Engaging Marketing Campaigns with Personalized Messaging
Crafting individualized messages can be time-consuming and challenging, often leading to increased sales acquisition costs.
However, with Conversya, you can leverage Generative AI—specifically, a large language model (LLM)—to automatically generate personalized emails based on product recommendation scores and consumer data.
This innovative approach boosts sales conversion rates while reducing the time invested in creating impactful email campaigns.
Feature Highlights
Scalable & Transparent
Conversya handles customer lists and recommendations in batches, expanding the reach of marketing teams and offering the adaptability to personalize messages for individual customers.
Cost Reduction
Optimize sales and marketing resources to enhance productivity and efficiency.
Personalized Messaging
AI-generated emails deliver tailored "recommended products" to individual customers.
Customizable
Sales and marketing teams can interact with the model using natural language instructions—even through voice—to refine emails for each segment and make necessary adjustments.
How It Works
Architecture
Generate product affinity scores for individual customers within Conversya by utilizing retail shop data and online marketplace insights, such as historical transactions and similar customer purchases.
These predictive scores are produced by a machine learning model trained on this pre-processed data. Companies can choose their preferred infrastructure for model execution: on-premise, private cloud, or managed cloud.
Tailored Messaging
Create personalized messaging for each recommended apparel item. Messaging can be adjusted based on the customer segment.
Users can input brief descriptions of each product or automatically generate them from images using a Large Language Model (LLM).
Campaign Design
Design email campaigns targeted towards specific customer segments, outlining the unique style and wording of the email template. Users can modify the template content through raw text instructions or voice commands.
Additionally, users have the ability to fine-tune the email manually.
Personalized Recommendations
For each customer, the "top recommended products" are inserted into the designated sections of the template. Users can preview a series of these customer emails to assess the final appearance.
Accountability Considerations
Data Privacy
Customer data may contain sensitive details. In the outlined design, no personal information is directly transmitted to the LLMs, and emails are crafted from templates generated by an LLM.
Employing a containerized version of the LLM with full certification from a chosen cloud provider is crucial to ensure corporate data privacy.
Language and Content Safety
Parameters for text generation should be meticulously reviewed to prevent toxic or polarizing language and to avoid utilizing customer demographics/segments in harmful ways (e.g., "single parents love this style").
The application incorporates human oversight, allowing marketing professionals to have the final say on message content before dispatching it to customers.
Responsible Utilization Recommendations:
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Conduct audits on the product recommendation system to ensure unbiased predictions—Conversya simplifies this process with explainable AI features.
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Ensure that the product recommendation model provides explanations for each recommendation, which can further refine the marketing message.
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Establish an overarching Responsible AI policy within the organization, ensuring its application covers both the foundational product recommendation model and the LLM used for email template generation.
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Implement a feedback mechanism for end users to report incorrect or harmful language.
