It is estimated that, by 2025, over 175 ZB of data will be produced globally, and close to 30% of new insights will be generated in real-time. Such a swift growth of the data sphere presents significant opportunities for retailers in this hyper-competitive environment. Big data in retail can be utilized to optimize costs at every stage of the operational life cycle and empower business leaders with better consumer insights.

However, the question is, how retailers could access it and where will this data be stored? The hypothesis of data warehousing isn’t new, as such systems were part of proprietary Business Intelligence solutions for decades. However, data warehousing has re-entered mainstream attention thanks to recent advances in cloud computing and big data analytics.

What is a Data Warehouse?

A data warehouse is a technical system aggregating business data from different sources into a centralized, secure, homogenous repository. You can infuse your analytics systems with data coming from:

Enterprise resource planning (ERM) solutionsCustomer relationship management (CRM) platformsCloud-based databases and relational on-premise andExternal public sources — ranging from general statistics to geospatial data

Combined or individually, these data sources can serve as a backbone for advanced retail analytics schedules, ranging from inventory demand forecasts to personalized dynamic prices for individual buyers.

Why E-tailers require better Data Storage and Analytics Capabilities

According to a survey, for 97% of business leaders, improving the online user experience is the top-most priority in the year 2021. But, 71% don’t know where to begin when it comes to understanding consumer behaviors.

Indeed, during the past year, customer behaviors changed tremendously. Spending around product categories and purchase channels remains in flux. However, what is certain is that E-commerce sales volumes will retain a strong momentum in 2021, as Accenture’s latest data suggests.

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Most retailers already recognize the tectonic shifts in customer behaviors. But the rapid migration from online to offline spending left numerous with limited purview into customers’ actions.

First-party consumer data — insights collated directly from your audience — remains siloed in different systems and departments and thus unused. For instance, multi-brand retailers need to access different data sources at the SKU and store levels to achieve cost transparency across the organization. These include:

Stock levelsAverage profit marginsAll cost components across sales channelsPlanned promotionsPersonalized discountsShipping quantities and costs.

In numerous cases, these often originate from different parties, including external ones. Therefore, brands must first aggregate this info to make it available for analytics. A centralized data repository. That’s what a DW is for. Apart from ensuring consistent access to internal info for marketing, data warehouses can be configured to host analysis-ready data for:

Scenarios development and Business continuitylast-mile delivery optimization and LogisticsProduct recommendations and Real-time personalizationOptimization and Supply chain managementAdvanced shopper behavior modelingPurchase and Demand trends predictionsProcess optimization scenarios to gain cost agility

Eventually, a data warehouse is a technological gateway for E-comm retailers to better predict, plan, and act on emerging consumer demands and market trends.

What is an e-commerce data warehouse?

A data warehouse, in context to the E-commerce industry, is a cloud-based system for collating, storing, and organizing information about your consumers. A data warehouse (DW) creates a single digital place for you to review the information. You can then utilize that warehouse to run analytics, reports and measure the entire company’s going on.

Benefits

First, a data warehouse (DW) is a technical pillar for consolidating data and ensuring its steadiness. In the truancy of such a system, you will never deploy advanced analytics and gain operational insights from BI tools.

Access to historical and real-time insights: Likewise traditional databases, data warehouses were designed to accommodate storage and easy retrieval of historical information — integral for building forecasts and predictive models.

Faster access to data: DW is an analytical database layer designed to ensure conformity, consistency, and quality of all data taken from other sources. Standardization provides quick access to various insights that can be immediately used for reporting and analytics.

Enhanced compliance: Retailers are bound by regulations regarding collecting, storing, and processing personally identifiable information (PII) — customer payment details, full name, personal addresses, and more. Data warehousing assists filter out such insights from creeping into analytics plus puts extra safeguards for protecting all stored assets.

Better interoperability: DWs can be hosted on-premises, in the cloud, or in multi-cloud environments. With the latter, you get the top-most competitive pricing and analytics tools from IaaS cloud vendors. What’s more, cloud-based DWs can be effectively integrated with other company’s infrastructure and business systems to support two-way data exchanges.

How are organizations utilizing Datawarehouse for Ecommerce?

So far, so good. Data warehouse management sounds excellent. But what kinds of returns should you expect on your investment? What do you do with the data? Let’s explore the possibilities.

Attribution modeling

An attribution model means organizations “tag” their incoming revenue with its proper source. In this model, you set the rules and assign full or partial credit of a sale to individual touchpoints in your sales pipeline.

As a result, you’ll have a more precise internal ROI. Which channels are providing the best results? Who’s making the sales? For mostly offline brick-and-mortar retail, it’s not possible to pull this off. But in an e-commerce DW environment, these insights are invaluable.

Predictive analytics

In e-commerce, predictive analytics isn’t just for guessing at next-quarter sales. It helps you build actual, content recommendations and practical products for consumer segments. A study found that predictive “lead scoring” was one of the top use cases here. Brands can leverage their data to predict which leads are most likely to convert into customers with lead scoring. This creates immediate leverage in marketing: where to put your money, you know who to market to, and what kind of ROI to expect.

Road Ahead

Therefore, it can be assumed that Data warehousing and e-commerce are two of the most rapidly expanding fields in recent information technologies. So, Get started with Polestar Solutions and find out how easy it is to feed your e-commerce data initiatives into the data warehouse of your choice. Book a session with our data warehouse consulting today!