The industries are flooding with high volume and high-velocity data from different sources. The optimum use of these high-volume data has become vital for all sectors to operate in this competitive landscape.
Similarly, eCommerce is entangled with data cluttered through various means. A study on big data and analytics shows that 99.5% of data never gets used or analyzed, thus wasting potential resources and growth opportunities for businesses. As data plays a central role in facilitating the growth and scale of eCommerce businesses by analyzing customer behavior and optimizing logistics, supply chain, decision-making, services, etc.
Data engineering with advanced tools and technology has the power to transform the industry by generating immense value for them. It ensures what data an organization collects, how long they keep it, who can access it, and ways to analyze it for bringing optimum solutions around business challenges, operations, product/service, security, and others.
DefineRight is a business enablement service provider that truly understands the importance of data and its role in accelerating a business and its growth. Our expertise in data engineering and related solutions helps eCommerce businesses tackle their challenges, optimize their process, and scale their growth.
What is Data Engineering in e-Commerce?
eCommerce is an industry that is evolving dynamically with the changing technology. Many eCommerce giants are harnessing the power of data with cutting-edge technology to improve their services and scale their growth.
Big eCommerce players like Amazon utilize their high volume of customer data to personalize each interaction, optimize pricing, advertisement, supply chain, and even control frauds. Fashion giants monitor their customers’ shopping trends to optimize personalized data and improve customer service.
Data engineering in eCommerce enables businesses to design and build systems to collect data from various sources and analyze it to gain an insight that helps transform these businesses. Here, the data source can be from website behavior, social media, purchase history, customer review, user logins, credit card transactions, etc. Analytical tools with AI and ML help analyze these big data to study the customer’s needs and buying patterns and predict what they will buy and when.
Data engineering helps create a system that can smartly route, persist, and enable low latency operations against a large volume of data. The data analytics-based strategies help make businesses effective decisions to deal with operational complexity and cost.
How Data Engineering is Driving the eCommerce Sector
eCommerce has changed the way people shop, and data science is enabling the sector, devising a strategy to boost their conversion rates, product visibility, product categories, SKU availability, and much more.
The following list shows how data and data analysis drives the eCommerce industry.
Better Search
Providing what a user wants and when it searches on the portal has always been tricky and complex for eCommerce businesses. While using a search bar to search for a product, earlier users had to enter a preferred word, hoping it matches with the word set on the site’s backend for their query.
However, the scenario with data engineering has changed, providing more flexibility. Now, you do not need to remember the exact keyword for a product. Advanced data engineering technology helps display all the related products based on your search for the preferred product, even if you do not write the exact keyword.
For example, if a search for a particular product on eBay using a specific keyword, the system search not only for that keyword but related nouns, synonyms, and phrases used for that product.
- Personalization and Targeted Advertising
Personalization and targeted advertising are gradually trending in the eCommerce industry. Data analysts are helping the industry know more about their customers by tracing the previous activities of the buyers, browsing requests, preferences, etc., to predict their future buying patterns. Based on it, organizations can make recommendations and shape their marketing campaigns.
Companies like Amazon and Netflix use a ‘collaborative filtering’ system to filter a user based on shopping patterns and behaviors. They recommend products to these users based on what purchasing behavior of people of the same profile.
- Predictive Lead Scoring
The application of Artificial Intelligence and Machine Learning in analyzing customer data is helping eCommerce businesses to predict what and when a customer buys the product. Predictive algorithms track everything, from customer needs to their personal information such as contact details, income level, etc., to understand which product will better correlate with a particular customer.
Through Predictive Lead Scoring, businesses analyze the behavior of prospective customers, determine their validity, and rank them based on their value. Automating predictive lead scoring algorithms with the help of big data can predict which prospect most likely can convert into a customer.
- Optimize Pricing of ProductsÂ
Consumers are always eager to get a good price deal for their products. E-Commerce giants utilize big data to discover opportunities to lower product prices in specific seasons. Flipkart, Amazon, and sites offer a discount on their best-selling products to encourage consumers. It also helps make the perception that they provide products at a low price to consumers.
Data-backed pricing of products leads to a 2-7% growth in business margins and an average ROI growth of 200%-350% annually. Thus, data-driven price management proves very effective for these e-Commerce sectors.
Benefits of Data and Analytics for eCommerce
A study by BARC shows that big data and analysis benefits e-Commerce businesses mainly in four ways; strategic decision making, operational processes, customer understanding, and cost reduction.
Strategic Business Decisions
Businesses can leverage data engineering with real-time analytics for making more informed and strategic decisions. Around 50 % of structured data collected through IoT gets used for strategic decisions by businesses.
Improve Operational Processes
Big data algorithms help analyze customer behavior and their shopping data, market trends, and supply chains. Organizations improve their operational processes by identifying optimum inventory levels warehouses need to maintain. Also, predictive analysis helps calculate checkout wait time and use this data to improve customer experience.
Better Understanding of Customers
Data analysis identifies and predicts customer needs and expectations, their search result trends, and even discover which product will suit their need. Building marketing and SEO strategies based on these results increase customer satisfaction and retention.
Reduction in Operational Cost
e-Commerce organizations use data engineering to minimize operational costs by optimizing their operation process, planning, supply chain activities, meeting customer expectations, etc. Investment in third-party logistics reduces the cost per team and optimizes operational methods.
Data Engineering Bringing Growth and Economies of Scale to eCommerce
The impact of data engineering on the eCommerce industry has been immense, from customer needs and expectations, marketing activities, operational planning and process, strategic decision making, and supply chain to cost optimization. Data engineering with advanced technology enables the sector to grow and scale its economies.
DefineRight enables eCommerce businesses in this drive of growth by providing data engineering solutions. We can dig and analyze a high volume of data to understand customer behavior and patterns.
The application of advanced technology for predictive analytics helps you make strategic decisions and marketing initiatives. We also support eCommerce businesses to accelerate with our business enablement services around people, process, data, and technology.