Category: Speed to Action

This article series explores the role of synthetic research and data in this speed to action.

By Greg Kihlström

Synthetic methods in marketing begin with data that never existed in a CRM yet behaves as if it did. Gartner frames synthetic data as information “generated by applying a sampling technique to real-world data or by creating simulation scenarios where models and processes interact to create completely new data”. Put simply: machines spin up plausible records—behaviors, preferences, even quotes—without lifting a single row from a customer database. For our purposes, “synthetic” also covers personas and research protocols that originate in algorithmic models rather than focus-group recruitment lists.

While anonymized or de-identified data strips away direct identifiers, it can still be reverse-engineered. Synthetic artifacts avoid that headache because they mirror statistical patterns without sharing a molecule of the original substance—a distinction MIT Sloan highlights in its discussion of privacy-safe data generation and Gartner’s projection that 60% of AI-ready data will be synthetic by 2024. In other words, marketers get the flavor without the fingerprints, satisfying regulators and risk officers in one swoop.

Synthetic outputs come in three broad flavors: structured tables for tabular modeling, unstructured text or images for qualitative insight, and multimodal bundles that fuse clicks, sentiment, and context into a single sandbox. A 2024 SAS report (drawing on Gartner research) estimates that by 2026 three out of four enterprises will lean on generative AI to create synthetic customer data, underscoring just how mainstream these techniques are becoming.

Generative AI adds rocket fuel to the mix. Forrester notes that GANs and VAEs now shape synthetic datasets that “capture real-world patterns and scale efficiently, improving analytics accuracy and speed” while keeping analysts on the right side of privacy law. The result is less time begging Legal for clearance and more time stress-testing new offers before your competitors have finished their coffee.

With definitions, distinctions, and use cases in hand, we can turn to the next logical step: building personas that move as fast as your data. In future articles, we’ll explore how synthetic personas give teams a living, breathing test market—without the airfare, incentives, or awkward small talk.

References

https://www.gartner.com/en/information-technology/glossary/synthetic-data

https://mitsloan.mit.edu/ideas-made-to-matter/what-synthetic-data-and-how-can-it-help-you-competitively

https://www.sas.com/content/dam/SAS/documents/infographics/2024/why-synthetic-data-is-essential-for-your-organizations-ai-driven-future-113845.pdf

https://www.forrester.com/report/synthetic-data-for-customer-insights/RES182031

This article series explores the role of synthetic research and data in this speed to action.

By Greg Kihlström

Competitive advantage hinges on speed. Decision cycles that once spanned quarters are now expected to resolve in days—sometimes hours. By the time a team syncs up for their weekly status meeting, the moment of opportunity may already have passed. This isn’t hyperbole; it’s the new operating reality. Gartner’s 2024 CMO Spend and Strategy Survey found that “demonstrating ROI” (69%) and “revenue generation” (59%) are marketers’ top priorities, yet average marketing budgets have dropped to 7.7% of total company revenue—a 15% year-over-year decline (Gartner, 2024). This combination of shrinking resources and rising expectations leaves little room for drawn-out research cycles. Insight delayed is advantage denied.

More fundamentally, time—not money—has become the most constrained resource in enterprise decision making. McKinsey reports that senior executives spend nearly 40% of their working hours making decisions, yet over 60% of that time is considered ineffective. For a typical Fortune 500 company, this inefficiency can cost up to $250 million annually in lost productivity (De Smet, Jost, & Weiss, 2023). The implication for marketing teams is clear: every hour spent wrangling unstructured data from a legacy dashboard is an hour not spent crafting meaningful, timely experiences. Synthetic data and personas—by generating ready-to-analyze, “plausible people” on demand—help eliminate that bottleneck. Instead of waiting weeks for customer feedback or panel insights, marketers can simulate interactions and test hypotheses within hours.

At the same time, customers have little patience for brand experiences that feel generic or out of step with their needs. As Harvard Business Review has cautioned, over-reliance on slow, outdated inputs leads to “engineered insincerity”—where automation mimics empathy but fails to deliver relevance (Scheibenreif, 2023). Personalization at scale only works when underlying signals are updated continuously. Otherwise, yesterday’s segmentation becomes today’s churn trigger. This makes the case not just for synthetic speed, but for synthetic adaptability. As this book will argue, synthetic personas and data—when governed responsibly—enable marketing teams to move at the pace of customer expectation, not just campaign calendars.

References

De Smet, A., Jost, P., & Weiss, L. M. (2023, August 30). How to make better decisions in the age of urgency. McKinsey & Company. https://www.mckinsey.com/featured-insights/mckinsey-guide-to-excelling-as-a-ceo/how-to-make-better-decisions-in-the-age-of-urgency

Gartner. (2024, May 29). CMO Spend Survey 2024: Proving ROI and Driving Growth With Less. Gartner, Inc. https://www.gartner.com/en/marketing/research/cmo-spend-survey

Scheibenreif, D. (2023, October 2). Personalization at Scale Requires Real-Time Customer Data. Harvard Business Review. https://hbr.org/2023/10/personalization-at-scale-requires-real-time-customer-data