At the MarTech conference in November, experts from Citrix, Randstad Digital and Qualified Digital joined Susan Ferrari, senior director at EmotionTrac, for a discussion on how AI is transforming market research and planning.
Panelists for the session included Brian Madden, futurist at Citrix; Steve Bevilacqua, Principal Consultant, Cella from Randstad Digital; and Katie Templin, Chief Experience Officer, Qualified Digital.
From data overload to signal clarity
Marketers no longer have trouble finding data; they have difficulty understanding it. The panelists agreed that the real value of AI is not in collecting morebut in uncovering better insights faster and more efficiently.
1. Comparative analysis, well done
Templin uses tools like ChatGPT to perform quick competitive scans and directional analysis. “It’s great to see how competitors are positioned in the market,” she said. “But I always verify the sources. I never rely solely on AI.”
She emphasized the importance of understanding where data comes from and using AI only as a guide to where deeper research is needed.
2. The ‘deep research’ mentality
Bevilacqua recommended activating “deep research” or “smart analysis” modes in AI tools when performing market scans. His process consists of two phases: a broad step into GPT or Copilot, followed by deeper dives using niche tools like Speak AI for conversation analytics or YouScan for social monitoring.
“Don’t forget the data you already have,” he added. “Before you look for new input, dig into your CRM, your customer conversations, your social feedback – it’s all there.”
3. Synthetic focus groups
Madden sees AI as a replacement for audience testing. “It’s like having a focus group of a million people that doesn’t need snacks,” he joked. Assigning LLMs roles as “CIO at a SaaS company,” he tests messages and collects multiple responses with just a few innovations.
“It’s not perfect,” he noted, “but it’s fast, guiding and often surprisingly human.”
Convert unstructured data into actionable insight
AI’s ability to handle unstructured data – audio transcripts, open-text surveys, social posts – was a recurring theme.
Bevilacqua highlighted tools such as Brandwatch for visual social listening and Dovetail for quality coding. “We can now uncover pain points or themes in hours instead of weeks,” he said.
Templin’s agency uses a private, sophisticated AI system for proprietary data. “We combine large and small models depending on speed and cost,” she says. “But we always keep people informed to investigate the results.”
Securely collect customer and competitor data
Ferrari and Templin both emphasized that data management is important. When analyzing internal or customer data, use private or sandbox models to protect confidentiality.
Ferrari also mentioned using simple tools like web scrapers to get an external impression of how a brand is perceived online – an inexpensive way to supplement internal data sets.
For competitive research, Madden shared a clever twist: “I feed competitors’ blogs, press releases, and videos into AI and tell it, ‘You’re a product strategist at this company – what’s your next step?’ It’s like opening their playbook.”
Bevilacqua added that real-time monitoring tools like Visualping can alert marketers to competitor price changes or changes in messaging as they occur.
Campaign execution at AI speed
The impact of AI on campaign design is primarily about speed and adaptability.
Templin described how AI helps her team run based on live data. “We can analyze campaign sentiment and behavioral changes in real time,” she said. “If something shows a negative trend, we activate human help or shift the message immediately.”
She also uses AI to automate the next-best action logic, deciding when to move a prospect to sales or another nurture path. The result: higher engagement and better retention.
Use AI without losing your critical eye
The panel agreed: AI is powerful, but it can be overly pleasant. “He’s like a cheerleader: he always tells you you’re brilliant,” Ferrari joked. Bevilacqua warned about “AI psychosis,” the pitfall of believing your model’s flattery.
Instead, he suggested asking why an idea can fail or asking for pros and cons to balance optimism. Templin added: “Always check sources. AI will make up quotes as necessary.”
Madden pointed out that bias is less risky if you use AI for brainstorming, not final decisions: “It’s another input – not the final authority.”
Dig deeper: how to get genAI to say it doesn’t know
Smarter cues, cleaner context
When sessions get long, Madden recommends resetting the context: “Ask the model to summarize the conversation to themselves and then start over.”
He also gave a preview of what’s next: longer context windows and “persistent memory” in upcoming GPT releases – features that will make persistent analysis easier.
During the session, panelists mentioned several popular AI platforms:
- Gamma: Transforms notes into brand presentations
- NotebookLM: Creates summaries or podcasts from URLs or documents
- Brandwatch and YouScan: Analyze brand perception and visuals
- Speak AI, Dovetail: Transcribing and Thematizing Qualitative Data
- Visualization: Monitors changes to competitors’ sites
- Revven: Turns simple prompts into “super prompts”
Key Takeaways
- AI accelerates insight, not just output. Use it to synthesize signals and test hypotheses quickly.
- Keep a person informed. Check reasoning, refine clues, and verify data sources.
- Use LLMs as living personas. They can simulate audience responses for testing messages.
- Optimize for generative search. Publish content that answers the questions chatbots will surface.
- Speed is the hallmark, but management is the control.
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