At some point to truly scale, brands must understand the importance of data beyond that which Shopify provides…. And that crafting this data is essential for 10x growth.

$10 million in revenue. While achieving 8-figures in revenue is an amazing accomplishment for e-commerce brands, it is also a watershed moment – the hiring, data decisions, and strategic approach that brands make at this revenue level will determine if they scale to $100mm-plus, or simply plateau.

Why? Because at this stage, brands transform from a mere “brand” to a data-driven company with unique needs, challenges, and analyses. The high-level excel analysis or broad insight provided by off-the-shelf data software will no longer suffice to drive the next 10x growth. Data-driven approaches addressing unique company marketing and operational issues will.

What is a data warehouse?

This is where data warehouse services come into play. A data warehouse ingests data generated across all sources, cleans and sanitizes such data, and unifies it such that brands can perform next-level data analytics to make optimal and mission critical decisions. Ingesting of data runs the full gamut –Shopify (or any other e-commerce platform) storefronts (across all geographics), Amazon, offline retail storefronts, order and inventory management systems (e.g. Skubana), fulfillment platforms (e.g. Shipbob), marketing platforms (e.g. Klaviyo, Facebook, GA), CX platforms (e.g. Gorias), the carriers themselves, and any other specialized application that a company may be utilizing.

Without the unification of this data, there is no way for companies to cross-link all of this data and understand highly correlated issues, such as SKU level time series analysis or NPS by product.

OK that makes sense, so how do I use a data warehouse?

The data warehouse is just the first step. What really makes the data warehouse useful is the customized data model that sits underneath the warehouse as well as the data visualization tools that directly result from the crafting of such model.

What are your unique methodologies/definitions when thinking about your key marketing metrics? What is your unique product taxonomy? Are you able to truly understand all sales KPIs by each individual SKU? If you are reading this, you certainly know that Shopify alone fails miserably at providing this level of data granularity. And as an extension, any off-the-shelf software tool merely ingesting data only from Shopify can do no better nor dive any deeper.

This is where Atidiv steps in… we provide the only solution that builds a warehouse and truly customizes the underlying data model to fit YOUR unique needs.

Case Study: Fresh Clean Tees

Fresh Clean Tees sells bundles of T-shirts, but wanted to know NPS, LTV, repeat purchases, conversions, and other granular data at the specific T-shirt level (type, color, size, etc.). They also wanted to understand this data across various time sequences and geographies. This was impossible to do before Atidiv built the unified data warehouse and model. Now, Fresh Clean Tees maintains a custom dashboard built on top of its data visualization tool (Sigma). With this, Fresh Clean Tees is able to make better marketing decisions on pricing and paid media strategy across all channels.

Get in touch with us if you are interested in learning more!