Generative AI in Ecommerce: How to Elevate Your Business
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Data warehousing in eCommerce analytics has revolutionized how businesses process and analyze critical data. The eCommerce industry came into existence in 1982 with Boston Computer Exchange (BCE), predating modern cloud infrastructures. Until the last decade, the eCommerce industry relied heavily on basic spreadsheets and manual reporting to analyze critical aspects of the business, such as customer behavior and logistical challenges.
While that approach once worked when every company operated with similar lagging actions, it is now often too late to respond in today’s ever-evolving marketplace, where your empowered competitors are processing millions of transactions in seconds and making critical business decisions in minutes.
Traditional storage systems, including on-premises storage and databases, are typically designed to prioritize daily business tasks and transactional processing. While these systems are well-optimized for such operations, the need for a more structured solution dedicated to analytics, reporting, and business intelligence has emerged.
Hiding behind its brick-and-mortar name, a modern specialized system was created specifically for the purpose of analytics and reporting in eCommerce. A data warehouse is a specifically designed system meant to store—and analyze—large volumes of structured and unstructured data from various sources.
Unlike traditional databases that focus on day-to-day transactional operations, data warehouses typically focus on analytics and reporting through both real-time and historical data.
The diagram above illustrates the functions, benefits, and applications of data warehousing in eCommerce analytics.
The core concepts of data warehousing include:
ETL (Extract, Transform, Load) is a fundamental process in data integration. The process of combining data from multiple sources, for the sake of easier analysis and ultimately to minimize the silo effect, is considered data integration.
Ensuring accessibility, consistency, and security, centralized data storage, typically in a single repository, is the backbone of data warehousing. For eCommerce analytics, it ensures better governance and scalability.
BI, data mining, predictive analytics, and query tools are software applications meant to interpret and visualize data stored in data warehouses. These tools, having access to organization-wide centralized data, allow you to make faster decisions and encourage improved collaboration.
With the fundamentals out of the way, let’s get our discussion back to eCommerce analytics and its dependency on data warehousing. Below are the key applications of the same:
The word silo originates from “silos”, meaning “pit for holding grain”. The analogy still fits in the eCommerce industry with data silos being the black holes where data goes to die. Data silos are isolated data collections that are not easily accessible by other departments within the same organization. For example, your marketing team may store customer engagement data in one platform, while the sales team uses a different CRM for their operations, preventing, or limiting, cross-departmental access for both.
With 69% of businesses lacking in offering a better customer experience due to data silos, it’s high time we try to minimize the adversity. Data warehouse integration in eCommerce provides a unified view of business operations—enabling better cross-departmental analysis and decision-making.
Consistent interactions across departments are critical for 79% of your customers to make a decision, while 73% of them expect better personalization with advancing technology. Successfully analyzing these customer behaviors can increase eCommerce customer retention by 5% and boost your profits even by 95%. Data warehousing enables more detailed tracking and analysis of customer interactions, preferences, and purchase patterns to help you:
Supply chain optimization is so critical to the eCommerce industry that overstocks, out-of-stocks, and supply chain disruptions cost businesses over $1.75 trillion in revenue annually. The availability of historical data through data warehousing with proficient analytics can help mitigate these issues and maximize profit for you.
Data warehousing often supports key optimization strategies such as demand forecasting, real-time inventory tracking, just-in-time (JIT) inventory, and automated replenishment. This ultimately results in reduced stockouts, lower inventory costs, and faster fulfillment— increasing customer retention, scalability, and profits.
Expounding on the minimization of the silo effect, marketing, and campaign performance are also improved with implementing a data warehousing strategy for eCommerce analytics. Squeezing various marketing data sources, including social media, email campaigns, and sales data, in a single platform increases the probability of better decision-making.
For instance, Walmart operates through both physical and an extensive eCommerce platform. The data harvested through these channels, in addition to dedicated marketing efforts, is massive. This has allowed them to implement a data warehousing platform integrated with advanced analytics tools to optimize their marketing performance significantly.
It’s also worth noting that access to real-time performance metrics empowers you with the ability to optimize your campaigns with A/B Testing, and rapid adjustments like budget allocation and audience targeting.
Juniper believes, and statistically so, that global online payment fraud losses might exceed $343 billion in 2027 from $48 billion in 2022. A cumulative dent. As an eCommerce business, now more than ever, it has become critical for you to identify fraudulent activity, manage cyber threats, and protect both you and your customer from financial losses and hefty fines in billions.
Data warehousing, as mentioned, brings together data from payment gateways, customer accounts, transactional histories, and transactional tools, for more accurate fraud detection by identifying correlations between seemingly unrelated data points.
Moreover, as most of these rogue acts revolve around promo abuse, account takeovers, and credit card frauds, a real-time predictive risk model, powered by eCommerce data warehousing and behavioral analytics can help.
In today’s fast-paced eCommerce world, analytics and data warehousing are the cornerstones of success. By harvesting data into a unified hub through proper ETL pipelines and setup, your business can gain insights to improve decisions, enhance customer experiences, and optimize operations. From understanding behavior to refining campaigns, it has proven to help eCommerce stay competitive and ready for future growth.
Specializing in advanced technologies like Apache Spark, cloud platforms like AWS, and AI tools like TensorFlow, RBM Software provides robust data warehousing and analytics solutions for eCommerce. Your business, irrespective of whether it’s based on Woocommerce or Shopify, can benefit in regards to marketing, customer relations, and inventory optimization, from the seamless data aggregation and actionable insights. For more details, feel free to book a free consultation call with us.
