A marketer's journey to dynamic customer personalization with Snowflake and a CDP

Recently, Business Insider reported that 71% of shoppers expect personalized marketing from brands, and 76% are frustrated if this expectation isn’t met.

Speaking of expectations, I’m not at all surprised to hear this; in fact, it’s something I’ve seen occurring over the past few years throughout my time at Snowflake. 

Today, consumers have countless options, and information about products and brand experiences is consistently in front of them through social media. The reality is that meeting consumers where they are at with personalized content matters. It matters more now than ever before. 

I’ve spent a large part of my career working with retailers, brands, and the broader advertising ecosystem and observed firsthand how companies have increasingly invested in marketing and personalization efforts over the years. Often, it’s a topmost priority and core to the success of the business.

“The reality is that meeting consumers where they are at with personalized content matters. It matters more now than ever before.”

The retail industry has evolved over the years, fueled by technological advancements (genAI, I’m not just talking about you!) and shifting consumer behaviors. COVID-19, in particular, was a period that foundationally changed how and what consumers purchased as they navigated the macroeconomic uncertainty. 

As the landscape continues to evolve, there are four big influential trends to consider that will impact that approach:

• Discretionary spending due to inflation: An increasing number of shoppers are looking towards discounts or shopping opportunities with lower-cost providers unless it’s perceived as a high-value product. 
• Concern over customer data privacy when GDPR/CCPA accelerated the focus on consumer privacy and will continue to be at the core of consumer engagement.
• The focus is on AI-driven individualization to deliver the right message, product, or experience at the individual level rather than broader segments.
• Providing an omnichannel experience that meets the consumers where they are, whether in-store, on social media, in a mobile app, on connected devices, or on a digital website.

Understanding the individual consumer’s needs and preferences has become imperative for retailers to thrive in a competitive market. AI will further accelerate how retailers can drive these experiences through AI-driven analytics, predictive algorithms, dynamic content optimization, hyper-personalized marketing campaigns, and innovative search technologies. 

That said, running relevant, personalized campaigns at scale is a difficult task without the right data strategy and foundation. Data and data access are the lifeblood of modern marketing. 

How customer marketing has evolved

In the early days, data management was characterized by siloed systems. Customer data resided in separate repositories, such as POS systems, CRM platforms, email marketing tools, and warehouses.

Each department within the organization maintained its own dataset, leading to duplicated efforts, inconsistent data quality, and limited visibility into the customer journey. This posed several limitations (below) that often resulted in generic marketing campaigns. 

• Retailers struggled to gain a comprehensive view of their customers and had incomplete/inaccurate customer profiles
• Inability to orchestrate campaigns seamlessly cross-channel 
• Duplicate customer records and conflicting data entries became common issues. Without a centralized mechanism for data governance, retailers faced challenges in maintaining data integrity and ensuring consistency across systems
• Privacy challenges, like duplicate copies of data and inefficient methods of data sharing (the physical movement of data), led to increased concerns about consumer privacy and how data was being used
• Rigid data collection and modeling requirements that required orchestration one-off across separated repositories 

Recognizing the inherent limitations of siloed systems, ambitious retailers began to explore unified approaches to data management. This shift towards integration and consolidation marked a pivotal moment, allowing retailers to break down organizational silos and harness the full potential of their data assets.

The emergence of Customer Data Platforms (CDPs) 

CDPs emerged as catalysts for change in the industry as a result. CDPs serve as centralized repositories for customer data, aggregating information from disparate sources and providing marketers with a single source of truth for customer insights. 

By consolidating data silos, CDPs enabled retailers to create unified customer profiles that captured the entirety of the customer journey, from initial awareness to post-purchase engagement. 

Advanced analytics and machine learning algorithms empowered marketers to glean actionable insights from vast datasets, uncovering patterns and trends that drive customer behavior.

However, as companies matured in their journey, this created another challenge. 

While CDPs enabled marketers to bring together customer data and segment affinities, they created another data silo from where the rest of the business data lived — think supply chain, product catalog, finance, and sales data — isolated from the rest of the data that traditionally lived in a data warehouse. 

As retailers focused more on data-driven insights and actions, the need for data access across all parts of the business became a requirement.

In line with modern marketing technologies, a multi-cloud native database approach emerged as a solution to breaking down these silos in a more effective manner. 

Snowflake vs. traditional cloud data warehouses

Snowflake, a cloud data platform, was a pioneer in bringing the multi-cloud native architecture to reality. Snowflake revolutionized the way organizations store, manage, and analyze their data.

‍

Snowflake’s unique architecture enables high performance and concurrency for data-intensive workloads with privacy and governance at the forefront, and it varies from traditional data warehouses in several ways for marketing use cases. How, you may ask?

Traditional data warehouses struggle with the sheer volume and variety of data

For one, traditional data warehouses often struggle to handle the volume and variety of data generated by modern marketing channels, such as social media, mobile apps, and online advertising. Snowflake's cloud-native architecture enables automatic scaling of both compute and storage resources, allowing marketers to handle large volumes of data and complex analytical workloads with ease. ‍

Traditional data warehouses require manual intervention to manage and activate

Another challenge of traditional data warehouses is that they often require manual intervention to scale resources, leading to inefficiencies and downtime during peak periods (i.e., holidays like Black Friday and Cyber Monday). Snowflake's architecture enables automatic scaling of both compute and storage resources, allowing organizations to handle fluctuating workloads.

Traditional data warehouses are also often limited in terms of data types and storage formats, requiring data to be structured and preprocessed before loading into the warehouse. Snowflake supports a wide range of data types and storage formats, including structured, semi-structured, and unstructured data. 

This flexibility allows data teams to ingest and analyze diverse data sources, such as customer transactions, website interactions, social media posts, and more, without the need for complex transformations or preprocessing.‍

Snowflake supports real-time use cases at scale

Finally, traditional data warehouses don’t support real-time use cases at scale. Snowflake enables data teams to perform real-time analytics on data, allowing teams to monitor and analyze customer interactions as they happen. 

This real-time insight enables marketers to identify trends, detect anomalies, and take action in the moment, ultimately improving marketing campaign effectiveness.

Snowflake further introduced a paradigm shift with connected and native application frameworks, which enabled the concept of bringing the technology to the data vs. physically moving data around — a game changer for retail marketers and also the IT and engineering teams managing the infrastructure behind the scenes.

Snowflake + CDP: The dream team for personalized marketing

The integration of Snowflake and connected CDPs allows Snowflake to be the backbone of the data infrastructure, storing and processing vast amounts of data, and creating a single source of truth. 

The connected CDPs can then leverage this data to build enriched customer profiles, incorporating demographic information, purchase history, and browsing behavior. This unified view of the customer serves as the foundation for personalized marketing initiatives.

The connected CDP journey is characterized by the following key components:

Data integration and enrichment: By combining customer data, transactional data, online interactions, social media engagement, and other touchpoints with an Identity backbone, retailers gain a holistic view of each customer.

Segmentation and targeting: With Customer 360, marketers can segment their audience based on demographics, preferences, purchase history, and behavioral patterns. 

Personalized campaigns and experiences: Leveraging Snowflake's analytical capabilities and CDPs' real-time data processing, marketers can orchestrate personalized campaigns and experiences across channels. 

The GenAI/AI capabilities through Snowflake Cortex, Container Services, and Snowpark ML will further streamline the ability to analyze customer data, identify the key patterns & preferences, enabling businesses to tailor the customer experience at scale.

Predictive analytics will help forecast future customer behavior based on historical data to help businesses anticipate customer needs and proactively engage with them through targeted campaigns and offers.

Continuous optimization and iteration: Dynamic customer personalization is an iterative process that requires continuous optimization and refinement. By analyzing campaign performance, monitoring customer feedback, and leveraging A/B testing, marketers can fine-tune their strategies to maximize engagement and conversion rates over time.

Let’s talk about the Snowflake Data Cloud for Marketing and what that means in the larger personalization context. The goal is threefold:

‍

1. Easy-to-use, single platform to unify marketing and enterprise data Integrate customer data and marketing data — in any format — from internal and external sources, across channels, and in near real-time, and leverage native AI to automate and optimize your marketing workflows.

• Unify all marketing, customer, and enterprise data with no friction in a single platform leveraging Snowflake connectors
• Execute real-time marketing use cases using Snowpipe streaming and dynamic tables
• Democratize insights to marketers and automate workflows with native AI/ML using Snowflake Cortex and Snowpark

2. Privacy-first, trusted, and secure platform with built-in governance Harness a fully managed, highly governed, multi-cloud platform to minimize overhead, mask sensitive data, and collaborate with privacy on customer data — without exposing it — via Global Data Clean Rooms.

• Govern customer and marketing data using Snowflake Horizon
• Collaborate without compromising privacy using Snowflake Data Sharing and Data Clean Rooms
• Scale at any volume, across clouds with Snowflake’s Snowgrid

3. Robust martech ecosystem and catalog of best-in-breed applications Execute the full marketing lifecycle with a catalog of marketing data cloud applications, partner with leading publishers and agencies, and enrich customer profiles securely with thousands of data products from Snowflake Marketplace with no ETL.

• Enrich profiles and resolve identities with no ETL using Snowflake marketplace
• Marketing ecosystem, connected to the data using Snowflake’s applications
• The advertising stack is agnostic and interoperable using Snowflake’s Network effect with leading publishers and agencies

Embracing a cloud-native approach to deliver customer marketing experiences

A connected CDP plays a significant role in unlocking value within the Snowflake Marketing Data Cloud. Data teams can focus on the data foundation, which includes building customer360, while marketing teams can focus on planning and activation activities across channels. 

This is what we mean by modernizing CDPs via a cloud-native approach. That, then, allows marketing teams to run analysis, streamline workflows, deliver a personalized customer experience, and understand their marketing strategy effectiveness while data teams help make sense of relative channel performance (campaign intelligence).

CDPs can mean different things depending on where a business is in their journey. However, the maturity curve typically covers two phases: Customer 360 and planning and activation. 

The building blocks that summarize a CDP solution are:

1. Data capture: Built-in ingestion capabilities, including first- and third-party data collection
2. Identity resolution and enrichment: Also known as an identity graph, which stitches unknown user activity to known users
3. Semantic unification (single view): Integration of all customer data to create a single view of the state of customers
4. Segmentation and orchestration: Point-and-click interface for marketers to define audiences and orchestration
5. Data activation: Activate segments to channels and retrieve data from Customer 360 in real-time for channel or product personalization
6. Analytics: CDPs can produce analytics reports that provide insights into consumer behavior and the customer journey

Snowflake serves as the foundation for storing and processing vast amounts of data, including customer data. At the same time, the CDP can focus on utilizing customer-specific data for marketing and customer experience initiatives. 

This integration enables comprehensive data management and analytics capabilities across the organization while fueling consumer experiences through personalized engagement.

The marketing data cloud space continues to develop

As we continue to develop the capabilities, the partnerships will continue to deepen as will the technology itself. 

GenAI/AI is of interest to many as you can imagine – just the other day, I was talking to a customer about a GenAI-powered chatbot in the form of a shopper’s assistant that can engage with customers in natural language conversations and understand user preferences, behaviors, and histories. That is valuable information to include in the CDP funnel and journey especially to drive targeted outreach.

Suppose you think about the content creation process today. It requires several manual steps — from gathering the information to defining KPIs and content distribution channels to actually building the content and publishing the content. 

GenAI models can take inputs such as text and turn that into an image, generating content in a quick, automated fashion. More broadly, it can help advertisers target their ads towards specific content categories based on relevant interests and preferences, including themes, moods, and genres, and allow those to be inserted in a way that is scalable and in real-time. CDPs play a role in getting the right content to the right people.

Conclusion

Today’s retail landscape demands one thing loud and clear: hyper-personalized customer experiences. But fragmented systems and siloed customer data make delivering these marketing experiences feel impossible. 

Fortunately, as consumer demands have evolved, so have the concept and technology around the marketing data cloud — and it continues to evolve every day, especially when it comes to genAI and machine learning.

While Snowflake’s platform provides the single source of truth through data storage, security, compliance, and processing capabilities businesses need, connected CDPs like Simon Data help marketing teams access comprehensive customer 360s and activate that data to build the 1:1 personalized customer experience customers crave.

To learn more about how Snowflake and a connected CDP can help, check out our latest guide.