What is Synthetic Data?
Synthetic data is artificially generated data that replicates the characteristics of real-world data. It is created using advanced algorithms and models, enabling businesses to simulate realistic datasets without relying on actual events or personal information. This approach not only protects privacy but also provides a versatile tool for various applications in marketing and commercial functions to simulate and guide strategy and execution. Prior to the recent Artificial Intelligence broader awareness, this was often done with mock data sets. Be aware as you move forward to discern between mock models, and the more advanced and industry future direction of – AI-generated synthetic data.
Gartner predicts by 2030 most of the data used in AI will be artificially generated by rules, statistical models, simulations or other techniques.
“The fact is you won’t be able to build high-quality, high-value AI models without synthetic data. – Gartner
Impact on Commercial and Marketing Functions
Synthetic data can revolutionize how companies approach marketing and commercial strategies. Here’s how:
- Enhanced Personalization: By creating detailed customer profiles and behaviors, synthetic data allows for highly personalized marketing campaigns.
- Scalable Testing: Companies can run extensive A/B tests and scenario simulations without the constraints of limited real-world data.
- Cost Efficiency: Reduces the need for expensive data collection processes.
- Privacy Compliance: Helps avoid privacy issues by using non-sensitive synthetic data instead of real customer data.
A Few Real-World Use Case Examples
Customer Segmentation and Personalization
A large retail company wants to enhance its customer segmentation to deliver more personalized marketing campaigns. By generating synthetic data that mirrors the purchasing behaviors and preferences of their existing customers, the company can create detailed customer profiles. This allows them to tailor marketing messages and promotions to specific segments, improving engagement and conversion rates.
A/B Testing at Scale
A financial services firm aims to test different marketing messages to identify the most effective one. Using synthetic data, they can simulate various customer interactions with different versions of their ads. This enables them to conduct large-scale A/B tests without risking real customer relationships, ensuring they choose the most impactful message before a full rollout.
Market Research and Trend Prediction
An automotive company wants to explore potential market trends and customer preferences for a new electric vehicle. By generating synthetic data based on historical sales data and current market trends, they can simulate future scenarios and predict customer responses. This helps in making data-driven decisions about product features, pricing, and marketing strategies.
Getting Started with Synthetic Data for Marketing Campaigns
Define Objectives & Assess Needs
Identify specific goals for using synthetic data. Do you want to improve customer segmentation, personalize campaigns, or predict market trends? Consider gathering valuable use cases internal and with external support to build a pipeline of opportunity. Develop the right framework to drive choice.
Evaluate Data Gaps
Analyze your current data to understand what’s missing and how synthetic data can fill those gaps.
Select Tools and Technologies
Choose Data Generation Tools by looking into platforms like Mostly AI, Synthea, or Gretel.ai that specialize in creating synthetic data. Leverage ML Platforms like TensorFlow, PyTorch, AWS Sagemaker, or Google Cloud AI to handle large datasets and integrate synthetic data.
Generate and Integrate Data
Develop models by using techniques such as Generative Adversarial Networks (GANs) to create synthetic data that mirrors real-world patterns.
Integrate data sets and tooling. Incorporate synthetic data into your existing marketing systems, including CRM, automation tools, and analytics platforms.
Test and Optimize
Use synthetic data to conduct A/B tests and validate different marketing strategies and messages without risking real-world data. Test various marketing scenarios to predict outcomes and refine strategies accordingly. Embrace simulation modeling similar to prompting, as a skill to use with an AI enabled workforce.
Monitor and Improve
Track performance by monitoring the impact of synthetic data on your marketing campaigns or choices it is applied to. Be thoughtful in laying this plan out as it may require certain investments or hold out groups to test the performance appropriately.
Refine strategies by using insights from synthetic data to make informed adjustments to targeting, messaging, and creative elements. Feed this into a continuous improvement process or program that actively manages the early pilots.
Train Your Team
Educate your teams on the use of synthetic data and related tools. Build proper skill networks and capabilities to drive sponsorship and early pull through successfully before scaling. Encourage cooperation between data scientists, marketers, and IT professionals to ensure successful implementation.
Ensure Compliance and Ethics
Ensure that synthetic data generation complies with privacy regulations like GDPR and CCPA. Regularly audit synthetic data to detect and correct biases that could affect campaign outcomes.
Leverage synthetic data to innovate and drive commercial models and marketing activity. The future of marketing lies in the intelligent use of data, and synthetic data offers a powerful way to enhance your strategies and achieve better results.
Contact Good Intent.