4 Best E-commerce Data Integration Benefits to Streamline Your Business

Last Modified: July 25th, 2023


In an ever-growing competitive market, e-commerce businesses are embracing personalization for business growth. According to a report, 89% of e-commerce businesses are investing in personalization. As there is no dearth of data in e-commerce companies, every e-commerce business must move towards offering personalized experiences.

However, to ensure superior accuracy of personalization, e-commerce businesses need to deploy data integration practices. Today, integrating data from various sources has become challenging as data resides in several sizes, formats, and structures. Various data integration tools available in the market help businesses connect different databases and get advantages from e-commerce data integration benefits.

With robust database integration, e-commerce businesses can combine data from customer touchpoints. This comprehensive data can then be used to train machine learning models to offer personalized experiences for customers. The e-commerce data integration benefits include better user engagement, a surge in sales, and more. However, you need to choose the right data integration based on your needs to streamline business operations. 

In this article, we shall learn about data integration and how business owners can reap e-commerce data integration benefits.

Let’s get started!

Options for Data Integration

A well-thought-out integration strategy is necessary to transfer data from one system to another effectively and for other e-commerce data integration benefits. Companies can select from various data integration options depending on their specific business objectives and IT infrastructure. These options are given below.

Options for Data Integration
Options for Data Integration

Data Replication

  • Data Replication involves creating multiple copies of data and storing them across different locations. The goal is to provide real-time data consistency for all users, regardless of where they access the data.
  • In the case of a disaster, data replication enables a company to have up-to-date backups of its data. This means accessing data through alternate servers is still possible if there is a technical fault brought on by malware, hardware failure, or any other issue.
  • Additionally, replicating data helps to reduce data load on the main server, enhancing network performance.

Data Consolidation

  • Data Consolidation is a type of data integration that involves creating a copy of all source datasets in a centralized system. It is generally accomplished using ETL technology.
  • ETL or extract, transform, and load technology collects data from multiple sources, converts it into a usable format, and stores it in a central database or data warehouse.
  • This e-commerce data integration benefits organizations to effectively manage and centralize their data, reducing the complexity of data storage and retrieval.

Data Virtualization

  • Data Virtualization integrates data by adding a virtual layer instead of copying or relocating data to a new database. The virtual layer links to all data sources and acts as a front-end application to provide consistent access to the data.
  • It does not own a data model of its own. Instead, it serves as an intermediate, taking requests, gathering the necessary data from connected databases, and displaying a consolidated view.
  • With no duplication or transfer of data required, this method allows quick access to data from several sources.

Data Synchronization

  • Data synchronization involves continuously integrating data from many devices. It further automatically updates any changes to ensure consistency between the systems.
  • By guaranteeing consistency between each data source and its corresponding endpoints, this e-commerce data integration benefits business by ensuring the data’s accuracy, security, and compliance. By preventing data conflicts, data synchronization reduces the risk of errors and low-quality information, improving the trustworthiness of the data.
  • You can also choose data integration solutions on the basis of data streaming type. 

Batch Data Integration

  • Batch Data Integration collects data in bulk and transfers it at predetermined intervals or once a specified quantity has been obtained.
  • This e-commerce data integration benefits your company when there is flexibility in the timing.

Real-Time Data Integration

  • Real-Time Data Integration integrates streaming data into existing records in real-time through established data pipelines. With change data capture (CDC) and transform-in-flight, you can complete data transfer in a matter of milliseconds.
  • This approach is important in applications where immediate access to data is necessary.
  • Irrespective of the data integration type, the underlying principle is the same: collection and integration of data sources. But every e-commerce company may not share similar business goals. Therefore, to choose the right data integration for maximum benefits, you must consider factors like long-term-short-term goals, available resources, and scope of integration. 
  • Generally, each company has unique targets (both long-term and short-term) and must determine which data will endorse those targets. The company’s business objectives, market trends, target audience, and essential competitive benchmarks will influence the selection of data integration methods.
  • Other issues can be addressed after identifying the company goals that data integration must serve. Meanwhile, it is important to ensure that the data adhere to compliance regulations and that integrated data is visible to every stakeholder.

How can E-commerce Data Integration Benefits Streamline Your Business?

E-commerce Data Integration Benefits
E-commerce Data Integration Benefits

Let me take the example of Amazon and explain how data integration benefits them to streamline the business.

  • For Amazon, data originates from disparate sources like its website, social media platforms, tracking tools, etc. 
  • Capturing and storing the data (ex: how many people purchase a particular item, geography split of different segments of customers, etc) is the first stage in data management. But, its full potential is only realized if it is appropriately integrated. 
  • By integrating data, Amazon can use the data captured to respond to critical corporate challenges and enhance rational decisions. For example, if Amazon notices the demand for a product going up and they face trouble operating accordingly, the trend should be analyzed and noted for optimizing future demand. 
  •  Integrated data insights, when paired with digital technologies like machine learning,  can help to understand customer behavior and forecast sales. This also includes recommending products, discovering growth factors for various items, and more. 

That’s the case with Amazon. Now, let’s take a look at the big picture. Here are the e-commerce data integration benefits: 

  • Predictive Analysis: Integrating diverse data sources enables e-commerce companies to enhance their predictive analytics and accurately predict consumer behavior. Companies can integrate crucial customer data from marketing platforms, the time between page visits and purchases, and more. Using such data, predictive analytics can aid companies in devising creative strategies to lower cart abandonment, accelerate buying time, and more. The data is also used to generate inventory estimates in response to seasonal variations.
  • Better Data Integrity and Synchronization: Synchronizing different applications used to run an e-commerce business is crucial for maintaining smooth and efficient operations. When several systems are synced, every component is updated appropriately, lowering the possibility of errors and issues. For instance, inventory data, orders, and customer information are automatically updated when an order is placed. This implies that if a consumer cancels an order, the system will promptly update the order and inventory data, preventing orders from being processed incorrectly. 
  • Identifying Trends: Using ML algorithms on top of integrated data can help e-commerce companies eliminate reliance on guesswork in identifying the next popular products. For instance, leveraging social media data mining can help discover products generating buzz on social media. Insights from social media data can be used to predict the next popular products in various categories accurately. Companies can also incorporate sentiment analysis to comprehend the context of online product reviews, such as whether the conversations are good or negative. 
  • More Selling Opportunities: Using ML algorithms on integrated data can increase your sales through upselling opportunities. By using data from different sources, e-commerce retailers can investigate a customer’s likelihood of accepting an upsell and what they would buy in addition to their current cart items. Cross-selling could also be accomplished by looking at consumer browsing patterns or recommending related items that other customers often buy with a certain product. This data-driven strategy enables companies to respond to specific customer needs, which increases the likelihood of successful online purchases.

From the above-mentioned e-commerce data integration benefits, you might have understood that it is essential for e-commerce companies to make intelligent decisions and have a competitive edge over rivals. By integrating data from multiple sources, companies can unlock insights that can be used to respond to critical challenges.

The e-commerce data integration benefits include predictive analysis, improved data integrity and synchronization, enhanced customer experiences, and more. To maximize benefits, decision-makers should take the time to define their organization’s goals and select the data integration approach that aligns with business needs.

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Preetipadma Khandavilli
Freelance Technical Content Writer, Hevo Data

Preetipadma is passionate about freelance writing within the data industry, expertly delivering informative and engaging content on data science by incorporating her problem-solving skills.

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