Ecommerce merchants and marketers have one major advantage over brick and mortar retailers: data.
When your shoppers come from the internet, you can measure almost every aspect of your interactions with them. However, that advantage doesn’t count for much without a system for making sense of your data.
Many companies think they already have a good system in place. But what they really have is a network of silos. It’s great to have data; sure. But if it remains in these silos, there’s no way to get big-picture insights.
When this happens, data doesn’t move. It sticks to different platforms like Shopify, Google Analytics, or Klaviyo. Or it waits on paid advertising platforms like Google Ads and Taboola.
Enter the ecommerce data warehouse: A cloud-based data warehousing system that extracts data from all the sources that matter to your business. From there, you can feed fresh data into a single dashboard.
Sounds interesting, right? Here’s how it works.
What is an ecommerce data warehouse?
A data warehouse, in this context, is a cloud-based system for gathering, organizing, and storing information about your customers. The name has a brick-and-mortar feel, but it’s a modern concept. “Warehouse” is an apt term. A data warehouse creates a single digital place for you to review your information. You can then use that warehouse to run analytics, reports, and measure what’s going on throughout the entire company.
The benefits of data warehousing for ecommerce businesses
Why bother creating a system that funnels all your data into a single location, anyway?
There are a few key benefits:
Faster time to insights
Data analysis always requires data gathering first. If you already have a system in place to collect and store all relevant data, you can run analysis whenever you like.
Reducing the silo effect
You may have incoming data from places like Shopify, Google Analytics, and Klaviyo. Data may still be sitting on paid advertising networks like Facebook, Google, and Taboola. The problem? You can’t get a sense of the bigger picture, because that data is stuck within those systems.
Whenever you try to measure your data across multiple channels, things get messy. Your siloed data might be useful, but if you can only see a fraction of the big picture at once, that data will by definition only be partly useful to you. A data warehouse reduces the pains of the silo effect and helps you visualize big-picture trends.
Full ownership of data
When you silo your data, you lack a single source of truth for business insights. Even worse: unless you warehouse your own data, you’re at the mercy of the data retention policy of every platform you’re on. If they decide to ditch your data and you don’t already have it, you’re out of luck.
With data warehousing, you can migrate that data into your own reference source. If you ever want to refer to it for predictive models (like for personalized product recommendations), all the historical data you need is ready and waiting.
Flexible, affordable data storage
True: there are upfront costs associated with data warehousing. You’ll be setting up your own pipelines to feed data into your new digital warehouse.
We ran the numbers on data pipeline costs and found:
- With home-grown data pipelines, you’re looking at $15,000/year for data modeling and $10,000 for every data source you plug in.
- With outsourced data pipelines, a U.S.-based vendor might charge you $20,000-$50,000 per connector, with in-house project management as high as $1,400 per week.
That doesn’t sound like an “affordable” benefit, but it’s possible to reduce these upfront costs with managed data pipelines. Working with Supermetrics, for example, you can manage your daily data transfers with just a few clicks. This increases flexibility and affordability far beyond what you could hope to achieve with the alternatives.
What can you do with an ecommerce data warehouse?
So far, so good. Data warehouse management sounds great. But what do you do with the data? What kinds of returns should you expect on your investment? Let’s explore the possibilities.
An attribution model means you “tag” your incoming revenue with its appropriate source. You set the rules here. You assign partial or full credit of a sale to individual touchpoints in your sales pipeline.
As a result, you’ll have a clearer measurement of internal ROI. Who’s making the sales? Which channels are providing the best results? For mostly offline brick-and-mortar retail, it’s nearly impossible to pull this off. But in an ecommerce data warehouse environment, these insights are invaluable.
A 2015 Forrester study found that predictive “lead scoring” was one of the top use cases here. With lead scoring, you can leverage data to predict which leads are most likely to convert into customers. This creates immediate leverage in marketing: you know who to market to, where to put your money, and what kind of ROI to expect.
Or, take another example: Netflix. When the streaming company created “House of Cards,” it wasn’t throwing darts at the wall. It used predictive analytics via historical data to determine the kind of show customers had already demonstrated they wanted. Netflix then simply went about creating that show.
It’s a simple fact of economics, as defined by the Pareto principle: a small portion of your customers are likely to have the greatest impact on your bottom line. Customer segmentation is all about identifying that impact and using it to your advantage.
Typically, customer segmentation has focused on traditional variables, like customer demographics. But an ecommerce data warehouse opens all sorts of possibilities. You can identify and differentiate customers by products purchased, how likely they are to open your emails, and their behavior upon a previous visit. Some ecommerce outlets even offer weather-specific recommendations based on geolocation.
Optimizing paid ads and marketing spend on campaigns
Once you have a more accurate view of your customer segments, you’ll have more precise targets for your paid ads. And with your pipelines transferring data into your data warehouse, every new advertising campaign doubles as a fresh learning experience.
For example, A/B split testing lets you target different variables in your campaigns. This includes ad channel selection, high-level messaging, audience targeting, and even the specific copy you use in the ads. Properly channeled into your data warehouse, you’ll have the results of every campaign ready for comparison. What works and what doesn’t work? Now, you’ll know.
How to get started with your ecommerce data warehouse
When you’re ready to get started with your ecommerce data warehouse, here are the steps you’ll need to take. Don’t get too far into the pipeline decision before you’ve nailed down what data you want to move and where.
1. Define the data sources and metrics you want to move
Before you choose a platform, you have to know what it is you need to accomplish. Ask yourself:
- What are the data sources and metrics you want to move?
- What data needs to be relocated into the data warehouse?
- How will moving the data help your ecommerce operation?
2. Choose the destination for your data
Next, you have to decide where you want to move your data.
For example, do you go with Supermetrics? Do you outsource the work to an external partner? Consider different visualization and analytics options to find out which one will best serve your ecommerce operation. It all starts with your infrastructure and the appropriate platform for your goals.
But how do you know which platform actually fits your needs?
- Look for integrations that suit your dashboard needs. For example, Supermetrics makes it possible to have all of your data in a digital warehouse. You can then create dashboards in tools like Google Data Studio, Tableau, and Power BI. Supermetrics even offers a direct connector from Snowflake to Google Data Studio, helping automate the data feed.
- Find automation tools for a cloud-based warehouse. If you can’t automate the data flow, you’re only making more work for yourself. Supermetrics, for example, can help automate ecommerce data transfers to any cloud-based data warehouse, such as Google BigQuery, Snowflake, Azure Synapse Analytics, and Amazon Redshift. Here’s an example of how one company automated its data pipeline to Google BigQuery.
- Define your data warehousing goals. You should look for a platform that migrates data to your business, automatically updating it to a central location that you can view and access at any point. This gives you ongoing access to fresh, clean data. That data is now ready to fuel every aspect of your business.
3. Choose your data transfer method
Finally, you’ll need to iron out your data transfer method. This might be through a managed pipeline like Supermetrics, or via a home-grown custom API connection, or even an outsourced custom API connection. Choose a method that safely and securely moves over your data with minimal risk during the migration.
Data warehousing success
Data warehousing works. Just look to this example from Flying Tiger Copenhagen, a 5,000+ employee retail company that had a goal to create a scalable data warehouse that facilitated their growth plans. They chose Supermetrics for BigQuery.
With that infrastructure in place, the company started drawing on data from Danish media in all of its campaigns. They immediately saw the new potential in the insights the data was providing to them on an ongoing basis. This data revealed new opportunities in bringing on more cross-channel reporting to identify which marketing efforts were having the most impact on potential customers.
Ecommerce data warehouse: The secret to data-driven sales
What can you do with an effective ecommerce data warehouse system in place? There’s only one way to find out: try it out.
Start a trial of Supermetrics and find out how easy it is to feed your cross-channel ecommerce data into the data warehouse of your choice.
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