How to Store Marketing Data in a Data Warehouse

A Use Case for an E-commerce Skin Care Brand

As digital marketing becomes more data-driven, companies are looking for better ways to store, analyze, and leverage their marketing data. One solution that's gained significant traction is using a data warehouse to centralize data and unlock insights. For e-commerce businesses, specifically those in the skin care industry, Google BigQuery offers a robust, scalable, and cost-effective platform to handle marketing data. In this blog post, we'll dive into how you can store marketing data in Google BigQuery, with a focus on a skin care e-commerce company using Shopify.

What is Google BigQuery?

Google BigQuery is a serverless, highly scalable data warehouse designed for big data analytics. It allows businesses to store massive amounts of data and perform real-time queries on it without worrying about managing the underlying infrastructure. It's particularly well-suited for businesses that need to handle large datasets, such as those in e-commerce and digital marketing.

Why Use Google BigQuery for Marketing Data?

For businesses in the digital age, storing marketing data is just as important as collecting it. The ability to analyze customer behavior, track campaign performance, and measure marketing ROI relies on centralized, high-quality data storage. Google BigQuery provides the following key advantages for marketing data storage:

1. Scalability: BigQuery can handle datasets ranging from gigabytes to petabytes.

2. Cost-Effectiveness: Google BigQuery uses a pay-as-you-go pricing model, meaning you only pay for the storage and compute resources you use.

3. Real-Time Insights: With BigQuery, you can query large datasets in real time, making it easier to gain insights quickly.

4. Integration: Google BigQuery integrates with a variety of tools, including Shopify, Google Analytics, and Google Ads, making it ideal for e-commerce marketing teams.

Use Case: The Skin Care E-Commerce Brand, GlowNaturals

Let’s set the stage with a skin care brand called GlowNaturals. GlowNaturals is an e-commerce company that specializes in selling organic skincare products. They run their store on Shopify and rely heavily on digital marketing through social media ads, email campaigns, and Google Ads to drive traffic and sales.

GlowNaturals has been accumulating a wealth of marketing data from various platforms. However, they are finding it difficult to manage, track, and derive actionable insights from this data. Their goal is to centralize all marketing data into a data warehouse to improve decision-making and optimize their marketing efforts.

Steps to Store Marketing Data in Google BigQuery for GlowNaturals

Step 1: Set Up Google Cloud and BigQuery

Before GlowNaturals can begin storing data in BigQuery, they need to set up their Google Cloud account and create a BigQuery project.

1. Create a Google Cloud Project: Go to the Google Cloud Console, create a new project, and enable billing.

2. Enable BigQuery API: Once the project is created, enable the BigQuery API, which allows data to be transferred into BigQuery for analysis.

Step 2: Integrate Shopify Data with BigQuery

GlowNaturals collects a wealth of data from their Shopify store, including customer purchases, cart abandonment, product views, and sales data. To bring this data into BigQuery, GlowNaturals can use Shopify's API or a pre-built connector like Supermetrics or Fivetran to automatically sync data from Shopify into BigQuery.

1. Install a Connector (e.g., Supermetrics or Fivetran): These tools make it easy to connect Shopify to Google BigQuery without needing to write complex code.

2. Choose Key Marketing Metrics: GlowNaturals wants to track key metrics such as customer lifetime value (CLTV), average order value (AOV), conversion rates, and traffic sources from various marketing channels.

Step 3: Store Data from Other Marketing Platforms

GlowNaturals runs ads on Google Ads, posts content on social media, and sends email marketing campaigns through Mailchimp. These marketing channels also generate valuable data that should be stored in BigQuery for comprehensive analysis.

1. Google Ads Data: Using the Google Ads connector in BigQuery or linking Google Ads via the Google Cloud Console, GlowNaturals can pull data on ad spend, clicks, impressions, and conversions.

2. Google Analytics: Since GlowNaturals uses Google Analytics to track website traffic, they can link their Google Analytics account to BigQuery to store data such as page views, bounce rates, traffic sources, and session durations.

3. Mailchimp Data: By using an integration tool or API, GlowNaturals can send email campaign data (open rates, click-through rates, etc.) into BigQuery.

Step 4: Transform and Clean the Data

Once the data from Shopify, Google Ads, Google Analytics, and Mailchimp is stored in BigQuery, GlowNaturals needs to ensure the data is clean, organized, and ready for analysis.

1. Data Transformation: Using SQL queries within BigQuery, GlowNaturals can join datasets from multiple sources (e.g., combining customer purchase data from Shopify with traffic data from Google Analytics).

2. Data Cleaning: The team can remove duplicates, fill in missing values, and ensure all data types are consistent (e.g., dates, prices).

Step 5: Perform Marketing Analysis in BigQuery

With all the marketing data centralized in BigQuery, GlowNaturals can start analyzing it to uncover valuable insights.

1. Customer Segmentation: By analyzing purchasing behavior and website interactions, GlowNaturals can segment customers based on demographics, purchasing habits, or engagement with marketing campaigns. For example, they could create a segment for customers who regularly purchase anti-aging products and target them with personalized ads.

2. Campaign Performance: Using BigQuery’s powerful query capabilities, GlowNaturals can track the performance of their Google Ads and social media campaigns. For example, they can see which campaigns drive the highest conversion rates and compare them against revenue data from Shopify.

3. Product Trends: By analyzing Shopify product data alongside Google Analytics traffic data, GlowNaturals can identify which skin care products are attracting the most visitors and driving the most sales. They can adjust their marketing strategies accordingly—like promoting popular products through email newsletters or social media ads.

4. Real-Time Insights: Google BigQuery allows for real-time data analysis, so GlowNaturals can track the effectiveness of marketing efforts in real-time and make adjustments to campaigns quickly. If a Facebook ad is performing well, for example, they can increase the budget to maximize returns.

Step 6: Visualize Data with Google Data Studio

For easier interpretation, GlowNaturals can use Google Data Studio, which integrates directly with BigQuery, to create dashboards and reports. These visualizations can help the marketing team quickly assess performance metrics like:

• Revenue generated from specific campaigns

• Customer demographics

• Email open rates and click-through rates

• Product sales trends

Conclusion

For an e-commerce brand like GlowNaturals, using Google BigQuery to store and analyze marketing data is a game-changer. By integrating Shopify with BigQuery and pulling in data from Google Ads, Google Analytics, and Mailchimp, GlowNaturals can centralize all their marketing data, allowing for more accurate analysis and better decision-making.

With BigQuery's scalability, real-time querying, and cost-effective pricing model, GlowNaturals can easily manage the growing volume of data as their business scales. By gaining insights into customer behavior and campaign performance, they can optimize marketing efforts, increase sales, and ultimately grow their brand in the competitive skin care market.

Is your business ready to unlock the power of marketing data with Google BigQuery? It’s time to start centralizing your data and making data-driven decisions that will transform your marketing strategy!