Digital Marketing is in a boom in today’s era. With the rapid rise in technology, people (sellers as well as buyers) are finding this Digital era of shopping and selling a better choice than traditional methods. The sellers post the Ads for their products on various social media based on the user’s behavior, and the customer, if they find it helpful, gets attracted to those Ads.
Practical analysis of these Ads data can lead to better marketing performance enabling sellers to make data-driven decisions and boost their sales.
This blog talks about two methods to integrate Amazon Ads to Snowflake and the uses of doing so. It also mentions the key features of Amazon Ads and Snowflake.
What are Amazon Ads?
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Amazon Advertising (Amazon Ads) was formerly known as Amazon Marketing Services. It was launched in 2018 as a search advertising solution for Amazon vendors. It is a service that works similarly to Google Ads, i.e., the Pay-Per-Click model, which means it will pay when the customer clicks on the Advertisements.
Amazon Advertising is growing at a rapid pace, and by having hundreds of millions of customers across the world, Amazon has an excellent understanding of how shoppers engage with Products and their behavior on product browsing and purchase.
Amazon’s Demand-side Platform has allowed sellers and businesses to programmatically display Ads on Amazon, Fire sticks, IMDB, Kindles, and many more third-party apps and platforms.
Key Features of Amazon Ads
- Digital Way to Reach Shoppers: With the rise of digitalization, there has been a significant increase in online shoppers. It is projected that there will be 279M online shoppers in the USA by 2024. Amazon Ads allows sellers and businesses to interact more effectively and efficiently with shoppers.
- Customizable Templates: Amazon Ads offers customizable templates to customize the features, Ad formats, and Ad placements.
- Customizable Budget: Amazon Ads offers a customizable budget to keep the cost of advertising within your budget.
- Quick Execution: Amazon Ads provide the ability to execute and optimize the campaigns faster than the traditional models. This results in a quick turnaround, and you have the opportunity to reach audiences at the right place and at the right time.
- Real-time Insights: Amazon Ads provides Real-time insights on the Advertisement that allows sellers to track real-time customer behavior and perform optimization. It will enable sellers to Track results, see how the ads are performing and make changes to the campaign as you go.
Types of Amazon Ads
- Sponsored Product
- Sponsored Brands
- Sponsored Display
- Stores
- Audio Ads
- Video Ads
- Custom Advertising
What is Snowflake?
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Snowflake is a fully-managed relational data warehouse on the cloud and available as Software-as-a-Service. It uses AWS/Azure/GCP’s cloud storage for its persistent store and compute instances to compute the analytics. Being a fully managed service, Snowflake itself manages the hardware, installations, maintenance, configurations, etc.
Snowflake can handle vast volumes of data and automatically scales up and down concerning data volumes. It uses ANSI SQL to perform analytics over the data and has excellent query optimization and results in caching techniques for better performance.
Key Features of Snowflake
- Result Caching: Snowflake works on the caching model so that when the same query is re-issued, it quickly results from the cache. Snowflake uses persisted(within the session) query results to avoid re-generating the output when nothing has changed.
- Query Optimization: Snowflake can optimize the query by clustering and partitioning on its own. You don’t need to worry about query optimization.
- Secure Data Sharing: Secure Data Sharing enables account-to-account data sharing through Snowflake database tables, views, and UDFs.
- File Formats: Snowflake supports structured and semi-structured data such as JSON, Avro, ORC, Parquet, and XML. It provides a column type, VARIANT, which allows you to store semi-structured data.
- Standard and Extended SQL Support: Snowflake has excellent support for ANSI SQL and supports advanced SQL functionality like Merge, Lateral view, statistical functions, and many more.
- Fault-Tolerant: Snowflake provides exceptional fault-tolerant capabilities to recover the snowflake object(tables, views, database, schema, etc.) in case of failure.
Why Connect Amazon Ads to Snowflake ?
Integrating Amazon Ads to Snowflake can be very helpful for an eCommerce business. To understand why, consider a simple example.
For a seller selling in the marketplace, they can determine the profit and loss by applying the formula as:
Profit(or Loss) = Sales -Expenses
The expenses are the money spent on Advertising and other related things like shipping, packaging, warehouse, etc. But for the simplicity of this blog, we will assume only Advertising expenses.
To get these details, the seller can download a report from Amazon Ads, which will contain the amount spent on running the Ads, and the user can download these sales reports from Amazon seller central.
Once the user downloads these reports, the two reports are merged into Excel sheets, and with the help of excel formulas, the user can generate the Profit or Loss statements.
The above step looks quite simple if they have only one Ad and one Product. Imagine if the seller has more than one product and sells in more than one Amazon marketplace. Then the above procedure will become a complex and time-consuming task prone to errors.
Hence, moving the data from Amazon Ads to Snowflake can minimize the risk and helps you to get insights into your campaign effectively.
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Methods to Connect Amazon Ads to Snowflake
You have learned from the above section that moving the data from Amazon Ads (upcoming connector) to Snowflake can efficiently help to generate insights and reduce the chances of human error. However, no direct official connector available will move the data from Amazon Ads to Snowflake, and you still need to connect these sources manually and integrate the data between them or use a no code data pipeline like Hevo. Here are two methods to Integrate Amazon Ads to Snowflake.
Method 1: Connect Amazon Ads to Snowflake using Hevo
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Hevo provides Snowflake as a Destination for loading/transferring data from any Source system. You can refer to Hevo’s documentation for Permissions, User Authentication, and Prerequisites for Snowflake as a destination here.
Amazon Ads is an upcoming source to connect Amazon Ads to Snowflake. Hevo supports a variety of Data Warehouses and Databases like Amazon Redshift, Snowflake, MySQL, Databricks, etc as sources. You can check out the integrations supported by Hevo here.
Configure Snowflake as a Destination
To set up Snowflake as a destination in Hevo for Amazon Ads to Snowflake Connection, follow these steps:
- Step 1: In the Asset Palette, select DESTINATIONS.
- Step 2: In the Destinations List View, click + CREATE.
- Step 3: Select Snowflake from the Add Destination page to connect Amazon Ads to Snowflake.
- Step 4: Set the following parameters on the Configure your Snowflake Destination page to migrate Amazon Ads to Snowflake:
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- Destination Name: Give your destination a unique name.
- Database Cluster Identifier: The IP address or DNS of the Snowflake host is used as the database cluster identifier.
- Database Port: The port on which your Snowflake server listens for connections is the database port. 5439 is the default value.
- Database User: In the Snowflake database, a user with a non-administrative position.
- Database Password: The user’s password.
- Database Name: The name of the destination database into which the data will be loaded.
- Database Schema: The Destination database schema’s name. The default setting is public.
- Step 5: To test connectivity with the Snowflake warehouse, click Test Connection.
- Step 6: When the test is complete, select SAVE DESTINATION to complete Amazon Ads to Snowflake Integration.
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Method 2: Manually Connect Amazon Ads to Snowflake
To manually connect Amazon Ads to Snowflake, you will be looking at the Amazon search Term Report to download and then move this to Snowflake for analysis.
The Amazon Search Term Report contains actual customer data, and it includes what the customer is exactly searching across Amazon to find your products. The reports includes the raw keywords data that are specific to your products.
The report includes:
- Keyword targeting
- Keyword Match Type
- Customer search term used
- Sales and conversion rate for that term during a specified period
- Clicks
- CTR
- Impressions
- CPC
- Spend
Extract Amazon Ads Report
- For this blog post, the prerequisites are that you should be having Amazon Seller Central account.
- Login to your Amazon Seller central, and navigate to Seller Central Homepage.
- Select Reports from top Navigation bar.
- Select Advertising Reports from the dropdown menu.
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- Select the campaign type, period of the campaign and then select Create Report by providing Report Name.
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- Once the reports are created, you can then Download from the main page as shown in below snapshot.
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Load the Report into Snowflake
Before we start ingesting data into Snowflake, it is important to have well-defined schema of the data to ensure the data integrity is maintained. Data in the Snowflake is well organized as Snowflake supports rich set of DataTypes. In Snowflake it is possible to load the data directly in JSON, AVRO, Parquet, XML and many other format of data.
Loading the data from local file system to Snowflake is a two step process –
- Load to Stage
- Copy from Stage to main table.
Follow the steps given below to connect Amazon Ads to Snowflake:
- Run the below command to load the data from your local machine to the stage folder in your local machine:
put file://c:\data\data.csv @~/staged;
- To check the files on the stage directory in Amazon Ads to Snowflake Integration, run the following command:
list @~;
- Once the data is copied into stage folder, create a table in the Snowflake with the create table statement, providing all the column names and their dataytpes in Amazon Ads to Snowflake Migration. Following syntax can be used:
CREATE TABLE mytable (col1 int, col2 string, ..)
- Now the table has been created, load or copy the data from staged location to the above created table:
copy into mytable from @~/staged file_format = (format_name = 'my_csv_format');
- Once the process is completed, you can now see the data by running a select query in the editor.
- Once the data is loaded into Snowflake tables, you can build and execute the SQL queries to generate insights from the data in Amazon Ads to Snowflake Integration.
Although these steps sound tedious to work with, they remove human error and enable you to gather the data and generate consolidated insights from various platforms.
Conclusion
This article provides a general introduction to Amazon Ads as well as Snowflake. In addition, it described two different methods for moving data from Amazon Ads to Snowflake. A manual data replication from Amazon Ads to Snowflake would require a significant amount of time and resources, making it a procedure that is both time-consuming and taxing on the user’s energy. A data integration solution such as Hevo, on the other hand, makes it possible to carry out the task in a prompt and effective manner.
However, as a Developer, extracting complex data from a diverse set of data sources like Databases, CRMs, Project management Tools, Streaming Services, and Marketing Platforms to your Database can seem to be quite challenging. If you are from non-technical background or are new in the game of data warehouse and analytics, Hevo can help!
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Hevo will automate your data transfer process, hence allowing you to focus on other aspects of your business like Analytics, Customer Management, etc. Hevo provides a wide range of sources – 150+ Data Sources (including 40+ Free Sources) – that connect with over 15+ Destinations. It will provide you with a seamless experience and make your work life much easier.
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Vishal has a passion towards the data realm and applies analytical thinking and a problem-solving approach to untangle the intricacies of data integration and analysis. He delivers in-depth researched content ideal for solving problems pertaining to modern data stack.