4 Ways to Supercharge Your Marketing Efforts with Google Cloud

SADA Says | Cloud Computing Blog

By SADA Says | Cloud Computing Blog

Today’s marketers face a Catch-22. They’re awash in primary data, but there’s so much that it’s extremely difficult to glean any actionable insights. Fortunately, Google Cloud’s data warehousing, smart analytics, and machine learning tools can help marketers cut through the noise and leverage their data to uncover valuable business intelligence.

Here are four ways organizations can use Google Cloud to deliver more effective, tailored, and customer-centric marketing messages.

1. Optimize customer targeting with a marketing data warehouse

Effective remarketing can dramatically increase conversion rates and optimize customer lifetime value (CLV). While organizations are not lacking in customer data, it’s typically locked inside siloed, incompatible systems. BigQuery, Google’s serverless data warehouse solution, can ingest and transform data from disparate marketing channels and data sources and use it to create powerful remarketing lists that give marketers a holistic view of their customers. However, collating all of this data into one place and transforming it into usable form requires extract, transform, and load (ETL) tools—many of which are very difficult to implement and produce varying results.

Google Cloud offers a variety of tools that greatly simplify ETL, such as Alooma, an ETL solution that automates the process of transforming and normalizing data. Alooma enables organizations to transform their on-prem data and move it directly to their target database on Google Cloud, with automatic error detection and automated removal of personally identifying information (PII).

SADA’s Database Migration Flat-Rate Packaged Offer simplifies the process further by removing the guesswork and saving organizations the time and hassle of performing the migration themselves. With no hidden costs and a predictable budget and timeline, our package includes discovery, planning, implementation, tooling, testing, and training. Our experts will create a detailed runbook and execution plan and provide hands-on support for data, schema, query, and access migration. We also include security, HA, performance, and compatibility testing for a customer’s database applications. In addition, we’ll provide GCP account setup/configuration, hands-on lab/training, and named/dedicated support for a white-glove migration experience.

2. Build a product recommendation system in Tensorflow

Artificial intelligence and machine learning are transforming marketing by delivering the highly personalized customer experiences that modern consumers demand. Product recommendation systems, which do everything from matching clothing items with fashion accessories to suggesting which streaming program a viewer might want to watch next, are integral to shaping the customer journey and influencing purchase decisions. TensorFlow, the world’s most popular software library for building ML applications, makes building product recommendation systems easy with the TensorFlow Recommenders (TFRS) package.

Built with TensorFlow 2.x and Keras, TFRS’ modular design enables users to easily customize individual layers and metrics while ensuring individual components work cohesively. With TFRS, users can build and evaluate candidate nomination models; incorporate item, user, and context information into recommendation models; train multi-task models that jointly optimize multiple recommendation objectives; and finally, use TensorFlow Serving to serve the resulting models.

3. Use Vertex AI and AutoML Tables to predict customer lifetime value (CLV)

The Pareto Principle states that 20% of a company’s customers represent 80% of their sales. Using Vertex AI, Google Cloud’s unified ML platform, marketers can identify which of their customers are in that valuable 20%, presently and in the future.

Vertex AI is the next generation of the former Google AI Platform, bringing together all of Google Cloud’s ML services under one unified UI and API. Users can train and compare models using AutoML or custom code, store all models in one central repository, and manage them with ML Ops tools such as Vertex Pipelines, which streamlines running ML pipelines, and Vertex Feature Store, which enables the efficient sharing and reuse of ML models at scale.

Using AutoML Tables, which is integrated into Vertex AI, marketing analysts can easily build, test, and deploy highly sophisticated ML models to predict CLV — with minimal expertise and no coding ability required. AutoML Tables determine the best architecture for a particular dataset, and the entire training process is automated, including hyperparameter tuning. After a model is trained, AutoML Tables provides a detailed analysis of the model’s performance, including an explanation of the importance of each feature.

4. Leverage BigQuery and AI to automate advertising insights at scale

Getting messages in front of the right eyes at the right time is important, but 75% of advertising impact is determined by creative quality, meaning the visual aspects of the ad. Where should a logo be placed to enhance brand recall? Do ads with images of cityscapes outperform those with images of beaches? Traditionally, answering these types of questions required expensive, time-consuming manual testing which digital marketers don’t have time for. They need to measure their campaigns’ efficiency and make changes in real-time.

By combining BigQuery with Google Cloud’s AI capabilities, digital marketers can extract and process visual metadata from their ads at scale, enabling them to quickly determine which images and videos resonate with their customers. Creative images and videos are fetched from Google Marketing Platform, copied to Cloud Storage, then processed by the Vision and Video Intelligence APIs. This raw data is funnelled into Pub/Sub, creating a scalable, real-time data pipeline that’s written to BigQuery. From there, data visualization tools like Data Studio and Looker help users make sense of their BigQuery data and create visualizations to identify trends and make informed adjustments on the fly.

Let SADA help you leverage Google Cloud to deliver marketing messages that get results

As a trusted Google Cloud Premier Partner, SADA has an extensive track record of helping organizations harness the power of Google Cloud to solve their biggest challenges. Contact us today to learn how we can help you make impactful, data-driven decisions. We’ll help you assess what data points matter most, migrate your on-prem databases to the cloud, and develop a cloud-first data strategy aligned with your business objectives.

White paper: Data warehousing in the cloud

Greater speed, scale, flexibility, modern technology, and business innovation await enterprises that move their data warehouse to the cloud. Read the white paper to learn more about building an analytics strategy to power your performance.

White paper: Data warehousing in the cloud

Greater speed, scale, flexibility, modern technology, and business innovation await enterprises that move their data warehouse to the cloud. Read the white paper to learn more about building an analytics strategy to power your performance.


Our expert teams of consultants, architects, and solutions engineers are ready to help with your bold ambitions, provide you with more information on our services, and answer your technical questions. Contact us today to get started.

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