Understanding the Need for Advanced Analytics in an E-Commerce Platform

E-commerce has evolved from basic online transactions to complex, omnichannel customer experiences providing immense data. To keep pace, merchants need advanced analytics that deliver comprehensive, actionable insights from this data to guide strategic decisions and optimize performance.

Legacy analytics platforms built into e-commerce solutions (like Adobe Commerce, Big Commerce, Shopify and others) provide basic reporting but lack the advanced capabilities around predictive modeling, customer segmentation, attribution analysis, and other advanced capabilities now considered critical for merchants. Integrating dedicated advanced analytics platforms bridges this gap for data-driven operations and creates a central 360-degree customer view of transactional and digital behavior. This consolidated allows merchants to conduct complex analysis on conversions, operational efficiency, marketing performance, lifetime value forecasting, and more. These insights inform strategic decisions across areas like inventory planning, campaign targeting, and resource allocation that can help the e-commerce enterprise increase profits

This article dives into the key reasons why your e-commerce platform should be utilizing advanced analytics and a step-by-step process to get the most out of it.

Key Reasons to Integrate Advanced Analytics with your E-Commerce Platform

Adobe Commerce (as a leading PaaS enterprise e-commerce platform), BigCommerce, and Shopify offer robust core capabilities but have basic built-in analytics and reporting. Seamlessly integrating advanced analytics (like GA4) adds immense strategic value through:

Holistic Data Consolidation

  • Syncing commerce data from multiple channels into a unified analytics platform
  • Eliminating data silos for comprehensive analysis

Enhanced Reporting and Visualizations

  • Highly customizable reporting dashboards with visualizations
  • Interactive features like filtering, segmentation, and drill-downs

Predictive Modeling and Forecasting

  • Statistical models predicting trends, customer behavior, and more
  • Scenario planning through simulations and estimations

Attribution Modeling

  • Quantifying the influence of various marketing efforts on conversions
  • Optimizing channel spend allocation for better ROI

Customer Analytics and Segmentation

  • Analysis to categorize customers based on common attributes
  • Targeted experiences aligned with customer needs

AI and Machine Learning

  • Automated pattern detection, decision recommendations
  • Continuous analytics model improvement over time

These allow merchants to shift from hindsight to insight to foresight in management decisions through analytics aligned with strategic goals.

Integrating Best-of-Breed Platforms: An Example (GA4 and Adobe Commerce)

One of the leading advanced analytics platforms, GA4 (formerly Google Analytics), offers enterprise-grade analytics far beyond legacy capabilities. Below are more details on how GA4 levels up Adobe Commerce.

Compared to GA3, GA4 stands out by:

  • Processing extensive, complex e-commerce data
  • Cross-channel measurement with advanced attribution
  • Predictive modeling and machine learning
  • Custom dashboards and flexible visualizations
  • Granular segmentation and cohort analysis
  • Event measurement and reporting

Meanwhile, Adobe Commerce provides a robust infrastructure for customizable digital commerce experiences with extensive built-in tools for merchandising, CMS, order management, and more.

Integrating GA4 unlocks significant mutually-reinforcing benefits:

For Adobe Commerce merchants:

  • There is no need to export data files manually from Commerce to Analytics
  • GA4’s processing is applied directly to Commerce data
  • The system syncs the analysis to related transaction and inventory data
  • The integration automatically reflects changes across platforms
  • This consolidation and synchronization means Adobe Commerce merchants have up-to-date, unified insights to optimize commerce performance.

For GA4 advanced analytics:

  • Access to rich e-commerce data from Adobe Commerce
  • Ingestion via API eliminating reliance on tags
  • Ability to ingest 100% of Adobe Commerce data with enhanced accuracy
  • Adobe Commerce data supplements existing analytics data
  • This additional high-quality e-commerce data amplifies analysis fidelity for GA4, resulting in more reliable model outputs to drive operational decisions.

In Practice: Launching an Attribution Modeling Program

Adobe Commerce merchants that have invested in the proper and strategic GA4 integration can immediately consider launching an attribution modeling program to improve marketing ROI. Optimizing marketing channel spending requires balancing upper-funnel brand awareness efforts with lower-funnel direct response…a perennial challenge.

A valid attribution model quantifies each channel’s true downstream conversion impact, guiding optimal budget allocation aligned to strategy. Here are the key steps involved.

Step 1: Configuration
  • Integrate web and marketing analytics with Commerce data.
  • Ingest granular user journeys across channels into GA4.
  • Classify marketing channels tagging UTM parameters.
Step 2: Modeling
  • Analyze historical conversion data.
  • Attribute channel influence on each conversion.
  • Quantify overall channel value with machine learning algorithms.
Step 3: Activation
  • Compare channel values to budget.
  • Reallocate over/undervalued channels.
  • Simulate impact before changes.
Step 4: Optimization
  • Implement revised channel budget.
  • Monitor performance through GA4 reporting.
  • Iterate as needed based on outcomes.

This builds a closed-loop process where advanced analytics directly guides strategy, demonstrating integration value. The results of a marketing spend are more easily and accurately analyzed and marketing dollars are invested in programs that provide greater return and value.

There’s more: Limitless Potential with AI and Machine Learning

Assume you are successful at moving to real data-driven decision making through an advanced analytics program. That’s awesome and you’re on the road to deeper profits. While attribution analysis delivers tactica and profitable returns, the long-term potential of an integrated advanced analytics solution is far greater. Applying AI and machine learning unlocks transformational opportunities.

Algorithms can ingest vast volumes of granular customer behavior data into Commerce and GA4, detecting subtle patterns and generating predictive insights at scale. Using these algorithms evolves analytics from reactive to proactive – accurate models prescribe micro-segment strategies, predict churn risks, estimate lifetime value per customer, forecast inventory needs, recommend promotions, and more…all customized via integration.

The integrated analytics engine continuously self-tunes predictions through iterative learning cycles as new data gets ingested, ensuring recommendations remain hyper-relevant to fuel data-driven automation. Leadership, thus, can focus less on analysis and more on activation.

The end vision is a natively intelligent commerce platform that analyzes, predicts, prescribes, and executes customer experiences tailored to the micro-segments of one. With AI/ML-powered advanced analytics integrated at the core, Adobe Commerce merchants lead markets through strategic data capabilities others cannot rival. The time for integration is now.

Key Takeaways

It’s easy for e-commerce merchants to depend on the basic analytics provided by their platforms. After all, the sales reports are almost always right at a merchant’s fingertips with the core reporting all the platforms provide.

To summarize, here are the top reasons why advanced analytics can help you get ahead:

  • Integrating your e-commerce platform with advanced analytics like Google Analytics 4 unlocks immense high-value capabilities beyond legacy commerce analytics.
  • Attaining tangible returns requires strategic implementation focused on 2-3 key business priorities via phased programs.
  • Over the long term, leveraging AI and ML across integrated datasets has transformative potential for automated, tailored customer experiences.
  • With the right approach, integrated advanced analytics fuels data-driven decision-making to optimize e-commerce performance.

If you want guidance on how to up-level your e-commerce platform’s current analytics capabilities, reach out to our team. We are happy to help guide you in the right direction!