Mastering Direct-to-Consumer Marketing with First-Party Data: Delivering Personalized Experiences Using Generative AI

When it comes to long-term success, Consumer Packaged Goods (CPG) companies have a lot to consider. They need to maintain brand control, improve profit margins, and find new ways to engage customers, among other things. The good news is that the advent of generative artificial intelligence (AI) has created many opportunities for CPG businesses to address all these considerations. However, it is not a one-size-fits-all approach. Simply bringing AI to an organization will not yield maximum benefits—it must include a strategic application that correlates to business goals.

To do this, CPG brands are turning to Amazon Web Services (AWS), which offers a host of advanced analytics and generative AI services and solutions, to capitalize on their Direct-to-Consumer (DTC) strategies. By utilizing specialized foundation models, a large library of tools, and robust infrastructure from AWS, companies can transform first-party data into actionable insights. Just ask Tapestry, Adidas, DoorDash, and FrontDoor Inc., which have reduced their reliance on third-party data and improved personalized experiences using AWS solutions and services. Their investments have future-proofed their operations and continue to drive sustainable growth. And they can do all this while also maintaining privacy compliance in today’s competitive business environment. In this post, we will explain how you can, too—but first, we will cover some essential background.

Using First-Party Data to Predict Consumer Behavior

First-party data plays a crucial role in helping CPG companies anticipate customer needs through sophisticated analytics capabilities. By analyzing historical purchase patterns, companies can uncover meaningful trends that shape future campaign strategies. Advanced machine learning (ML) models enhance this capability by forecasting specific behaviors, such as purchase intent or potential churn risk. CPG companies can also use these insights to drive hyper-targeted messaging campaigns that resonate with distinct customer segments.

How CPG Companies Are Adapting with First-Party Data Strategies

CPG companies are adapting to a future without cookies by focusing on first-party data through loyalty programs and improved customer relationship management (CRM) systems. At AWS, we suggest focusing on five key strategies that enable personalized marketing, data-driven product development, and retail media network collaborations for targeted advertising.

1. Building loyalty programs: Loyalty programs incentivize consumers to share their data in exchange for rewards. For instance, offering discounts or exclusive products can encourage repeat purchases while collecting valuable insights into buying patterns. Starbucks is a great example of this.
2. Enriching CRM systems: Integrating first-party data into CRM platforms enables CPG companies to create detailed customer profiles. These profiles support personalized marketing campaigns and foster stronger customer relationships.
3. Personalized marketing campaigns: By analyzing first-party data, brands can segment audiences based on preferences and behaviors. For example, a company that identifies a segment interested in organic products can craft targeted campaigns highlighting these offerings to increase conversion rates.
4. Optimizing product development: First-party data provides direct consumer insights that inform product innovation. So, if a company notices rising interest in gluten-free snacks among its customers, it can prioritize developing those types of products to meet demand.
5. Retail media networks: Companies like iHerb and Oriental Trading collaborate with Amazon Ads technology to for delivering personalized ads, streamlining advertiser connections, and enhancing shopper experiences across their platform. This makes it easy for companies to offer advertising to brads already using Amazon Ads, providing them a streamlined connection to advertisers.

Key Infrastructure Components for AWS-based First-Party Data Solutions

CPG companies should invest in a first-party data strategy if they want to see long-term value. By taking advantage of the AWS solutions and services that are critical to data collection and utilization, businesses can do this in several ways.

• Technology infrastructure: Implementing tools such as Customer Data Platforms (CDPs) allow businesses to centralize consumer data from multiple sources into unified profiles for personalized marketing efforts. This involves some investment in cloud storage solutions and analytics software.
• Data management: Businesses need platforms for maintaining clean, accurate, and secure data. Amazon Simple Storage Service (Amazon S3) for data lakes or Amazon Redshift for data warehouses are great options. They can store large datasets securely while enabling analytics. Tools such as AWS Glue automate the ingestion and transformation of raw data into actionable insights, supporting simplified data preparation.
• Analytics and data insights: AWS analytics tools such as Amazon SageMaker, Amazon QuickSight, and Amazon Bedrock can analyze first-party data from CDPs to identify patterns and predict consumer behavior. The generative AI capabilities of Amazon Q for Business provide natural language insights, automate trend analysis, and generate predictive forecasts for customer segments based on historical data and real-time behavioral signals.
• Compliance efforts and privacy compliance: AWS Artifact offers comprehensive compliance tools and certifications that allow enterprises to access audit reports and maintain regulatory compliance. The solution comes with fine-grained access controls, encryption capabilities, and regional data residency options to help organizations meet specific privacy requirements while maintaining detailed audit trails.
• Organizational alignment: Amazon S3 and Amazon Redshift are effective in centralizing storage options, but CPG companies can also use them to foster cross-department collaboration. Additionally, they can use AWS Lake Formation and AWS Glue to support secure data sharing along with extract, transform, and load capabilities. Finally, AWS QuickSight and Amazon OpenSearch Service helps teams to analyze and visualize shared data. These services include identity access management controls that assign and manage appropriate access levels for different departments.

While these systems may require some initial planning, the ROI exists. Research from IDC and Forrester demonstrates that integrating first-party data into advertisement targeting strategies delivers significant business benefits. Companies that do this have achieved 500 percent lift in conversions by using AI-driven insights to reallocate media spend, 300 percent increase in advertising click-through rates (CTRs), a 78 percent boost in customer satisfaction, and a 73 percent improvement in conversion rates.

Harnessing the Power of Generative AI with a Complete Toolkit from AWS

Enterprise solutions from AWS center on Amazon Bedrock, which provides access to large language models (LLMs) such as Claude and Llama 2. It also opens access to Amazon Titan, which has retrieval-augmented generation capabilities for real-time, context-aware responses. Amazon Q for Business delivers natural language data interaction, while Amazon SageMaker supplies model development and deployment tools. Businesses can also implement Amazon Comprehend for natural language processing, Amazon Kendra for intelligent enterprise search, and Amazon Q Developer. Together, these tools enable organizations to deploy secure, scalable AI solutions that can transform their businesses through numerous use cases. Below are just a few of the ways they can enhance customer experience and engagement using AWS solutions and services.

1. Intelligent customer support: Improve customer experience by providing real-time, personalized interactions and automating routine tasks. CPG brands can do this using:

a. Amazon Connect: Make 24/7 customer self-service a reality with AI-powered chatbots and voice assistants, reducing wait times and improving first-contact resolution rates.
b. Amazon Lex V2: Build conversational interfaces for applications using voice and text. Automate and speed up bot creation of an Amazon Lex V2 bot by using Amazon Bedrock’s generative AI capabilities.
c. Amazon Q in Connect: Generate dynamic, context-aware responses and accurate, empathetic communication for agents and customers.

2. Personalized customer engagement: Create hyper-personalized experiences by pairing generative AI with customer data analysis. This makes it easier for brands to tailor marketing messages, recommendations, and offers.

a. Amazon Personalize: Elevate customer experience with AI-powered hyper-personalized user experiences in real-time, at scale.
b. Amazon Bedrock: Select from large language models (LLMS) to generate personalized emails, product descriptions, and promotional content.
c. Amazon SageMaker: Build ML models to analyze customer behavior for targeted campaigns.

3. Proactive customer outreach: Facilitate proactive engagement by using generative AI to predict customer needs and initiate timely interactions.

a. Amazon Connect segmentation capabilities: Automatically segment customers based on real-time and historical data to deliver personalized outreach campaigns using both ML and generative AI.
b. Amazon Comprehend: Analyze sentiment in feedback to identify opportunities for proactive engagement.

4. Enhanced agent assistance: Support brand representatives with real-time insights, summaries, and recommendations powered by generative AI during customer interactions.

a. Amazon Q in Connect: Provide a better work experience for agents with instant access to relevant information during calls or chats, boosting productivity.
b. Amazon Kendra: Support intelligent enterprise search to retrieve precise answers from knowledge bases. This saves representatives’ research time and improves the accuracy of information they provide.

5. Self-service enhancements: Generative AI lets businesses empower their customers with advanced self-service options that are intuitive and effective.

a. Amazon Transcribe: Convert voice inputs into text for seamless interaction in self-service systems.
b. Amazon Translate: Activate multilingual capabilities for reaching out to global customers.

Generative AI yields benefits beyond the business side of operations, extending to customers. Examples include the ability to:

• Resolve issues faster with automated personalized responses. AI-powered systems can analyze and respond to customer queries instantly, reducing response times. Automated responses are personalized based on customer history and context, ensuring accurate solutions within minutes instead of hours.
• Enhance customer satisfaction through seamless interaction. Consistent, accurate responses can improve customer satisfaction scores. AI systems provide 24/7 support without delays, maintaining quality service even during peak periods.
• Increase conversion rates through precise targeting. AI analyzes customer behavior patterns and purchase history to deliver targeted recommendations, resulting in faster query resolutions and higher conversion rates compared to traditional methods.
• Anticipate customer needs before they arise. Predictive analytics identify potential customer needs based on behavioral patterns and historical data. This can result in proactive support interventions and fewer support tickets.
• Reduce churn by addressing dissatisfaction early. Real-time sentiment analysis identifies at-risk customers, paving the way for immediate intervention. Early issue detection and resolution reduces customer churn and increases retention rates.

Maximize data across dimensions

First-party data has emerged as a critical asset for CPG companies navigating the post—third-party cookie landscape. With AWS, companies can now do more with their data—both internally and externally. Whether it’s transforming raw customer data into actionable insights with ease or upleveling personalized experiences, CPG companies have more options than ever.

If you want to discuss your data needs or you are ready to deploy a GenAI solution for retail, then AWS is here to help. Contact your account team today to get started.

Additional Reading

• Tapestry collects feedback from thousands of store associates to provide real-time feedback on customer demands, optimizing inventory management and local stock alignment.
• Adidas uses generative AI on AWS to deliver personalized marketing campaigns unique to each recipient, improving engagement rates.
• DoorDash used Amazon Connect and Amazon Lex to create an interactive voice response system, reducing agent transfers by 49% and increasing first-contact resolution by 12%, saving $3 million annually.
• Frontdoor, Inc.’s experience highlights how AI-powered workspaces can enhance agent capabilities, enabling them to handle complex customer inquiries more effectively from day one.