Andrew, 32
Living situation
- Age: 32
- Political affiliation: Centre
- Type of innovation adaptation: Early majority
- Professional and financial situation
- Employment Status: Data Engineer, Full-Time Employee
- Household income: $75,000 to $99,999
- Highest degree: Master of Science (Data Science)
- Housing situation
- Place of residence: Anaheim
- Region: California
- Type of residence: Freehold house
- Marital status
- Single / unmarried
- Household size
- 1 person
- Effects of economic circumstances
- My cost of living has risen noticeably
- I have tried to spend less money
Personal characteristics & attitudes
- Hobbies and interests
- Technology & Computers
- Video games
- Outdoor activities
- Important aspects of life and values
- A happy relationship
- New Learning
- Lead an honest and respectable life
- Attitude towards innovation
- I like to stay technologically up to date
- I like to try out innovative products
- It's important to me to always have the latest technology
- Food attitudes
- I actively try to eat healthy
- Attitudes towards digital media
- Best picture and sound quality is important to me
- I prefer to subscribe to a bundle of streaming services
- I like watching movies and series on my smartphone
- I prefer platforms that offer personalized recommendations
- Attitudes towards personal finances
- I am well informed about my financial situation
- I would like to see more payment options using cryptocurrencies
- I could imagine handling all financial matters exclusively online
- Internet settings
- I really appreciate having mobile internet access everywhere
- I am well informed about the topic of cyber security
- Attitudes towards services
- I like to use AI tools (such as ChatGPT) to handle everyday tasks
- I am happy to pay for services that make my life easier and more convenient.
- I tend to book services and services online
- Attitudes to travel
- When I travel, I look forward to unique experiences
- I like being in nature when I travel
- I book travel services on the fly using my smartphone
- Settings for consumer electronics
- I would like to control my house by smartphone or voice
- I want the best audio and movie experience on all my devices
- Settings for insurances
- I trust my insurance company to take care of my claims.
Andrew in Detail
Life and character
Andrew is 32 years old, single, and lives alone in a one-person household in a home he owns in Anaheim, California—in the tech-savvy southern part of the state. As a data engineer, he works full-time on data pipelines, platforms, and infrastructure for a regulated manufacturer; at the decision-making level, he is both a user and a technical specialist—the person who builds data architectures, evaluates and deploys tools, and whose technical judgment carries significant weight in the selection process, even without a large budget of his own. He holds a Master of Science in Data Science, is part of the “early majority” when it comes to innovations, and has a household income between $75,000 and $99,999. Politically, he identifies as centrist—and, in light of a noticeable rise in the cost of living, has recently made a conscious effort to spend a little less.
At his core, Andrew is a curious, eager-to-learn techie whose understanding of value and risk hinges entirely on the quality of data and systems: A faulty pipeline, inconsistent or incomplete data, an unstable job, or a security vulnerability mean not only trouble for him, but also incorrect analyses, late-night emergency calls, and tedious debugging. That’s why he thinks in terms of data quality, scalability, and reliability—and evaluates tools based on whether they can handle heavy loads, integrate seamlessly, and run reliably in production, rather than just looking good in a demo.
What drives him is the desire to learn new things and work with the latest technology. Technology and computers are both his profession and his passion; he likes to stay up to date, tries out innovative products, and it’s important to him to always have the latest technology—yet, as an early adopter, he combines this enthusiasm with a critical eye. Learning new things is one of his core values; accordingly, he naturally uses AI tools like ChatGPT for research, technical support, professional development, and communication. He is well-informed about cybersecurity—an issue that directly impacts his day-to-day work in today’s data-driven, interconnected world.
His digital daily life is demanding and tightly scheduled. He wants mobile internet access everywhere; he values the best picture and sound quality on all his devices, bundles his streaming services, and prefers platforms with personalized recommendations. He’s fully on top of his finances, can imagine managing them entirely online, and even wants more payment options using cryptocurrency—a sign of his strong affinity for early-stage tech. He’s happy to pay for services that make everyday life easier and signs up for them online; however, he consistently filters out ads: Online ads often annoy him, so he uses ad blockers.
Andrew seeks a balance between screen time and nature. For him, video games are just as much a part of this as outdoor activities and travel, during which he seeks out unique experiences, enjoys spending time in nature, and spontaneously books travel services via his smartphone. When it comes to food, he pays attention to his health. He’s intrigued by smart home controls via voice commands or apps. When shopping, he enjoys browsing for fun but keeps an eye out for special offers; he thoroughly researches major purchases online, relies heavily on customer reviews—and prefers to have his orders in hand the very same day.
What motivates him - what drives him?
Values & attitude:
For Andrew, what matters is that things are done technically sound and honestly. A happy relationship, learning new things, and living an honest, respectable life are his core values. Applied to his work, this means he wants to build data solutions he can be proud of—robust, scalable, well-documented, and traceable. Half-baked solutions that break down later in production go against his standards. He earns respect through technical merit and openness, not through marketing promises.
Goals:
Professionally, Andrew wants to build data infrastructures that operate reliably, scalably, and securely—and that make analytics, AI, and better decision-making possible in the first place. Specifically, this means stable, automated data pipelines; high data quality and integrity; seamless integration of heterogeneous sources (from production data to business systems); and high-performance, easily maintainable platforms. He wants to grow professionally, make effective use of modern technologies (cloud data platforms, streaming, AI/ML infrastructure), and create systems that not only work today but also scale to meet future requirements. In the pharmaceutical and manufacturing context, this means ensuring that end-to-end digitalization is sustainable from a data perspective—from data capture in production to an analyzable, compliance-compliant database. Key metrics for him include pipeline reliability and uptime, data quality and integrity, processing performance and latency, error/failure rates, as well as the scalability and security of his systems.
Pain Points / Challenges:
Three things cause him the most frustration: first, poor data quality, data silos, and inconsistent sources, which make any proper processing difficult; second, unstable or poorly documented tools, proprietary formats, and the threat of vendor lock-in, which make integration and maintenance a struggle; third, scaling, performance, and security requirements under time and cost pressure, including integration with established legacy systems. Added to this is the constant challenge of reliably and securely consolidating heterogeneous sources—especially in industrial environments with OT data. He is highly skeptical of marketing buzzwords, a lack of technical depth, and hard-to-find documentation; a provider quickly loses trust if its tools fail to deliver in practice what the spec sheet promises.
Digitalization & Technology Adoption:
Andrew is at the heart of digital transformation—he builds the data foundation that makes it possible in the first place. Cloud and big data platforms, streaming and real-time processing, AI/ML infrastructure, IT/OT data integration, and the automation of data flows define his day-to-day work. As an early adopter with a strong passion for technology, he is exceptionally open to new ideas—provided they are mature, secure, scalable, and easily integrable. He thinks in terms of architectures and data flows rather than individual tools and prefers open, well-documented solutions that integrate seamlessly rather than creating new dependencies. The fact that he already makes extensive use of AI in his personal life makes him an early, competent professional user and advocate of such technologies.
How does he inform himself?
Media and information behavior:
Research is second nature to Andrew—thorough, digital, and quality-driven. Before making major purchases, he first does extensive research online; customer reviews are very important to him. For product research, he uses search engines, customer reviews, online stores, brand websites, and—typical for a developer—online forums where other practitioners share their real-world experiences. While working, he looks for solutions in technical documentation and API references, in developer communities (such as GitHub or Stack Overflow-style forums), through tutorials, tech blogs, and open-source projects. Outside of work, he stays up to date primarily through a mix of podcasts (preferably science & technology and comedy) and YouTube—traditional print and TV media play hardly any role for him.
Channels & formats:
Andrew can be reached digitally via social media, video platforms, online stores, and search engines—traditional touchpoints like television, radio, movie theaters, or local stores have only a marginal impact. Important: He actively filters out advertising (ad blockers, skepticism toward ads), which is why crude ads rarely reach him; he’s persuaded by content and practical value. On social media, YouTube, Instagram, and X (Twitter) are the channels he actively uses: He likes and follows people such as content creators, comments on posts, and sends private messages. In terms of content, formats with real substance resonate with him: technical deep dives and architecture-related content, tutorials and hands-on demos, comprehensive documentation and code examples, benchmarks and honest comparisons, tech talks, and open-source references—as long as the content goes in-depth and can be tried out immediately.
Credibility & Trustworthy Voices:
For Andrew, credibility comes from what is technically substantiated and verifiable: comprehensive documentation, open specifications, reproducible benchmarks, and honest discussion of strengths and limitations. He trusts the opinions of other developers and data professionals most of all—peers on his team, experienced users in communities and forums, as well as competent tech experts and open-source maintainers. Glossy marketing leaves him cold; an active community, good technical support, and transparent code are strong indicators of trust for him.
Communication style:
When speaking with Andrew, you should be objective, technically precise, and on equal footing—engineer to engineer. He wants facts, specifications, architectural details, and examples rather than buzzwords, and he sees right through marketing jargon. He is very open to modern, data-driven, and AI-powered approaches, as long as they demonstrate technical merit, scalability, and security. He feels most comfortable when communication helps him arrive more quickly at a clean, working solution.
Which messages work?
The messages that resonate most strongly with him are those that take his engineering standards seriously: “Clean, well-documented, and scalable data architecture will save you nights of debugging down the road.” He finds value-based arguments such as the following particularly convincing:
* “Here’s how to build reliable, scalable data pipelines—using robust technology, open standards, and comprehensive documentation.”
* “Here’s how to seamlessly integrate heterogeneous sources—from IT to OT—without data silos or proprietary lock-in.”
* “How to ensure data quality and integrity throughout the entire process—as the foundation for reliable analytics, AI, and compliance.”
* “How to Create a High-Performance, Secure Platform for Analytics and AI—One That Grows with Your Needs.”
* “Here’s how to stay flexible instead of feeling trapped—with open, scalable solutions and a strong community.”
He’s always willing to try something new—as long as the quality is there: reliable technology, good documentation, open standards, and support or a community that really helps when it counts. What he won’t accept are empty promises, a lack of technical depth, or tools that create more problems than they solve in a production environment.
Which tonality fits?
In terms of tone, Andrew is best described as technical, fact-based, and free of marketing hype—the tone of a good technical white paper or a strong engineering blog. Instead of buzzwords, we need accurate terminology (pipeline, data lake/warehouse, streaming, IT/OT, data integrity), precise statements, and verifiable examples. The ideal style is one that demonstrates depth while remaining clear: concrete, honest, with specifications, benchmarks, and practical relevance, as well as clear prioritization (data quality, scalability, and reliability as core priorities; clean integration as a prerequisite; security always taken into account; openness and no lock-ins as anchors of trust). Self-service via good documentation, tutorials, and code is preferred—but in an emergency, what counts is an accessible, competent point of contact and fast technical support.
How does he make decisions—and who else is involved in the decision-making process?
Purchase criteria:
When evaluating new tools, three things are most important to Andrew: first, technical quality, scalability, and performance under real-world load; second, the ability to integrate via open standards, interfaces, and comprehensive documentation, without vendor lock-in; and third, data security and integrity, as well as a strong community and good technical support. In addition—partly because of his deliberate cost-cutting measures—he looks for a fair price-performance ratio and transparent terms.
Risks to be insured against:
Before making a decision, Andrew wants above all to rule out unstable pipelines and outages, a lack of scalability, data quality and integration issues, security and compliance gaps, and dependence on a single vendor. The more convincingly a vendor demonstrates—through documentation, benchmarks, and open standards—that the product’s technology, security, and support can hold up in production, the more likely it is to win Andrew’s trust.
Buying Center & Organizational Obstacles:
Formally, Andrew rarely makes budget decisions on his own—but his influence as a technical gatekeeper is significant: anything he rejects on technical grounds stands little chance of being approved. The buying center typically includes his supervisor or the data/engineering lead, the data scientists and analysts (who serve as internal “customers” of the data platform), IT and security managers, and the procurement team. Andrew provides the technical evaluation and feasibility assessment and is often the one who recommends—or rejects—a solution internally. Typical organizational hurdles include limited budgets and cost-cutting mandates, established legacy systems, compatibility and integration constraints, tight evaluation timelines, and friction between the IT, OT, and data worlds. Messages are therefore most effective when they provide him with the technical arguments he needs to convincingly advocate for a solution internally.
Positioning in the Big Five model
Openness: 9
Relevant adjectives: very curious, eager to learn, tech-savvy, eager to experiment, and committed to quality
Conscientiousness: 8
Relevant adjectives: thorough, quality- and detail-oriented, strong in documentation and process management, reliable in execution
Extraversion: 3
Relevant adjectives: somewhat reserved, results-oriented, active in tech and online communities, focused on substance rather than appearance
Compatibility: 6
Relevant adjectives: cooperative, honest, team-oriented—expects substance and openness in return
Neuroticism: 3
Descriptive adjectives: generally level-headed and solution-oriented, safety-conscious, seeks stability and reliability
Media use & consumption
- Digital advertising touchpoints
- Social media
- Video platforms
- Online shops
- Search engines
- Settings for online advertising
- Online ads often annoy me
- I use an ad blocker on the Internet
- Non-digital advertising touchpoints
- On TV
- Directly in store
- On the radio
- At the cinema/movie theater
- Use of publishing media (last 12 months)
- Podcasts
- Preferences for podcast content by genre
- Comedy
- Science & Technology
- TV usage by duration (per week)
- 6 to 10 hours
- Preferences for films and series by genre
- Comedies
- Docs
- Dramas
- Sports
- Use of social media by brand
- YouTube
- X (Twitter)
- Activities on social media
- Liked posts from other users or followed people
- Private messages sent
- Commented on posts
- Posts from influencers/content creators liked or followed
- Products/topics talked about online
- Computer, smartphone & technology
- Games / Video games
- Movies & Series
- Sports
- Use of AI
- Online research
- Technical Support
- Education + Learning
- Messaging and communication (e.g., emails, text messages, translations)
- Internet access by type
- Landline Internet connection (e.g., DSL, cable, fiber optic)
- Mobile data connection (e.g., 4G, 5G, smartphone hotspot)
- Shopping Settings
- I like to go shopping just for fun
- When I shop, I look out for special offers
- Online shopping settings
- Customer reviews on the internet are very helpful
- Before making any major purchases, I always do some research online first
- I'd like to receive my purchases the same day
- Sources of inspiration for new products
- Social media
- Online shops
- Search engines (such as Google)
- Information sources for product research
- Search engines (such as Google)
- Customer reviews
- Online shops
- Brand websites
- Online forums
- High brand awareness by category
- Smartphones
- PCs and laptops
- Clothing
- Shoes
- TV + Hi-Fi
- Smartphone by brand
- Apple
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