In this episode of Demand Gen Studio we sat down Chris Silvestri, founder of Conversion Alchemy, to discuss the intersection of AI and marketing.
Chris shares his journey from software engineering to founding Conversion Alchemy, emphasizing the importance of research and understanding customer behavior.
The conversation explores synthetic research, the challenges of traditional customer research, and how AI can enhance marketing strategies. Chris also provides insights into effective tools for customer research and the role of positioning in marketing.
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Background and Career Journey
Before launching Conversion Alchemy, Chris spent a decade as a software engineer in industrial automation. His journey into marketing started almost by accident, driven by a desire for a career that offered more flexibility. That led him to explore online businesses, eventually discovering copywriting. His background in programming and UX gave him a systems-oriented mindset — one he now applies to architecting message strategies that scale across entire organizations.
After leading UX efforts at an agency and observing how real users interacted with digital experiences, Chris merged his love for data-driven research and storytelling to create Conversion Alchemy in 2021. Today, he helps B2B SaaS brands connect the dots between customer insight, message strategy, and copy that converts.
The Importance of Messaging Strategy
When B2B SaaS brands approach Conversion Alchemy asking for new web or email copy, what often needs fixing first is the foundation: customer research, positioning, and messaging strategy.
Without a deep understanding of who the customer is, what the company stands for, and how it uniquely solves problems, even the best-written copy won’t resonate. Messaging strategy acts as the bridge between insight and execution — defining how a company communicates its value across personas, channels, and touchpoints.
Only once those strategic layers are aligned can compelling, conversion-driven copy truly work. This upfront alignment ensures every marketing effort, from ads to web pages, reflects the brand’s positioning and connects authentically with the right audience.
The Role of AI in Marketing Research
Chris highlighted how artificial intelligence is reshaping the way marketers understand their customers. Traditional research, while extremely valuable, is time-consuming, manual, and often limited by sample size. Synthetic research, powered by AI, offers a way to scale those insights without losing accuracy.
By generating “synthetic data” or creating digital twins of real customers, marketers can simulate conversations, test assumptions, and uncover behavioral patterns faster than ever. But the key isn’t to replace human research—it’s to augment it. Combining real-world interviews and data with AI-generated insights allows teams to validate findings, spot trends, and expand their research depth exponentially.
This “augmented synthetic research” gives marketers a richer, more scalable understanding of their audience.
Synthetic Research Methods & Platforms
Synthetic research is redefining how marketers and product teams gather insights—making it possible to test, learn, and scale faster than traditional research ever allowed.
As Chris explained, there are different levels of synthetic research:
- Fully synthetic - AI-generated data
- Partially synthetic - AI-augmented but anonymized real data
- Augmented synthetic research - the most valuable blend of human and AI insight, where real customer data forms the base and AI expands patterns for scale and depth.
Tools like Synthetic Users and AskRally enable structured studies, persona-based simulations, and even pre-launch validation of messaging or creative variants.
Some focus on product development through “agent experience” testing, while others specialize in marketing and messaging research, helping teams refine positioning and copy before going live.
Although enterprise-level solutions can be costly, marketers can still apply the same principles by storing and analyzing customer conversations with AI to build a “living dataset” of insights. Ultimately, synthetic research doesn’t replace human understanding but enhances it, giving marketers a way to build richer context, ask smarter questions, and make faster, more confident decisions.
Purpose-Built Models for Customer Research
As Chris explains, not all AI models will perform the same when it comes to understanding customers. General-purpose tools like ChatGPT or Gemini can be helpful for brainstorming but fall short for serious research. These models weren’t trained with marketing or behavioral data in mind and carry built-in biases from their pre-training.
Purpose-built models, platforms like Synthetic Users and AskRally, are designed specifically for customer research, allowing marketers to simulate structured studies, generate persona-specific insights, and even test messaging or creative variants at scale.
By combining real customer data with these specialized tools, marketers can achieve cleaner, more directional insights and validate their strategies before launching campaigns. This new wave of dedicated AI platforms represents a major step forward, helping teams test, learn, and iterate faster while staying grounded in real customer understanding.
Leveraging AI for Positioning Strategies
When it comes to positioning, Chris emphasizes that AI’s value depends entirely on the foundation you build before using it. The process starts with deep, structured research - interviews with internal teams, customer conversations, competitor analysis, and collaborative workshops that bring product, sales, and marketing together to align on what makes the company truly unique, better, and different.
Once that foundation is solid, AI becomes a powerful amplifier, helping marketers synthesize insights, spot patterns, and validate whether messaging aligns with real customer behavior.
By feeding AI a “world” of structured context, such as market data or customer transcripts, teams can create positioning statements and value propositions that are data-driven yet human-informed. AI then helps test and refine those ideas, ensuring that copy, campaigns, and content stay consistent with the company’s strategic narrative.
In Chris’s view, AI should be viewed as a collaborator that accelerates clarity, reveals blind spots, and helps marketers define positioning faster with greater confidence and alignment across teams.
Practical AI Models for Copywriting
For marketers without a dedicated copywriter, or the budget for an enterprise-level solution, AI can be an incredibly effective creative partner if used with the right intent and tools.
Chris recommends using ChatGPT primarily for brainstorming and ideation, thanks to its strong memory and conversational capabilities.
For analyzing research or to shape messaging strategy, Gemini 2.5 Pro has a larger context window, allowing users to upload long transcripts or survey data and ask detailed, context-aware questions.
Compared to the above models, Gemini is more prone to challenge assumptions instead of simply agreeing which makes it a powerful tool for refining strategy.
Once the groundwork is laid, Claude is a better choice for crafting the final copy, producing more natural, human-like writing that feels ready for campaigns or web pages. Just be sure to feed it all the above research and information to contextualize first.
Together, these tools form a practical AI stack that helps marketers brainstorm smarter, analyze faster, and write more persuasively without sacrificing strategy or quality.
You can find more of Chris's insights via LinkedIn or by subscribing to his weekly newsletter.
Interested in learning more? Check out the rest of the episodes of Demand Gen Studio. We discuss marketing and demand generation topics, with inspiring interviews with thought leaders. See you next time!
