It's a week after Black Friday, and the results are in! While online spending didn't break a new record this year, it still totaled a massive $8.9 billion, making Black Friday one of the biggest sales days ever for digital merchants. Online sales were even healthier on Cyber Monday, totaling $10.7 billion

But how did paid marketing contribute to all those holiday shopping season sales?

Some e-commerce retailers struggle to measure the effectiveness of their paid campaigns. Others don't know how to integrate data from paid campaigns with customer relationship management (CRM) and order management systems. 

All of that makes it difficult to predict the profitability of channels like Google and Facebook Ads.

In this guide, learn why pulling data from paid channels and other sources is fundamental for optimizing business performance and making smarter organizational decisions. Then discover a tool that can integrate paid channel data for better reporting and analytics. 

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Table of Contents

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Paid Marketing and Black Friday

E-commerce retailers planned their paid marketing campaigns with precise detail ahead of the Black Friday weekend, investing heavily in channels like Facebook Ads, which reaches over 2 billion active users. Other paid channels that proved successful for Black Friday deals this year included display ads, paid content discovery, paid search, and pay-per-click ads. 

With all of these channels, marketers segmented audiences and offered different Black Friday promotions based on a segment's location, interests, and purchasing behavior. Marketers will continue to use these strategies in the run-up to Christmas and New Year as sales skyrocket. 

Read more: Change Data Capture: CDC for E-Commerce

Measuring the Return on Black Friday Digital Marketing

Many e-commerce retailers experienced a sales boost as a result of their Black Friday marketing strategy. However, some retailers don't know what paid channels triggered those sales. That's because they haven't integrated paid marketing data with their CRM or order management systems. Or they don't know how to push data from Google Ads, Facebook Ads, and other channels into a single system for analytics.

These retailers, therefore, have no way to track where leads come from. Nor do they know which paid channels generate the biggest return on their marketing spend. Without these insights, retailers could be wasting money on paid marketing strategies that provide little results. Plus, they can't capitalize on the channels that generate the most leads. 

Read more: Unlock Your Brand's Post-Pandemic Potential

Why Data Integration is Important for Online Shopping Campaigns

The most successful e-commerce retailers integrate data from multiple platforms to generate real-time insights about consumer habits and, ultimately, sales. These platforms include paid channels like Facebook Ads, as well as tools such as Shopify and BigCommerce. While it's too late for some companies to measure this year's Black Friday sales, investing in data integration tools will provide greater visibility for future Black Friday offers and holiday season promotions, as well as sales from any day of the year. 

Data integration can help:

  • Track potential customers
  • Find new customers
  • Discover which marketing channels and techniques provide the most value. For example, search engine ads, SEO, email marketing, influencers, CTAs, upselling, popup ads, gift guides, content marketing, retargeting, countdown timers, coupons, social media, etc.

Data integration is fundamental for e-commerce retailers because it consolidates data from various sources into a single dataset. By standardizing data in this way, marketers can run that data through analytics programs and generate intelligence about the people who purchase their products and services. 

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How to Integrate Data

There are various data integration methods. Perhaps the most effective is something called Extract, Transform, and Load (ETL). This strategy involves extracting data from a source, transforming it into the proper format for analytics, and loading it to a final destination such as a data warehouse or data lake. From here, e-commerce marketers can run the data through business intelligence (BI) tools and find out which paid and organic channels trigger click-throughs, influence conversion rates, and boost sales. Many BI tools turn datasets into powerful visuals, making it easier to understand trends and patterns within those datasets. 

Retailers might use BI tools to learn more about:

  • Average order value
  • Cart abandonment
  • The checkout process
  • Most popular products from the last year
  • Number of subscribers 
  • First-time customers
  • Existing customers
  • Email campaigns
  • Brand messaging

When retailers generate these insights, they can improve future marketing operations. For example, they can send Black Friday emails next year with subject lines that include the names of their most popular products. 

ETL Use Cases for Online Shopping

Retailers can use ETL to integrate data from various sources. Here are some examples:

  • Extracting data from an order management system, moving that data to a warehouse, and comparing it with Facebook Ads data in a BI tool. This can help marketers understand the number of customers who placed an order after clicking on a Facebook advertisement.
  • Extracting data from a CRM system, moving that data to a warehouse, and comparing it with Google Ads data in a BI tool. This can help marketers learn the effect display ads on the Google Display Network have on customer purchases.
  • Extracting ad data from Twitter, moving that data to a warehouse, and comparing it with Instagram ad data. This tells marketers the most effective social media platform for generating leads and online store sales. 

ETL vs. ETL Tools

Retailers can carry out ETL themselves. However, manual ETL requires complicated data pipelines that move data from one location to another. Small businesses might struggle with the code needed to execute this process. That's why it's a good idea to invest in ETL tools that automate most of the work associated with data integration. Some of the most popular ETL tools for e-commerce companies include:

These tools vary in price and features, and not all of them will provide retailers with the results they are looking for. However, all ETL tools will move data from the most popular databases, systems, and applications without any code. For example, Integrate.io lets you move Salesforce data to a warehouse such as Amazon Redshift so you can run that data through a BI program and generate insights about customers and orders. It does this via a native Salesforce connector, eliminating the need to build manual data pipelines. 

Read more: Top 7 Integrate.io Features

How Integrate.io Helps With E-Commerce

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Integrate.io is the ETL solution for e-commerce retailers that want more insight into their sales and marketing strategies. Unlike some other platforms, Integrate.io uses a pay-as-you-go pricing model that charges users for the number of native connectors they use and not the data they consume, which can work out cheaper for some small businesses. 

With over 100 native connectors, Integrate.io optimizes data integration between sources and destinations. Digital merchants can generate insights from data that currently exists in separate locations and improve future e-commerce campaigns.

Integrate.io is a data integration solution for e-commerce retailers everywhere. Start your 7-day free trial now.