Build Customer Data Platform using Google Cloud
Introduction — Customer Data Platform(CDP)
A Customer Data Platform (CDP) is a platform that amalgamates and manages customer data from various sources. The goal of a CDP is to provide businesses with a complete view of the customer by enabling other analytics tools to use the data and provide useful insights.
Across industries, there are a few secular trends that are driving the urgency among marketers to take control of consumer data. First, Consumers are demanding more relevant, personalized experiences, and second, they are increasingly concerned about how their data is used, leading regulators in tech companies to restrict data collection and sharing. The implications for marketers are clear, they need to get smarter at activating their data which is commonly referred to as 1st party data. Companies that do this well can get more incremental revenue from a single ad placement compared to organizations that lack data maturity.
As you approach architecting your Customer Data Platform (CDP), make sure you have identified what the data your business specific needs, the potential value this data can drive, and how you are going to activate it. This will help you create the most efficient strategy for your business.
Building a Customer Data Platform is often a journey, you can start with breaking down data silos and experimenting with basic marketing analytics use cases and capabilities that can grow over time as you integrate additional data sources.
CDP Use Cases
Unified Customer Profile: A CDP can create a consolidated customer profile by aggregating data from various sources. This can enable businesses to get a 360-degree view of the customer and more insights into their behavior and preferences.
Real-Time Data: Collect real-time data on customer behavior, enabling businesses to respond quickly to changes in customer preferences or market trends.
Segmentation: Segment customers based on their behavior, preferences, and other criteria. This will identify high-value customers and create targeted marketing campaigns, increasing customer engagement and loyalty.
Predictive Analytics: A CDP can use predictive analytics to forecast customer behavior, such as their likelihood to purchase or churn. This can help businesses to anticipate customer needs and take proactive measures to retain their customers.
Why CDP on Google Cloud?
Building a Customer Data Platform on Google Cloud is much more than a traditional CDP application. With Google Cloud; you can consolidate data from across your organization with even systems like SAP and Oracle, activate your entire marketing ecosystem not just google properties but also Facebook, Marketo, etc. you can go beyond marketing to drive digital commerce and product innovation use cases, you get built-in AI/ML and predictive analytics capabilities and the ability to democratize insights and activation across your marketing team. This open and flexible approach to building CDP will make your infrastructure adapt to any changes in marketing plans or Partnering changes.
High-Level Technical journey of CDP on Google Cloud
The following diagram will give you some examples of 1st party data collected directly from the consumer, most of this consumer data is spread across many places (i.e Ad Campaigns, CRM systems, websites, app analytics, service logs, loyalty programs, etc.) which makes hard to get a complete view of any consumer.
Building a CDP on Google cloud has different stages:
The first stage of this journey is collecting data. You can consolidate all your customer data from disparate sources into BigQuery and fully leverage the power of a modern highly scalable data warehouse. Google Cloud platform will provide you rich data ingestion options which will cover your data from all angles. The native data connectors for Google products such as BigQuery data transfer service effortlessly lands the data you already have in Google Analytics, Google Ads, Google Ad manager etc. into BigQuery. Dataflow speedily handles both batch and real-time ingestion and DataFusion provides an easy user experience for batch and real/time ingestion. You can also leverage other 3rd party connectors such as Fivetran to create data pipelines across data sources.
The second stage is to transform and enrich customer data and store securely. All customer data is transformed into useful schema for analysis and also enriched by augmenting with additional aggregated insights from combining 1st party data with 2nd and 3rd party data sources.
The third stage is to analyze and visualize it to make it useful. It’s not enough to just collect data, you also need to experiment with it and get insights from it using AI/ML capabilities and then also visualize and activate it across your organization using Looker.
The final stage is Marketing Activation, this is where you capture value with your generated insights whether by smartly segmenting your consumers so you can reach them at scale in a personalized relevant way or by optimizing your marketing spend to improve ROI on your media budget.
Following are few of the Google Cloud services that could be used while building a Customer Data Platform.
- BigQuery : petabyte scale data warehouse
- Dataflow : is a fully managed service for ingesting and processing data. It is an ETL tool that allows businesses to extract data from databases in their system and transform it into useful data
- Cloud Storage : is a cost-effective , highly scalable object storage service that can be used to store customer data.
- Cloud Pub/Sub : is a messaging service that can be used to stream data from various sources, such as IoT devices and social media platforms. It enables businesses to collect and process real-time customer data, which can be used for personalization and analytics
- DLP, Dataplex and Data Catalog : for managing sensitive data and establishing governance and security
- BigQuery ML + Vertex AI : can be used to deploy Machine Learning models for use cases such as Product Recommendations , Sentiment Analysis etc.
By leveraging these products and services, businesses can build a robust and scalable CDP on GCP, enabling them to gain a deeper understanding of their customers and improve customer experiences.
High level phased architecture for a Customer Data Platform on Google Cloud
Steps for implementing a CDP on Google Cloud:
- Data Ingestion: The first step in implementing a Customer Data Platform is to ingest customer data from various sources, such as CRM systems, marketing platforms, and transactional databases into Google Cloud. This can be done using services like Google Cloud Pub/Sub, and Google Cloud Dataflow, Data Fusion.
- Data Transformation: Transform the collected data into a common format for analysis. GCP provides several data transformation tools, including Google Cloud Dataproc, Google Cloud Dataflow, and Google Cloud Dataprep.
- Data Storage: The transformed data can be stored in various data storage options, including Google Cloud Storage, Google Cloud SQL, and Google Cloud Bigtable.
- Data Analysis: GCP provides several data analysis tools, including Google BigQuery, Google Cloud AI Platform, and Google Cloud Datalab to gain insights into customer behavior and preferences.
- Personalization: Using the insights gained from analysis, to deliver personalized experiences to their customers across multiple channels. Google Cloud tools like Google Marketing Platform, Google Analytics, and Looker can be used for delivering personalized experiences.
Key benefits of developing a Customer Data Platform on Google Cloud Platform :
- Centralized Data Management: CDP on Google Cloud provides a consolidated view of customers by gathering data from multiple different sources.. This allows companies to have an understanding of their customer’s preferences, likes and dislikes.
- Personalization: Real-time customer data can be used to deliver personalized experiences across multiple channels. Personalization can help increase customer engagement and loyalty.
- Data Security and Compliance: GCP provides robust security features, including data encryption, access controls, and compliance certifications, to ensure customer data is secure and compliant with industry regulations.
- Scalability: GCP is highly scalable, allowing businesses to handle large volumes of customer data and rapidly scale their CDP as their data needs grow. Faster Time to Market: GCP offers a range of pre-built data tools and services, allowing businesses to quickly develop and deploy their CDP solutions, reducing time to market, and enabling faster data-driven decisions.
- Cost-Effective: GCP provides cost-effective pricing models, allowing businesses to pay only for the services they use and reduce costs associated with maintaining and scaling their infrastructure.
Overall, implementing a CDP on GCP can help businesses gain valuable insights into their customers, deliver personalized experiences, and achieve their business goals more efficiently and cost-effectively.
A special thanks to Abdul Shaik for contributing to this article !
