360-Degree View Of Customers With Snowflake’s Data Lake

In today’s data-driven world, firms are recognizing the significance of having a unified view of their customers across all touchpoints and data sources. A customer data platform (CDP) provides this 360-degree view by consolidating customer profiles and interactions from various systems into a centralized snowflake data lake service. Snowflake, with its powerful data lake services, is well-suited to build such a CDP.

The Need For A Unified Customer View:

Siloed data makes it difficult to understand customers holistically. Data about a customer may exist in multiple source systems like CRM, marketing automation, support tickets, online behaviors, etc. But without unification, the full context is missing.

A CDP correlates this disparate data to create a single customer profile. This unified view enables personalized experiences, predictive insights, and coordinated campaigns across channels.

Architecting A CDP On Snowflake:

Snowflake offers a flexible data warehouse optimized for unstructured data. Its elastic storage and computing make it ideal for housing vast amounts of raw customer data from distinct sources. The logical first step is to establish a centralized data lake using its snowflake data lake services. It involves:

• Defining file formats, folders & security
• Loading source data via COPY INTO commands
• Transforming data into normalized schemas
• Setting up automated ingestion through pipelines

Ingesting Customer Data:

The CDP needs to continuously ingest new customer data from various sources. Snowflake supports bulk loading from cloud storage like S3. For streaming data, change data capture tools like Attunity or Kafka Connect can capture transactional changes and load them incrementally into Snowflake. Its built-in tasks and pipelines automate scheduled loads. External tools like Fivetran or StitchData also simplify ingestion.

Correlating Customer Profiles:

Once data is centralized, the next step is correlating profiles. It involves joining and merging records from distinct sources that refer to the same customer. Techniques like probabilistic matching on attributes like email, phone, etc. are used. Customer keys or identifiers must be defined to uniquely identify each person. Data quality steps standardize formats and enrich profiles.

In addition to basic demographic attributes, other significant data for profiling customers includes their product ownership history, support cases, marketing interactions, online activity, and more. Snowflake allows joining these diverse data sets residing in different schemas/tables through its powerful SQL capabilities.

Developers can write queries to correlate records based on probabilistic matching rules. For example, matching an email address in the CRM to an email used to log into the company website. Data quality steps are then applied to standardize formats, resolve duplicates, and enrich attributes that may be missing from some sources. Over time, as more customer touchpoints are integrated, the unified profiles become increasingly complete.

Storing Profiles And Interactions:

The unified and enriched customer profiles are stored in normalized schemas optimized for queries. Dimensional modeling patterns help denormalize and optimize access. Aggregated metrics about behaviors, purchases, etc. are also captured. New interactions are joined to update profiles incrementally. Profiles are versioned to allow analyzing behavior changes over time.

Once the initial customer profiles are created, it is important to keep them updated as new customer data streams in. Snowflake supports change data capture or CDC capabilities through integrations with common tools. It permits capturing the incremental changes to source systems and synchronizing them to update the corresponding customer profiles in Snowflake.

For example, tracking new support cases, form submissions, or purchases and joining this transactional data to the relevant customer profile to refresh the view. Profiles are versioned so that analysts can compare attributes over time and analyze customer journey stages and behavior changes.

Exposing Data For Applications:

To power personalized experiences, the CDP data must be exposed securely. Snowflake offers distinct options for interacting with applications and tools. Externalized views define subsets tailored to specific use cases.

Streams replicate transactional changes. Integration with data warehouses like Redshift enables reporting and analytics. APIs and SDKs allow programmatic access. Security controls govern access.

Enriching Profiles With AI/ML:

AI and ML techniques can further enrich profiles by inferring attributes not captured explicitly. For example, analyzing text interactions may reveal interests and preferences that enrich profiles. Snowflake’s integration with cloud AI services like Azure ML or SageMaker enables such advanced enrichment and segmentation at scale directly on the centralized data.

In addition to basic segmentation, machine learning models can be developed and trained directly on the large customer profile data in Snowflake. Some examples include predictive churn models, lifetime value forecasts, next-best product recommendations, sentiment analysis on support conversations, and more.

Snowflake integrates with all major cloud AI/ML services, like Azure ML, SageMaker, TensorFlow, etc. so these advanced analytics don’t require data movement. Models can be deployed using Snowflake’s built-in ML services and APIs/SDKs to power intelligent applications and personalized experiences.

Orchestrating Customer Journeys:

With a unified view, it becomes possible to understand customer journeys, touchpoints, and outcomes. Tools like journey mapping visualize these paths. Segmentation identifies customer types to personalize outreach.

Predictive models anticipate needs proactively. Campaign management orchestrates omnichannel messages. Snowflake supports these use cases through integrations and partner ecosystems.

Monitoring And Maintaining Data Quality:

As new data streams in, it is critical to monitor data quality and catch anomalies. Snowflake provides visibility into data profiles, outliers, missing values, etc. Tools like Great Expectations validate quality rules. Proactive alerts notify teams about data issues. Automated workflows remediate quality over time to ensure the CDP remains a trusted single source of truth.

Ensuring data quality will be an ongoing process as more sources are integrated into the CDP. Snowflake provides visibility into data profiles to understand distributions, outliers, missing values, and other anomalies. Teams can define quality rules using tools like Great Expectations and receive automated alerts about any violations.

Conclusion:

In summary, Snowflake data lake services provide a scalable platform to build a centralized customer data platform. Its flexibility, governance, and integration capabilities help unify siloed customer data to deliver consistent, personalized experiences across channels. With the CDP, firms gain a 360-degree understanding of each individual to deepen engagement and lifetime value.