Mastering Artisanal Analytics: My Recommended AI Tools

Introduction

Data analysis is much like sculpting from marble—what you remove is as important as what you leave behind. In a sea of data overload, artisanal analytics is the sculptor’s chisel, stripping away the superfluous to reveal the masterpiece hidden within. This crafted approach to data examines layers beyond the surface, enabling businesses to uncover and act upon the most meaningful insights.

As a person who regularly crafts human-centric research to solve complex business problems, I’m often asked about the nuts and bolts of my methodology. The heart of my solution lies in the realm of what I have dubbed “artisanal analytics”, a meticulously honed process that surpasses mere data crunching to create resonant results. This is not just a technical exercise; it's a form of art, where the data tells a story and the numbers become a narrative.

The Big Data Trap

Yet, many businesses have tumbled deep into the 'big data' trap, amassing an intimidating mountain of customer data and data tools driven by the misguided belief that more equals better. In this relentless pursuit, a jaw-dropping 99.5% of collected data gathers dust, remaining unused and unanalyzed, and 85% of big data projects fail to deliver on their promises. The recent 2024 landscape (see below image) of machine learning, AI, and data tools now features over 2,000 different data tool providers that all do different things, so the analytics industry has become a crowded and chaotic marketplace. Instead of guiding businesses to clarity, it often leads to more overwhelm and confusion—more akin to an endless assembly line churning out more widgets and data noise than any real insightful masterpieces.

Escaping the Endless Assembly Line of Data Tools

What businesses need is a sweeping paradigm shift, from the robotic assembly line of analytics to the conscientious craftsmanship of artisanal analytics. This evolution demands a liberation from the exhausting cycle of data hoarding and mining (i.e. “data is the new oil” mindset) and a new focus on data cultivating, tending and nurturing (i.e. “data is the new soil” mindset) throughout the entire data chain.

Picture a master craftsman assessing their lumber inventory. They're not merely stockpiling every piece of wood in sight — they discerningly select the specific material that aligns with their creative intent. The same principle applies to a celebrated chef, who painstakingly sources the finest ingredients rather than blindly procuring everything. These artisans prioritize quality over quantity, a contrast starkly evident when compared with mass produced alternatives.

In each example, the contrast is clear - artisans focus on intentional, quality-obsessed refinement of specially curated sources. Mass producers prioritize scalable output through mechanized assembly lines. One approach crafts masterworks, the other churns commodities.

In this world of data, we understand that raw data serves as the primary resource, but it's human judgment and skilled labor that truly unlocks its value. Employing this artisan’s mindset to analytics involves careful selection of relevant data and its diligent refinement into actionable insights through a comprehensive, multidimensional strategy.

Crafting Customer Insights

Just as the artistry of a sculptor or a potter unfolds in multiple dimensions, the true genius of artisanal analytics manifests in the smooth integration of multiple dimensions of customer data. Businesses that aim to thrive should not restrict themselves to solely examining big data behavioral datasets — this one-dimensional view fails to capture the vast human context.

Attaining true mastery over customer understanding involves a holistic approach that integrates and bridges gaps between key dimensions that are usually considered in isolation:

1. The X-Axis: The Quantitative Dimension - What They Do: Think of big data as your foundational framework — your X-axis that discloses the "what." It reveals quantifiable patterns, behaviors, and trends observed at scale across vast samples. Sales metrics, engagement data, operational efficiencies - these offer a preliminary understanding but fall short in truly decoding customers without supporting dimensions.

2. The Y-Axis: The Qualitative Dimension - Why They Do It: This secondary axis, the Y-axis, provides the essential qualitative depth that helps demystify the "why" behind the numbers. Techniques like ethnographic research, interviews, and other thick data methods shine a light on emotional drivers, cultural subtleties, and personal narratives that stimulate customer actions beneath the quantitative trends.

3. The Z-Axis: The Hidden Dimension - The Unseen Influences: Tapping into generative AI to analyze underutilized, unstructured "dark data" sources like call transcripts, online chatter and deep web behaviours, the Z-axis uncovers the often unseen and ignored aspects shaping their customer experiences. This dimension opens a window into the hidden realms of perceptions, aspirations, and desires that guide customer thoughts and actions.

4. The W-Axis: The Wisdom Dimension: Timeless Insights over Space and Time: The W-axis integrates the use of generative AI to weave in diverse and universal human wisdom principles, philosophical perspectives, social sciences principles to not only shed light on human behavior but also to brainstorm and simulate creative solutions. This wisdom dimension delivers insight into authentic meaning, resonance, and vision — aligning businesses with deeper universal truths underpinning every customer interaction.

The magic of the approach really unfolds when you harmonize these dimensions into a symphony of understanding. Leveraging generative AI and LLMs, this unified approach breeds innovation, sparking solutions that resonate deeply with customers.

Let's dive into illustrative examples from clients I’ve worked with:

• An online makeup brand that uses big data to identify falling sales of their top-selling products in selected markets (X), discovers a preference for natural, ethical ingredients in those markets through focus groups (Y), when mining their unused online app reviews and testimonials they notice a burgeoning discussion about inclusive beauty and body positivity - indicating an opportunity to rebrand with a focus on self-acceptance and inclusivity (Z), and infuses their marketing and product messaging with resonant philosophies and teachings about inner beauty and self-acceptance from diverse cultures (W) resulting in growth of sales in those markets.

• A fintech startup using analytics identified unexplained high uninstall rates for its money management app (X), through user interviews with churned users they realized there was user anxiety around budgeting and a lack of personalized guidance (Y) by scraping online forums, social media and other deep web data they unearthed a vast underserved demand for financial coaching from “mentors” and topics people wanted to be coached on (Z), and integrated personalised AI coaching as well as the psychology of money and happiness philosophies to address the problem (W) which ultimately reduced the uninstall and customer churn rate.

Through this artisanal four-dimensional perspective, these businesses decoded not only the current 'what,' 'why,' and hidden motivations but also envisioned future scenarios, explored new opportunities, and consistently adjusted their insights to build lasting, impactful customer relationships across all dimensions.

Thriving in the business world requires more than just crunching numbers from big data sets. This one-dimensional approach, though crucial, only scratches the surface of the vast human context. To truly comprehend your customer, you need a holistic approach.

Much like an artist brings a lump of clay to life, each stroke and contour reveals a story; the magic of artisanal analytics lies in bringing together different aspects of customer data to create a complete picture.

Generative AI: The Artisan's New "Swiss Army Knife"

While there will always be a place for some specialized tools, we're witnessing the rise of a powerful new tool—generative AI. Tools like OpenAI 's @ChatGPT, Anthropic 's Claude , 谷歌 Gemini, and those built atop their APIs are akin to a modern-day Swiss Army Knife—a versatile, all-encompassing utility that eliminates the need for disconnected data tools.

With generative AI’s prowess spanning data analysis, qualitative insights, coding, creative brainstorming, and synthesis across disciplines, generative AI could become the centerpiece of your artisanal toolkit.

To maximize the potential of this powerhouse, it's essential to cultivate distinctly human skills. In this AI era, proficient communication (command of natural language, giving clear instructions and feedback, motivational skills), deep understanding of humanities like psychology and philosophy, and the ability to integrate diverse wisdom will thrive.

Ironically, the celebrated 'generalists'—liberal arts, humanities, and creative people—may be better positioned than technical specialists, data scientists, and analysts.

Tech skills are no longer a barrier.

My Advice for Applying the Artisanal Analytic Approach to your Business

1. Handpick Your Tools: Similar to an artisan choosing their materials, select tools that align with your mission and your clients' specific needs. Limit your toolset to only those that truly enhance your insight generation process.
2. Master Your Craft: Deepen your expertise by becoming a master of your tools and keeping abreast of new techniques within your toolset to maintain a high level of craftsmanship. Deep knowledge of a few tools is more valuable than a superficial understanding of many.
3. Develop a Strategic Framework: Artisans work with a vision. For each project, craft a structured blueprint that outlines how each tool will be employed to achieve the desired outcome.
4. Embrace the Human Element: Balance the quantitative output of your tools with qualitative insights and your own expertise and intuition. Just as artisans know their materials, know your data. Develop an intimate understanding of the context, source, and story behind your data.
5. Maintain an Artisanal Mindset: Focus on the quality of insights rather than the quantity of data analyzed. Stay curious and continue to learn, much like artisans who continually refine their skills and techniques.

My Recommended Toolkit:

I often get asked about the best AI tools out there.

My go-to response is usually - it all depends.

Your business objectives dictate the toolkit that best suits your business, nonetheless I've compiled a list of some of the AI tools and use cases I've personally explored or experimented with for my clients.

Important Disclaimer: Before using the tools recommended, please note:

Always anonymize datasets and remove any personally identifiable information before input. Avoid using proprietary or confidential data, as there's a risk of exposure in the event of a data breach.

Always Verify: While tools like ChatGPT are continuously improving, they can generate inaccurate or fabricated information (known as hallucinations). Always verify critical facts through reliable sources, especially when using versions not connected to the internet.

1. Data Interaction and Querying

• Data Querying for Novices: Don’t have coding experience? Don't worry! Tools like Julius AI or DataGPT allow you to chat with your data as if it were an expert analyst. You can also craft your own similar tool using a custom GPT.
• Voice-Driven Data Interaction: Bring your data to life with ChatGPT's voice capabilities, allowing you to analyse your data through dynamic back-and-forth conversations on the go, anytime, anywhere.
• Real-Time Pattern Detection: Simply upload a complex dataset and let ChatGPT leverage Advanced Data Analysis to swiftly uncover hidden patterns, anomalies, new insights, or even spot early weak signals across disparate datasets you've yet to tap into (like owned social media, 'dark social' channels like Slack, customer requests, or call transcripts).

2. KPIs and Decision Support

• Your Personal KPI Coach: Leverage ChatGPT to receive tailor-made advice on how to optimise your KPIs and metrics. It's like having a bespoke mentor right at your fingertips!
• Rapid Idea Generation: Leverage ChatGPT to streamline roadmap decisions and boost A/B test idea generation aligned with your product KPIs.
• Decision-Making Companion: Use ChatGPT to simulate your hypothetical go-to-market scenarios or predict impacts of your strategies and decisions for informed decision-making.

3. Personas and Audience Insight

• Persona-Centered Data Stories: Automatically spin compelling data narratives, customised to each audience persona within your business.
• Create Simulated User Personas: Utilize LLMs to emulate diverse user personas, testing your concepts, or use existing tools like Synthetic Users , PersonaPanels , Yabble? or OpinioAI .
• Prospect Profiling: Harness tools like Humantic AI to comprehend your prospect profiles and foresee potential sales objections for seamless customer interactions.

4. Customer Journeys and Feedback

• Virtual Customer Feedback Panels: Rapidly assemble customer panels with Qualzy , Bulbshare , Wynter , Attest and more to test your ideas and concepts with real customer insights in real-time.
• AI Customer Journey Companions: AI-agents like Struct can conduct "courtesy calls", gathering personalized feedback for valuable insights across the buyer journey. Alternatively use pop-up or conversational chatbot surveys using tools like Hotjar | by Contentsquare , Formbricks , Voiceflow , RhetorAI.com or Streetbees .
• Emotion AI User Testing: Combine video survey tools like VideoAsk or Voxpopme with emotion AI platforms like Affectiva , AffectLab and Heartbeat.ai to gauge user emotions, body language non-verbal cues during product testing.

5. Qualitative Research and Ethnography

• AI-Boosted Digital Ethnography: Utilise Claude’s and ChatGPT’s computer vision capabilities to glean deep cultural insights from visual data. You can also train these LLMs to provide anthropological expert-level cultural insights at your disposal.
• Build an AI Research Assistant: Employ services like Artisan.co , Aphid.com and Relevance AI to automate daunting research tasks. It's like a 24/7 research team member who never sleeps!
• Fast-Track AI Moderated Research Interviews: Tools like Outset and heyjuno.co use an AI to help you conduct extensive interviews incredibly quickly, unveiling in-depth customer perspectives.

6. Market, Category and Community Analysis

• Deep Community Listening: Utilize AI-powered or no-code web scraping tools (e.g. getmagical.com , simplescraper.com and more) or niche community search tools (e.g. groouply.com , gummysearch.com , SparkToro , Rephonic.com PodEngine.com , Syften.com ) to identify and track niche or influential communities to discover emerging memes, influencers, subcultures, trends, and movements.
• Transcribable Insights: Transcribe various audiovisual content (e.g. meetings, events, webinars, sales calls, earnings calls, podcasts, video content) with tools like Otter.ai or Fathom - AI Meeting Assistant to discover hidden gems or untapped needs from verbal discussions.
• Digital Mystery Shopping: Use browser extension tools like Jungle Scout Tool , VidIQ , Helium 10 , and Keywords Everywhere to mine consumer search queries, comments, questions and reviews for your competitors and wider category across platforms like YouTube, Shopify, and Amazon

So there you have it! Are you ready for this future?

If you’re ready to craft data solutions that truly matter, follow me for more tips and start by adopting one tool or generative AI approach from my artisanal data toolkit this month and see the difference it makes.

If you feel inspired to explore what artisanal analytics can do for your business but are not sure where to start - reach out for a free 1:1 consultation, and let’s craft a data strategy that is as unique as your business.

Either way, join me in revolutionizing how we approach data.

It’s time to transform numbers on a screen into stories that resonate and inspire action.