Criteo to Snowflake – Made Easy

Posted By: editor
Posted On: 17 Aug 2020

If you’ve come here, you are probably looking for a way to transfer data from Criteo to Snowflake quickly. In this article, we talk about why Criteo is essential and how you can get access to this data without having to write any code.

The choice for eCommerce business when it comes to marketing and selling their merchandise is growing every day. eCommerce vendors have to decide on what channels they want to sell on, which channels they want to spend their advertising dollars on, whether the channels include:

  • Branded websites 
    • In some cases branded eCommerce sites per country
  • Marketplaces
    • In many instances, marketplaces per country
  • Retail stores
    • to create an omnichannel presence and to engage buyers where the shop

Complexity increases with the addition of every sales channel. For instance, if we consider marketing channels available to support online business, you will find a choice of:

  1. Social Media Ads – Some platforms include Criteo, Instagram, LinkedIn, Twitter, and others
  2. Digital ads and remarketing – Criteo, Taboola, Outbrain, and others
  3. PPC – Google ads, Bing ads, and others
  4. Email – Mailchimp, Klaviyo, Hubspot, and others
  5. Podcasts
  6. Affiliate – Refersion, CJ Affiliates
  7. Influencer marketing
  8. Offline marketing and more

Choice, while being a great virtue, leads to complexity, and this complexity when not managed properly, can, in turn, impact the efficiency of running an eCommerce business. Most eCommerce businesses grapple with this complexity; some well and many not so well.

In a competitive digital landscape that we live in, it has become imperative that eCommerce businesses of all sizes that aspire to grow and stay profitable have to look into their data deeply and leverage this for growth.

With the increase in competition, eCommerce Companies should strive to be more data-driven for various reasons. Some of these reasons include.

  • understanding the balance between demand and supply
  • understanding customer lifetime value (LTV)
  • Segmenting customer base for effective marketing
  • finding opportunities to reduce wasteful spend
  • optimizing digital assets to maximize revenue for the same marketing spend,
  • improving ROIs on Ad campaigns and
  • offering an engaging and seamless experience for customers in every channel that the customer engages with the brand.

Businesses these days need to be efficient in terms of their data analysis. They are struggling to make sense of the data generated from various applications and tools used to manage different processes efficiently.

Due to the reasons highlighted above, any eCommerce business typically operates at least 10-15 different software/platforms to deliver on their customer expectations. As a result, data silos are created, which makes it more difficult to consolidate data and use the data for reporting, operations, analysis, and taking informed forward-looking decisions.

Marketing platforms like Criteo generate a substantial amount of data like impressions, user behaviour, clicks, product details, and more. Additionally, eCommerce companies that sell globally often end up having separate ad accounts for each country which in turn creates data silos for each country. Imagine a brand selling on three marketplaces or three countries – They may have three accounts per channel in which they are generating data—consolidation of data from these accounts for effective reporting.

These silos make an analysis of the entire business data comprehensively, challenging. Data Savvy eCommerce businesses try to reduce the effort of reporting and analysis by integrating data from all these channels into a cloud data warehouse like Snowflake. By taking this step, the process of reporting and analysis becomes easy, inexpensive, and consequently done more frequently.

In this post, we will be looking at methods to replicate data from Criteo to  Snowflake.

Before we start exploring the process involved in data transfer, let us spend some time looking at these individual platforms.

Criteo Overview

Criteo relies significantly on technology and intelligent algorithms to advertise products and to re-target them. This means that if a user has seen the advertiser’s webpage once, they will be shown the ads from the same webpage in the future as well. This technique of using machine learning and AI for re-targeting consumers makes Criteo unique and can be considered the reason why it has progressed this much in just a decade.

Some other companies have tried their hand at remarketing tools for e-commerce too, but none beats Criteo to date. The reason for this might be Criteo’s affordable pricing and creative and personalized support. Criteo has over 1.4B active monthly shoppers and more than $600B annual sales across retailers, brands, travel, and B2C commerce companies. Around 18,000 advertisers are known to use it worldwide. The key features can be listed as:

  • Reach and visibility across web, mobile, video, and social platforms through Criteo’s worldwide network of several publishers and partners
  • Creation of additional sales by estimation of certain goods or items of interest
  • Connecting with buyers through a variety of devices, browsers, and apps to engage them with the most relevant products
  • Increasing user engagement with the use of Artificial intelligence and machine learning algorithms and dynamically tailoring ads’ visual designs according to each buyer’s taste, ensuring brand continuity and maximizing conversions
  • Tracking campaign performance with real-time campaign reports
  • The highest level of protection and privacy of data is established in compliance with industry requirements and regulations – including GDPR

Snowflake Overview

Snowflake is a cloud-based data warehouse created by three data warehousing experts at Oracle Corporation in 2012. Snowflake Computing, the vendor behind the Snowflake Cloud Data Warehouse product, raised over $400 million over the past eight years and acquired thousands of customers. One might wonder if another data warehouse vendor is needed in an already crowded field of traditional data warehousing technologies like Oracle, Teradata, SQL Server, and cloud data warehouses like Amazon Redshift and Google BigQuery. Well, the answer is the disruption caused by cloud technologies and cloud opportunities for new technology companies. Public clouds enabled startups to shed past baggage, learn from the past, challenge the status quo, and take a fresh look at cloud opportunities to create a new data warehouse product. You can read this article to understand the core technology components that make up this modern, cloud-built data warehouse for consumers of cloud technologies.

You can register for a $400 free trial of Snowflake within minutes. This credit is sufficient to store a terabyte of data and run a small data warehouse environment for a few days.

Why Do Businesses Need to Replicate Criteo Data to Snowflake?

Let’s take a simple example to illustrate why data consolidation from Criteo to Snowflake can be helpful for an eCommerce business.

An e-commerce company selling in multiple countries is running campaigns on various platforms and is using Criteo to optimize ad delivery. They have different selling platforms like Shopify, Amazon, eBay, payment gateways, inventories, logistic channels, and target audiences in each country. An ad might be running off a product which might no longer be in stock, or might not be deliverable in the location which it is running, or the ad might be focussing on people with lesser buying intent, rendering these ads redundant and thus causing a substantial loss for the company. Now when the decision-makers want to rectify this and optimize their Ad campaigns using Optimove to maximize ROIs, they are faced with the following problems.

  1. The typical buying journey of a customer is no longer linear. They switch between websites, compare similar products, search Google for promo codes, drift to trusted online sources for reviews, before returning to your website, and finally making a purchase; perhaps using a completely different device. Thus, the company needs to decide on what channels they want to sell and how much to spend. Criteo data is used in this case to optimize conversions from individual channels.
  2. However, there are separate data silos for inventory data, logistics data, which need to be separately downloaded and compared and updated regularly to optimize all the ad campaigns and reduce redundant ads.
  3. Again, in case of re-marketing, people who have not completed payments, or have encountered a failed transaction need to be targeted in addition to people who have added products to their cart, wish lists, or favorites. People who have responded to other marketing campaigns like email, SMS, social media marketing also need to be targeted. So again separate data silos from various selling platforms, payment gateways, marketing tools need to be downloaded, analyzed, and compared with Criteo and other marketing channel data.
  4. Audience profiling data from e-commerce platforms, CRMs, customer support systems need to be analyzed to optimize audience targeting. Some of the Ad channels are dependent on the target audience, rather than their searched keywords or topics, it is essential to have an accurate target audience to get the optimum ROI.
  5. While calculating profits/losses of the overall business, it becomes a nearly impossible task to pull all of these data from multiple platforms for each country separately, and then analyze all of this data together with the expense data and calculate profits. It involves numerous working hours which costs money, and there is usually a time lag involved, which reduces the accuracy of the analysis and its effectiveness as the data is not analyzed in real-time.
  6. The compilation and processing of data from multiple sources for thorough research is a considerable challenge if carried out manually.
  7. Finally, and most importantly, not much digital business trust the attribution that is received from Ad platforms or CRMs. There is a good reason for it as well, although that is a topic for another day. In order to measure attribution, a data warehouse, and quick and accurate data analytics is critical.

Additionally, and more importantly, hardly any company runs advertising merely on Criteo. Marketers use multiple marketing channels to take the brand message out to the public. To understand the true ROI of campaigns across all the marketing channels, data consolidation cannot be escaped whether the process is manual or not.

For these reasons, top companies consolidate all of their data from Criteo and other apps and tools into a data warehouse like Snowflake to analyze the data and to generate and automate reports at a rapid pace.

The more data you can gather and use from different sources in your Criteo ad campaign, the more your ad delivery is optimized. All these data can not be natively transmitted to Criteo. Such data must be collected and analyzed correctly in a data warehouse like Snowflake before you use the relevant information to run ad campaigns on Criteo.

Replicate data from Criteo to Snowflake

There are two board ways to pull data from any source to any destination. The decision is always a build vs buy decision. Let us look at both these options to see which option provides the business with a scalable, reliable, and cost-effective solution for reporting and analysis of Criteo data. You can also retrieve the data from Snowflake any time you want. To know more, click here.

Use a cloud data pipeline

Building support for APIs is not only tedious but it is also extremely time-consuming, difficult, and expensive. Engaging analysts or developers in writing support for these APIs takes away their time from more revenue-generating endeavors. Leveraging a cloud data pipeline like Daton significantly simplifies and accelerates the time it takes to build automated reporting. Daton supports automated extraction and loading of Criteo data into cloud data warehouses like Google BigQuery, Snowflake, Amazon Redshift, and Oracle Autonomous DB.

Configuring data replication on Daton on only takes a minute and a few clicks. Analysts do not have to write any code or manage any infrastructure but yet can still get access to their Criteo data in a few hours. Any new data is generated is automatically replicated to the data warehouse without any manual intervention.

Daton supports replication from Criteo to a cloud data warehouse of your choice, including Snowflake. Daton’s simple and easy to use interface allows analysts and developers to use UI elements to configure data replication from Criteo data into Snowflake. Daton takes care of

  • authentication
  • rate limits,
  • Sampling,
  • historical data load,
  • incremental data load,
  • table creation,
  • table deletion,
  • table reloads,
  • refreshing access tokens,
  • Notifications

and many more important functions that are required to enable analysts to focus on analysis rather than worry about the data that is delivered for analysis.

Daton – The Data Replication Superhero

Daton is a fully-managed, cloud data pipeline that seamlessly extracts relevant data from many data sources for consolidation into a data warehouse of your choice for more effective analysis. The best part analysts and developers can put Daton into action without the need to write any code.

Here are more reasons to explore Daton:

  • Support for 100+ data sources – In addition to Criteo, Daton can extract data from a varied range of sources such as Sales and Marketing applications, Databases, Analytics platforms, Payment platforms, and much more. Daton will ensure that you have a way to bring any data to Snowflake and generate relevant insights.
  • Robust scheduling options allow users to schedule jobs based on their requirements using simple configuration steps.
  • Support for all major cloud data warehouses including Google BigQuery, Snowflake, Amazon Redshift, Oracle Autonomous Data Warehouse, PostgreSQL, and more.
  • Low Effort & Zero Maintenance – Daton automatically takes care of all the data replication processes and infrastructure once you sign up for a Daton account and configure the data sources. There is no infrastructure to manage or no code to write. 
  • Flexible loading options allows to you optimize data loading behavior to maximize storage utilization and also easy querying.
  • Enterprise-grade encryption gives your peace of mind
  • Data consistency guarantee and an incredibly friendly customer support team ensure you can leave the data engineering to Daton and focus instead of analysis and insights!
  • Enterprise-grade data pipeline at an unbeatable price to help every business become data-driven. Get started with a single integration today for just $10 and scale up as your demands increase.

Sign up for a free trial of Daton today!

Interested in learning more about data warehouses, their architecture, and how they are priced? Check out our other articles.

Google BigQuery Google Bigquery Pricing Google BigQuery – Architecture and Key Features
Snowflake Pros and Cons of Snowflake Snowflake Architecture
AWS Redshift Amazon Redshift
Oracle Autonomous DB Oracles Autonomous Data Warehouse
For sections where we talk about manual reports and lost productivity https://sarasanalytics.com/blog/improving-data-analyst-productivity
What is a cloud data pipeline https://sarasanalytics.com/blog/what-is-a-data-pipeline

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