Precision marketing—utilising in-depth insights into consumer behaviour and preferences to send the right messages to the right people at the right time—makes customers feel valued and understood. Done effectively, it enhances customer satisfaction, fosters loyalty, and drives spending.
However, what happens when businesses lack access to the necessary data or the budget to acquire it? Synthetic data, generated by artificial intelligence models trained on real-world data, provides a scalable and cost-efficient alternative.
Growing by 31% annually, the synthetic data generation market is predicted to reach a value of $2.3 billion by 2030—and for good reason. A game changer for personalised marketing and customer acquisition, synthetic data enables businesses to model and validate scenarios where real-world data is inaccessible, reducing the time, cost, and access to strategic marketing insights.
The business advantages of using synthetic data
Affordable hyper-personalization
Demographics, interests, purchasing habits, communication preferences, and customer motivations can all significantly impact the effectiveness of a marketing approach.
With 69% of consumers stating that the relevancy of messaging impacts their perception of a brand and 80% more likely to purchase from companies offering personalised experiences, considering the often subtle differences between consumers is paramount. However, the effectiveness of precision marketing hinges on the data available.
Traditional data collection processes are time- and resource-intensive. Not only must businesses wait for the required data to become available, but ensuring data quality, navigating regulatory compliance, and storing vast amounts of information all significantly drive up costs. Unsurprisingly, 64% of marketers cite a lack of budget as the biggest barrier to personalisation.
Synthetic data, on the other hand, presents fewer challenges. It can be generated on-demand, is pre-labeled, and enables regulatory hurdles to be sidestepped—reducing the costs by up to 99% compared to traditional methods and providing a cost-effective pathway to greater personalization.
Rapid and scalable development
With consumer tastes and expectations constantly evolving, traditional data collection methods often fall short. Collecting data can take months, by which point consumer preferences may have shifted, leaving businesses with insights that offer little value. Even for companies with vast reserves, 25-30% of that data becomes inaccurate each year, with customer insights based on outdated information likely to produce poor results.
Time is of the essence, and synthetic data offers a clear advantage. It allows businesses to generate targeted datasets within minutes, enabling marketing strategies and AI models to be rapidly iterated and tested.
Nike, for instance, used synthetic data to build the initial proof-of-concept for its generative AI-powered shopping assistant, which customises product recommendations based on each customer’s preferences, purchase history, and user profiles, while continuously learning and adapting with every interaction. Within just 21 days—while many projects would still be in the data collection phase—the product was ready to launch, underscoring the potential of synthetic data to accelerate innovation.
Niche data generation
Reaching niche consumer segments has long proven challenging for businesses, given the data required to target them is often limited and acquiring it may not be cost-effective. This compels businesses to work with incomplete datasets or overlook these segments altogether.
However, with synthetic data generation, businesses can leverage their limited real-world data to create targeted datasets that fill the gaps in their data reserves. This enables the creation of virtual personas for consumers of any age, gender, location, and socioeconomic status—segmented further according to their hobbies, social media usage, products they regularly buy, and much more.
While synthetic data generated using limited real-world insights raises concerns about authenticity and reliability, AI is already capable of delivering data that matches real-world results 95% of the time. At the very least, this data can serve as a critical starting point to pilot campaigns tailored to niche audiences, generating authentic data in the process.
Consistent and high-quality data
Not all data is created equally. Real-world data is often fraught with inconsistencies, whether caused by poor survey design, fraudulent inputs, or human error. Adding unnecessary complexity and scale to data ecosystems, these troves of unusable data cost organizations $12.9m annually on average.
Synthetic data offers a more reliable alternative. Generated using limited amounts of real-world data that has already been cleaned, the resulting datasets are far more consistent, which reduces the risk of segmenting and targeting customers based on flawed data.
Likewise, as consumers grow increasingly wary of allowing businesses to utilise their data—with only one in ten fully trusting organisations to handle their personal information responsibly—accessing up-to-date, high-quality data becomes even more challenging. This shift will drive an even greater reliance on synthetic data, with 71% of marketers anticipating it will make up more than half of all data collected within the next three years.
Enhanced retention
Synthetic data can enhance marketing efforts by not only attracting new customers but also ensuring existing ones remain loyal, helping brands to anticipate churn and intervene before revenue is impacted.
While anonymised data models perform poorly at predicting churn, research shows that synthetic data models produce results similar to those trained on original, real-world data.
Many leading organisations are already using synthetic data to enhance their retention strategies. For example, Netflix uses synthetic datasets to train its churn prediction models, identifying when users are likely to unsubscribe. With this insight, the company can be proactive, delivering personalised offers and content suggestions designed to re-engage subscribers and reduce churn—maximising the lifetime value of each customer.
Making precision marketing accessible and affordable
It’s no secret that data-driven marketing aids decision-making, improves the customer experience, and, in turn, increases return on investment. Yet, a successful strategy is only as good as its underlying data.
A lack of accessible, high-quality data remains a significant barrier to precision marketing, with only 38% of marketers reporting they have sufficient customer segmentation and personas to guide their decision-making.
However, synthetic data is helping businesses to overcome this obstacle—providing a rapid and cost-effective solution to fill the gaps in data reserves and produce the insights businesses need to keep up with the ever-changing consumer.
Author: Ken Tantsura is the Vice President of Innovations at Customertimes, a leading digital consultancy helping businesses overcome their most complex technological challenges. Full bio here.
