Big data analytics in B2B e-commerce provide business-to-business companies with insights into customers, sales processes, orders, inventory, and more.
B2B managers can use these insights to enhance the customer experience, improve lead generation, and forecast future outcomes.
ETL, ELT, and ReverseETL are some of the data integration methods used for B2B e-commerce analytics.
By moving e-commerce data to a warehouse, you can run that data through BI tools.
Integrate.io is a low-code/no-code data warehousing integration platform that simplifies the entire process.
Big data analytics in B2B e-commerce can generate unparalleled insights for business-to-business companies about client behavior, purchasing trends, inventory management, and the overall supply chain. However, collecting, processing, storing, and preparing data for e-commerce analytics can be challenging when so many data management tools target B2C companies. In this guide, learn more about big data analytics in B2B e-commerce and how a data warehousing solution like Integrate.io can make the process easier.
Table of Contents
- What is Big Data Analytics in B2B E-commerce?
- What are the Benefits of Big Data Analytics in B2B E-commerce
- How does Big data Analytics in E-commerce Work?
- Challenges of E-commerce Data Analytics in B2B
- How to Perform Big Data Analytics in B2B E-commerce
- How Integrate.io Helps With B2B E-commerce Data Analytics
Integrate.io is the new data warehousing integration platform for e-commerce that performs ETL, ELT, ReverseETL, and super-fast CDC. Whether you specialize in B2B, B2C, or both business models, you can move data to a warehouse of your choice without lots of complicated code or programming. Integrate.io's native connectors remove the pain points associated with data integration, helping you identify patterns and trends in data without the hard work. Email email@example.com to learn how Integrate.io supports big data analytics in B2B e-commerce.
What is Big Data Analytics in B2B E-Commerce?
Annual revenue from the global big data analytics market could reach more than $68 billion by 2025, and an unlikely business model is helping fuel this growth. While big data analytics is commonly associated with business-to-consumer (B2C) marketing, business-to-business (B2B) companies can derive as much value from analyzing data as their customer-facing counterparts, especially for e-commerce.
Big data analytics in B2B e-commerce lets companies like yours collect large volumes of data from separate or 'siloed' sources and run that data through business intelligence (BI) platforms for valuable insights into your day-to-day operations. By identifying patterns and trends in e-commerce data, you can improve decision-making, optimization, and problem-solving in your enterprise.
What are the Benefits of Big Data Analytics in B2B E-Commerce?
Big data analytics can provide B2B companies with all kinds of intelligence about their e-commerce operations. Here are some of the benefits of big data analytics in B2B e-commerce:
Generate More Leads
By analyzing data, you learn more about the businesses that purchase your products and services. For example, you can review previously closed deals in specific campaigns and use that information to identify new lead generation opportunities. Running data through a BI platform like Tableau or Looker provides you with incredible insights into your lead generation processes, and you can analyze metrics like cost per lead, qualified lead volume, and sales volume.
Improve the Customer Experience
Another benefit of big data analytics in B2B e-commerce is better customer experiences. When you analyze data from e-commerce operations, you can discover more information about the businesses that use your products and services and deliver better customer service. For example, you can track metrics such as B2B sales, customer satisfaction (CSAT), customer lifetime value (CLV), retention rate, and churn rate and use this information to optimize communication and engagement with business clients.
Forecast the Future
E-commerce big data analytics techniques, such as predictive analytics, can forecast future outcomes in your B2B enterprise and prepare you for any events that might impact your company. For example, you can analyze data to identify potential cost-cutting opportunities in your enterprise or calculate the likelihood of a sales slump.
Big data analytics in B2B e-commerce lets you evaluate previous marketing initiatives and learn which campaigns resonate the most with business clients. That can help you fine-tune B2B marketing activities in your organization and create campaigns that drive more sales and revenue.
How Does Big Data Analytics in B2B E-Commerce Work?
Big data refers to all the large data sets that flow in and out of your B2B organization. In an e-commerce context, that might mean data you collect about customers, transactions, orders, sales processes, marketing campaigns, and inventory management.
By moving all that data to a business analytics solution, you can quickly access information about your business, often in real-time. The best analytics tools use technologies such as artificial intelligence (AI), machine learning (ML), and natural language processing (NLP) to turn raw data into actionable insights that benefit your e-commerce teams. You can then view data on reports, charts, graphs, and dashboards in one place.
B2B companies that use big data analytics for e-commerce and other functions can uncover information that drives business growth, improves engagement, enhances communication, personalizes customer experiences, and more. Analytics can also improve account-based marketing (ABM) and target businesses that provide ongoing value.
Challenges of E-Commerce Data Analytics in B2B
E-commerce data might exist in lots of different systems that don't connect with each other, which makes big data analytics difficult.
Say you own a small B2B company that sells products and services to business clients over the internet. You might have:
A B2B customer relationship management (CRM) system that processes data about business clients like Salesforce.
An enterprise resource planning (ERP) system that manages your financials.
A transactional database that supports data operations.
Several SaaS tools.
You might even store data in social media platforms, email marketing automation tools, and other e-commerce platforms.
With all this data in different places, analytics can seem almost impossible. Various teams might use different programs, and all these siloed systems might be separate from the rest of the organization. Ideally, you need to move all your data to a 'single source of truth' (SSOT), making it easier to run that data through BI tools and generate the big data analytics you require. However, that can be hard when data exists in multiple formats, or you use older legacy systems.
Integrate.io is a data warehousing integration solution that connects siloed e-commerce data by moving it to a warehouse for analytics. The easy-to-use platform streamlines the data integration process and removes all the jargon associated with moving information between data sources and destinations. Email firstname.lastname@example.org to learn more.
How to Perform Big Data Analytics in B2B E-Commerce
When B2B e-commerce data exists in multiple systems across an enterprise, the best way to perform analytics is to move that data to a target system like a data warehouse. Once data reaches the warehouse, you can use BI tools and algorithms and generate insights that improve business decisions.
Some of the most common data integration methods for big data analytics in B2B e-commerce are:
Extract, Transform, Load (ETL), which involves extracting e-commerce data from a source, transforming the data into the correct format, and loading it to a warehouse like Snowflake, BigQuery, Amazon Redshift, or Microsoft Azure for data analysis.
Extract, Load, and Transform (ELT), which involves extracting data and loading it into a warehouse before transforming it. ELT is ideal for unstructured data or large loads of e-commerce data.
ReverseETL pushes data from a warehouse back to an operational system like a SaaS tool.
Change Data Capture (CDC) syncs two or more databases, letting you view changes made to those databases.
All these methods require knowledge of data engineering or programming, meaning some B2B companies will struggle with data integration for analytics. Investing in a data warehousing integration, however, can help you move data to your desired location without the need for lots of code.
How Integrate.io Helps With B2B E-Commerce Data Analytics
Integrate.io performs ETL, ELT, ReverseETL, and fast CDC, helping you integrate e-commerce data for analytics. With its simple drag-and-drop interface and out-of-the-box connectors, you can develop complex data pipelines in minutes and connect databases, CRMs, ERPs, and more.
Some B2B companies might worry about moving sensitive e-commerce data outside of their organization to a third-party warehouse or BI tool. However, Integrate.io uses enhanced data security, complies with major data governance regulations, and hosts and stores data on Amazon's data centers, providing peace of mind.
Integrate.io can help you with big data analytics in B2B e-commerce. The platform provides you with an SSOT for all data decisions in your enterprise, helping you generate operational insights that grow your business. Email email@example.com for more information or schedule an intro call now.