approach ensures that strategies are data-driven, user-centric, and tailored to different user segments, ultimately leading to improved business outcomes.

The post The Consumer Journey Stakeholders: How User, Shopper, and Influencer Shape Decisions first appeared on Innovation SWAT.

1. Airtable: A versatile platform that combines the functionality of a database with the simplicity of a spreadsheet. It allows users to create custom applications and integrate various data sources without coding. Airtable
2. Tableau: Known for its powerful data visualisation capabilities, Tableau offers a no-code interface for connecting different data sources and creating interactive dashboards. Tableau
3. Google Data Studio: A free tool that allows users to create interactive reports and dashboards by connecting various Google services and other data sources. Google Data Studio
4. Zapier: An automation tool that connects different apps and services without coding. It helps in automating workflows and integrating data from different sources. Zapier
5. Power BI: Microsoft’s business analytics tool that provides interactive visualisations and business intelligence capabilities with an easy-to-use interface for connecting various data sources. Power BI
6. Looker Studio: A data exploration and business intelligence tool that integrates seamlessly with Google Cloud and other data sources to provide comprehensive data analysis and visualisation. Looker Studio
7. Caspio: A no-code platform for building custom database applications that can connect to various data sources and provide robust data analysis capabilities. Caspio
8. Knack: A no-code tool that allows users to build custom applications, manage data, and create reports and dashboards without any programming knowledge. Knack
9. Monday.com: A work operating system that allows users to build custom workflows, integrate different data sources, and visualise data with no-code tools. Monday.com
10. Parabola: A no-code data automation tool that allows users to import, transform, and export data between various apps and services through a visual interface. Parabola
11. Retool: A low-code platform that enables users to build custom internal tools quickly by connecting to various databases and APIs without extensive coding. Retool
12. Quick Base: A no-code application development platform that allows users to create custom business applications, automate workflows, and integrate data from various sources. Quick Base

These no-code tools can help streamline data analysis and provide a user-friendly experience for users of all technical levels.

Pros and Cons of Using No-Code Data Analysis Tools

While it is convenience to use no-code data analysis tools, especially for the non-technical users. However, it is important to be aware of their limitations in terms of customisation, scalability, and performance.

Below are some pros and cons when considering no-code tools for your innovation research.

Pros:

• Ease of Use: No-code tools are designed to be user-friendly, allowing non-technical users to perform data analysis without needing programming skills.
• Speed: These tools enable rapid development and deployment of data analysis projects, which can significantly reduce the time to insights.
• Cost-Effective: No-code tools often have lower upfront costs compared to traditional data analysis solutions, making them accessible for small businesses and startups.
• Flexibility: Users can easily adapt and modify their data analysis workflows without requiring extensive coding knowledge.
• Integration: Many no-code tools offer seamless integration with various data sources and other software applications, facilitating comprehensive data analysis.

Cons:

• Limited Customisation: No-code tools may not offer the same level of customisation and advanced features as traditional coding-based solutions, potentially limiting complex data analysis tasks.
• Scalability Issues: As data volume and complexity grow, no-code tools might struggle to handle large-scale data analysis efficiently.
• Performance Constraints: No-code solutions may have performance limitations compared to custom-built data analysis systems, especially when dealing with high computational demands.
• Dependency on Providers: Users become reliant on the tool providers for updates, support, and new features, which could impact long-term usability.
• Security Concerns: Using third-party no-code tools can raise security and data privacy concerns, particularly when dealing with sensitive or proprietary data.

The post No-Code Data Analysis Tools first appeared on Innovation SWAT.

Nowadays, we can also leverage AI and Machines learning tools to help analyse and identify emerging trends and opportunities to transform business.

Integrated Customer Value Insight Analysis

1. Leverage Advanced Data Collection and Analysis Tools: Utilise automated machine learning platforms, AI tools, and data visualisation software to gather and analyse vast amounts of customer data. Tools such as DataRobot, IBM Watson, and Tableau can help identify key customer trends, sentiments, and preferences by processing data from various sources like social media, online reviews, and customer feedback.
2. Implement Real-Time Market Monitoring: Use customer experience management platforms and social media listening tools to monitor real-time market dynamics. Platforms like Sprinklr, Sprout Social, and Brandwatch allow businesses to track online conversations, understand evolving consumer perceptions, and quickly respond to market changes. This ensures that the business stays aligned with customer needs and market demands.
3. Combine Qualitative and Quantitative Insights: Integrate qualitative trend spotting with quantitative data analysis for a comprehensive understanding of customer value. Use resources like Google Trends, TrendWatching, and WGSN for qualitative insights, and combine them with quantitative data from sources like YouGov, World Bank Open Data, and Statista.

This approach helps to identify not only what customers are doing but also why they are doing it, leading to more accurate and actionable insights.

Automated Machine Learning Platforms

• DataRobot: Assists in quickly creating and deploying predictive models to analyse large volumes of macro data and extract vital customer trends. DataRobot
• Google Cloud AI and Machine Learning: Offers various tools for data analysis, including AutoML for building machine learning models with minimal effort and expertise. Google Cloud AI and Machine Learning
• H2O.ai: Provides an automatic machine learning platform for predictive modelling, analysing macro data to identify customer value trends. H2O.ai

AI Tools for Data Analysis

• IBM Watson: Provides AI tools for data analysis, including natural language processing capabilities to analyse customer sentiments and preferences from various data sources. IBM Watson
• NetBase Quid: Uses AI to analyse text from news articles, blogs, social media, and more to deliver consumer insights and market trends. NetBase Quid
• Brandwatch: Analyses online conversations about brands to understand consumer perceptions and changing values. Brandwatch
• SAS Text Miner: Uses natural language processing (NLP) to explore large amounts of text data and discover new topics, trends, and customer preferences. SAS Text Miner

Data Visualisation and Analytics Platforms

• Tableau: Excels at visualising data and discovering trends, integrating with various AI platforms. Tableau
• Qlik: Offers end-to-end, real-time data integration and analytics solutions to access and transform all data into value. Qlik
• Looker: A business intelligence software and big data analytics platform that helps explore, analyse, and share real-time business analytics. Looker

AI Tools for Customer Data Analysis and Real-time Market Dynamics

The emergence of AI has introduced advanced data analysis and real-time market monitoring tools. Below are some AI tools that can assist in extracting and consolidating in-depth customer value trends from collected macro data, as well as monitoring real-time customer dynamics:

Customer Experience Management Platforms

• Sprinklr: Helps brands listen to customers, learn from them, and show them love across digital channels. Sprinklr
• Sprout Social: Provides social media listening and analytics, social management, customer care, and advocacy solutions. Sprout Social
• Zoho CRM: Uses AI to help businesses predict sales trends, identify anomalies, and derive insights on sales performance. Zoho CRM
• Salesforce Einstein: An integrated set of AI technologies that makes the Customer 360 Platform smarter and brings AI to everyone. Salesforce Einstein
• HubSpot: Offers a full platform of marketing, sales, customer service, and CRM software to help businesses grow better. HubSpot

Social Media and Online Sentiment Analysis

• NetBase Quid: Analyses text from various sources to deliver consumer insights and market trends. NetBase Quid
• Brandwatch: Analyses online conversations to understand consumer perceptions and changing values. Brandwatch
• Sprout Social: Provides social media listening and analytics to gather customer insights. Sprout Social

Predictive Analytics and CRM Tools

• DataRobot: Assists in creating and deploying predictive models for customer data. DataRobot
• Zoho CRM: Uses AI to predict sales trends and derive insights on sales performance. Zoho CRM
• Salesforce Einstein: Brings AI to the Customer 360 Platform for smarter customer insights. Salesforce Einstein
• HubSpot: Offers CRM software to help businesses grow by analysing customer data. HubSpot

In today’s rapidly evolving business landscape, integrating various analytical approaches is crucial to uncover actionable customer insights that drive business innovation. By combining qualitative and quantitative data, leveraging advanced AI and machine learning tools, and maintaining a keen understanding of socio-cultural trends, businesses can gain a comprehensive view of their customers’ needs, preferences, and behaviours.

This holistic customer value insight analysis approach enables companies to identify emerging trends, anticipate market shifts, and develop innovative solutions that resonate with their target audience. Moreover, integrating diverse data sources and methodologies ensures that insights are both deep and broad, providing a solid foundation for strategic decision-making.

Ultimately, the ability to effectively integrate and analyse customer insights empowers businesses to stay ahead of the competition, foster customer loyalty, and achieve sustainable growth in an increasingly complex and competitive market.

The post Transform Your Business with Customer Value Insight Analysis first appeared on Innovation SWAT.

We consolidated 40+ useful resources to help uncovering the future growth.

Macro PESTLE Landscape Research

PESTLE research is a framework used to identify how external factors – political, economic, sociocultural, technological, legal, and environmental could impact a business or a project. Tools often used for this research include online databases, government publications, industry reports, and news articles.

Through these macro landscape research, we aim to identify PESTLE trends. These are changes in consumer preferences, behaviours, societal norms and trends that can impact businesses growth opportunities. It covers macro research in political dynamics, economic and trading information, as well as any social & environmental policy changes etc.

Technology innovation trend also played a critical role in tomorrow’s business. Innovation team need to identify new and upcoming technologies that might disrupt a business or industry. Tools often used include tech industry reports, patent databases, technology-focused publications, which provides insights on the maturity and adoption of technologies.

Below are some useful resources for trends and business innovation trend analysis. While some of the data sources are similar, we categorise them into roughly 3 main categories – Quantitative Numbers Crunching, Qualitative Trend Spotting, Patents & Academic Research.

Quantitative Numbers Crunching

1. YouGov: An international research data and analytics group, providing a broad range of data-driven solutions and custom research services. YouGov
2. World Bank Open Data: Provides free and open access to global development data. World Bank
3. FRED (Federal Reserve Economic Data): A database of over 760,000 economic time series from 96 sources. FRED
4. Pew Research Center: Offers comprehensive research on a wide range of topics. Pew Research Center
5. Asia Pacific Economic Cooperation (APEC): APEC is a regional economic forum established to leverage the growing interdependence of the Asia-Pacific region. It provides useful resources on economic trends and policies in the region. APEC
6. Asian Development Bank (ADB): ADB provides comprehensive data and analysis on development issues and economic trends in the Asia Pacific region. ADB
7. China National Bureau of Statistics: This is the central authority for statistical information and data in China. It provides comprehensive national economic data. China National Bureau of Statistics
8. OECD (Organisation for Economic Co-operation and Development): Provides data and analysis on a wide range of economic and social issues. OECD
9. IBISWorld: Offers industry research reports and analysis on over 700 industries. IBISWorld
10. Statista: An online statistics portal providing data on over 80,000 topics from more than 22,500 sources. Statista
11. Trading Economics – China: Trading Economics provides historical data, forecasts, and news for a wide range of economic indicators. The China section provides information related to China’s economy. Trading Economics – China
12. Euromonitor: Provides comprehensive data and insights on industries, economies, and consumers worldwide. Euromonitor
13. Kantar: A data, insights and consulting company that provides various reports and insights on consumer trends, media trends, and market trends across different cultures and geographies. Kantar
14. Ipsos: A global research and insights organisation, providing strategic insights to companies around the world. Ipsos
15. Mintel: Provides market research, market analysis, and consumer opinions. Mintel
16. Global Reporting Initiative (GRI): Sets standards for sustainability reporting. Provides resources and reports on ESG performance. GRI
17. United Nations Sustainable Development Goals (SDGs): Offers a framework for sustainable development and provides data and reports on progress. SDGs

Qualitative Trend Spotting

1. TED Talks: An excellent resource for insights on the latest trends and innovations across various fields, including technology, design, and the social sciences. TED Talks
2. TechCrunch: A leading technology media property, dedicated to obsessively profiling startups, reviewing new Internet products, and breaking tech news. TechCrunch
3. Google Trends: A free tool from Google that provides data and graphs on the popularity of specific search terms over time. It can be particularly useful for identifying cultural trends based on what people are searching for. Google Trends
4. TrendWatching: An independent consumer trends firm, providing insights into emerging consumer trends that can inspire profitable innovation. TrendWatching
5. WGSN: A trend forecasting company, offering market-leading trend forecasting for fashion and the creative industries, design validation and big data retail analytics. WGSN
6. Pantone: Renowned for its colour matching system and yearly ‘Colour of the Year’, Pantone provides insights into colour trends across industries. Pantone
7. Design Week: An online magazine covering the latest news, trends and happenings in the design world, from graphics to interiors. Design Week
8. Artsy: A platform for collecting and discovering art, Artsy provides insights into emerging artists and art trends. Artsy
9. Dezeen: A leading online architecture and design magazine, Dezeen showcases the latest design projects and trends worldwide. Dezeen
10. Cool Hunting: An online publication that uncovers the latest in design, technology, style, travel, art and culture. Cool Hunting
11. Stylus: A trend forecasting and innovation research service that provides reports on various industries. Stylus
12. Lifehacker: A blog about life hacks and software that can help you to be more productive. Lifehacker
13. Wired: A magazine that focuses on how emerging technologies affect culture, the economy, and politics. Wired
14. Fast Company: A magazine that focuses on technology, business, and design. Fast Company
15. The Verge: Covers the intersection of technology, science, art, and culture. The Verge
16. Gizmodo: A design, technology, science and science fiction website. It features articles on the latest news and developments in the field of technology. Gizmodo
17. ESG News: A website covering news and developments in ESG investing. ESG News

Patents & Academic Research

1. USPTO (United States Patent and Trademark Office): The official source for US patents, trademarks, and design patents. Offers search tools, patent application information, and legal resources. USPTO
2. Espacenet (European Patent Office): A comprehensive database of patents from around the world, including Europe, the US, and Japan. Provides advanced search features and patent family information. Espacenet
3. Google Patents: A free and user-friendly database for searching patents by keyword, inventor, or patent number. Includes patent images, citations, and related information. Google Patents
4. PatentSight: A platform that provides patent analytics, landscape analysis, and portfolio management tools. Offers a variety of subscription plans. Patentsight
5. ACM Digital Library: A comprehensive database of publications in computer science, information systems, and related fields. Provides access to journal articles, conference proceedings, and technical reports. ACM
6. IEEE Xplore Digital Library: A database of publications from the Institute of Electrical and Electronics Engineers (IEEE). Covers topics like electrical engineering, computer science, and telecommunications. IEEE Xplore
7. ScienceDirect : A large database of scientific and technical publications from Elsevier. Includes journals, books, and conference proceedings. Sciencedirect
8. arXiv.org: A repository of electronic preprints in physics, mathematics, computer science, and other fields. Offers free access to research papers before they are published in journals. arXiv
9. Google Scholar: A powerful search engine specifically designed for academic research. Google Scholar
10. Directory of Open Access Journals (DOAJ): A curated directory of open access journals across many disciplines, offering a reliable source for finding high-quality, peer-reviewed research that is freely available. DOAJ
11. ResearchGate: A social networking site for researchers that allows you to connect with other researchers, share your work, and access research papers (some are free, others may require membership). ResearchGate
12. CORE (COnnecting REpositories): A search engine for open access research repositories, providing access to a vast collection of research from universities, institutions, and other organisations. CORE

In-house vs Consulting Firm Innovation Trend Analysis

Traditionally, these macro research and innovation trend analysis are done by business analyst or strategic planning team in an organisation. These research and analysis can be done continuously in-house to inform OGSM plan and business decisions.

While some of the above resources are free of charge, many in-depth reports require subscription fees or additional purchases. Therefore some MNC managements will commission external consulting firms to conduct these research, one for their width of “knowledge base” and two for a “non-bias” consulting report and recommendations.

Pros and Cons compared

There are pros and cons in conducting these innovation trend analysis and research in-house or with external consultants.

The Pros of doing in-house is the knowledge of the industry and own business resources and constraint. However the Cons will be the potential bias of existing success and lack of wider perspectives beyond own industry.

The Pros for hiring external consulting firm is on their width of knowledge. Their cross-industry experience can help identify business innovation opportunities & trends from multiple perspectives. However the Cons will be the lack of in-depth understanding in real-life operation, as well as the organisational constraints that might hinder the recommendation’s execution.

AI empowering Innovation Trend Analysis

We believe all organisation should aim to build in-house Innovation analysis capabilities, especially in the era of rapid changing digital world.

With the maturity of LLM AI, it will empower internal innovation SWAT teams to quickly gather and analyse a wide range of resources and data across different industries. By combining the both the internal and external data stream, innovation SWAT’s Strategist plays a key role in identifying new trends and business growth opportunities faster and better!

Mastering the macro trend spotting is just one part of business innovation capability, next, we will also discuss how to dig deep to harvest customer insights better and faster than your competitions.

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