Statista forecasts that by the end of 2024, the global revenue of the eCommerce market will reach an astonishing USD 4,117.00 billion, creating a more competitive market than ever before. Those looking to strive in this competitive industry are already leveraging eCommerce marketing analytics and gaining invaluable insights to get the bigger piece of the pie.
In this article, we will take a deeper look into this critical aspect of eCommerce companies growth and share our experience on the effective utilization of E-commerce data analytics.
The Growing Impact of eCommerce Analytics
According to a survey by Forbes, 95% of businesses face at least some kind of need to manage their unstructured data, while over 40% say they need to manage unstructured data frequently. The eCommerce industry is not an exception, furthermore, analyzing vast amounts of data can be turned into game-changing strategic insights, enhancing almost any business operation.
The Benefits of e-Commerce Analytics
Here are some of the most impactful advantages of eCommerce customer analytics that result in the next-level competitive edge for organizations that use it:
- Improved Demand Forecasting: data analytics eCommerce allows businesses to anticipate changes in demand based on historical trends, seasonal patterns, and customer behavior. We have already overviewed the process of demand forecasting in Retail with machine learning, so you are welcome to read this article for more details.
- Optimized Inventory Management: organization can drastically improve inventory management, reducing holding costs and minimizing excess stock by analyzing real-time sales data, historical trends, and supply chain variables.
- Enhanced Customer Retention and Loyalty: companies can personalize customer interactions, anticipate needs, and improve conversion rates thanks to analysis of purchase history, browsing patterns, and preferences.
- Effective Fraud Detection and Prevention: unfortunately, eCommerce businesses are frequent targets of different types of fraudulent attacks. ML-powered data analytics solutions can help prevent fraud in real time by spotting suspicious transaction attempts and sending instant alerts.
- Higher Sales and Marketing ROI: data-driven eCommerce analytics positively impact marketing efforts as well, allowing companies to target audiences more effectively and spend budgets wisely.
Dmytro Tymofiiev
Delivery Manager at SPD Technology
“What makes eCommerce data analytics initiatives work and unlock all aforementioned benefits is having a structured and scalable data analytics strategy, ready to adapt to any market changes and meet evolving customer expectations. So, it will be a good idea to craft such a strategy relying on real business and tech expertise, before embarking on an eCommerce web analytics project.”
How Does eCommerce Data Analytics Work?
eCommerce data analytics works by collecting, processing, and analyzing data from various online business activities. Customer online interactions or at the physical point of sale is a great example of this. Leveraging various analytical techniques, businesses analyze the data to identify trends, patterns, and correlations. This may include, but is not limited to, using the following analytical techniques:
- Descriptive Analytics: delivers information of what happened in the past, by summarizing all historical data.
- Diagnostic Analytics: dives into specific events of the past and builds cause-and-effect relationships.
- Predictive Analytics: allows forecasting events that might happen in the future based on historical data patterns.
- Prescriptive Analytics: this one goes beyond eCommerce predictive analytics by offering guidance and potential strategies for scenarios that might occur.
Dmytro Tymofiiev
Delivery Manager at SPD Technology
“In a real-world scenario, all four aforementioned types are combined in a layered, looped approach to create a comprehensive eCommerce analytics solution that not only accurately interprets the previous performance, but also helps to proactively shape the future growth. ” –
Main Application Areas of Analytics for eCommerce
The eCommerce data analytics techniques we described above are used by businesses to deal with data that comes from different sources (eCommerce website, social media, customer feedback etc.). Let’s discover what are the application areas of analytics for eCommerce that deliver the most valuable insights for businesses.
Customer Sentiment Analysis
Being one of the main implementations of Natural Language Processing (NLP) for retail business and eCommerce, it allows for automatically assessing the opinions of customers on specific brands or products. This is achieved by letting ML algorithms analyze reviews, comments, social media posts, and other information online.
Social Media and Influencer Analysis
Similarly, companies analyze the performance of specific social media accounts, processing engagement levels, brand mentions, and sentiment on a specific platform. In addition to that, advanced data analytics can also evaluate influencers, recommending the most suitable and effective potential business partners.
Customer Segmentation Analysis
Modern solutions with the right amount and quality of data can easily segment customers based on demographics, purchase history, or browsing behavior, allowing businesses to tailor and pinpoint their marketing efforts, increase revenue, and minimize the bounce rate.
Market Basket Analysis (Affinity Analysis)
This type of analysis identifies the products that are bought together, helping businesses to develop highly effective cross-sell and upsell strategies. With automated analysis of purchase patterns, platforms can recommend personalized bundles, without the involvement of employees.
Churn eCommerce Analysis
This identifies specific customers with a high risk of leaving, helping organizations to take action before important clients leave. In practice, ML models can detect the signs of churn, including lowered purchase frequency or engagement, and automatically deploy targeted retention campaigns.
Cohort Analysis
With this analysis, organizations have customers grouped based on particular characteristics or experience within a given timeframe, to track the level of their engagement and retention over time. The insights obtained here, allow evaluating the success of marketing campaigns over certain periods.
Clickstream Data Analysis for eCommerce
This one is tracking the customer interactions with a website, resulting in an overview of user behavior, page visits, time spent, and actions taken. When done correctly, it allows the detection of problems in the user journey, suggests page load time optimizations and website design changes, contributing to better conversion rates as a result.
eCommerce Data Analysis for Price Optimization
Modern, data-driven price optimization tools can analyze competitor pricing, and changes in demand, detect emerging purchase trends to recommend the most optimal prices for products. Some of the eCommerce data analytics tools can optimize product prices to align them with users’ financial profiles. To build such a profile, user behavior, demographic data, and indirect indicators (like what price ranges are users clicking on, and how often they abandon cart items based on price) can be used.
Customer Satisfaction (CSAT) and Net Promoter Score (NPS) Analysis
The information from these two scores is critical for getting a direct insight into customer satisfaction and loyalty. Combined with feedback analysis, automated solutions can track customer sentiment and understand the factors that influence customer loyalty.
Gen AI and Analytics in eCommerce – The Next Level Use Cases of Their Combination
Statista reports that the global generative AI market will reach USD 36.06 billion by the end of 2024, showing the massive popularity of this ML-powered advancement, focused on content creation. But how does generative AI work in conjunction with eCommerce data analytics and what impact do they make?
Generative AI models that are used for E-commerce analysis require a large amount of data from customer behavior, transaction histories, and market trends. Based on this information, Generative AI identifies the most prominent tendencies and can come up with personalized product recommendations or even tailored marketing messages.
Data analytics, in turn, continuously feeds data into Gen AI models, creating a loop in which insights collected from data analytics are being enhanced by Gen AI and result in improving customer experience. As a result, the impact is creating ultimately personalized experiences with dynamically tailored content in resonance with the demands of each user.
Now, let’s discuss some specific use cases of Generative AI and data analytics combination.
eCommerce Web Analytics for Hyper-Personalized Shopping Experiences
Generative AI is capable of creating custom product recommendations in real time, just as the customer is browsing the website. These suggestions are being adjusted according to specific click patterns and other engagement metrics captured through eCommerce website analytics.
Automated Content Creation for Products and Marketing
Based on top keywords, product attributes, and customer preferences, provided by web analytics for eCommerce, generative AI can create SEO-optimized descriptions of the products, text for the chains of marketing emails, or even entire ad articles on specific products or services, which saves time for marketers.
Conversational AI for Enhanced Customer Support
Integration of data analytics multiplies the benefits of using chatbots and virtual assistants, as it helps Gen AI to understand the most common customer queries, pain points, and purchasing patterns. With this information, bots and assistants can comprehensively answer questions and suggest the most appropriate similar products, and, in some cases, help with complex queries as well.
Inventory and Demand Forecasting with Generative Simulations
While data analytics is tracking sales patterns, trends, and purchasing data, Generative AI can create insights in seconds by simulating multiple scenarios for inventory demand prediction, which is especially useful in peak seasons or during special promotions.
eCommerce Marketing Analytics for Dynamic Product Bundling and Cross-Selling
This is another area, where the combination of the two shines. Data analytics identifies customer purchasing patterns while Gen AI delivers dynamic, relevant, and personalized product bundles tailored to specific customers. In cross-selling cases, AI can even come up with custom promotions on complementary products.
Real-Time Shopping Behavior Analysis and Abandonment Recovery
Analytics in online retail allow businesses to detect the precise moment in real time when the customer is about to abandon the cart. Gen AI is able to instantly respond to this, and create personalized messages or discounts at the spot to prevent this unfortunate scenario from happening.
Dmytro Tymofiiev
Delivery Manager at SPD Technology
“While we are still at the stage of the early GenAI adoption, we, at SPD Technology, have already completed numerous projects that prove its effectiveness in combination with data eCommerce analytics. I think we should expect not only the transformation of the industry from the two but also big steps in the evolution of eCommerce as the responsive and adaptive space during the next few years.”
The Best Practices for eCommerce Analytics Implementation
The advanced use cases of eCommerce data analytics and the ultimate benefits it delivers make both large and small retailers think about implementing this technology into their workflows. Below are some of the best practices for turning eCommerce analytics into a disruptive power to enhance business operations.
Aligning Analytics Goals with Business Objectives
As the starting point of data analytics implementation, all data-related initiatives should be closely aligned with key business goals, for example, improving conversion rate and customer acquisition, optimizing supply chains, or inventory management. Here, at SPD Technology, we understand that data should have a direct value, so we collaborate closely with our clients to identify specific objectives and design analytics frameworks that accommodate these goals.
Opting for a Centralized and Scalable Data Infrastructure
The importance of data integration could not be overstated, as information from multiple channels like websites, CRM systems, or mobile apps should flow without any hitches into a central repository for effective analysis. In our projects, we always prioritize advanced cloud infrastructure that fully covers current and future data requirements, with scalable environments ready for traffic spikes.
Developing a Data Governance Framework for Security and Compliance
Security measures like these are mandatory for the data-intensive eCommerce industry, with the highest demands on customer privacy. A data governance framework specifies how information is being collected, stored, accessed, and processed adhering to current regulations, while clear policies protect data across departments. We, at SPD Technology, hold data governance of the utmost importance, always implementing encryption standards, regular compliance audits, and role-based access controls in our projects.
Implementing a Data Quality Management Process
The effective process of data quality management addresses common issues like duplicate records, incomplete fields, and outdated information, ensuring that data-driven insights are precise and reliable. We make sure that our clients can trust their analytics by implementing fully automated validation checks and data cleansing protocols, as well as conducting continuous monitoring practices.
Dmytro Tymofiiev
Delivery Manager at SPD Technology
“These practices are just a bare minimum of what we are doing in the real-world projects because every case is unique and requires tailored approaches to get the best results. Our company is doing everything possible to ensure accurate E-commerce insights for the clients, harnessing the power of AI/ML and Big Data, while following proven security requirements.”
eCommerce and Data Analytics – The Most Common Challenges
We, at SPD Technology, understand the wide range of unique challenges that the implementation of data analytics in eCommerce creates. In this section, we will discuss them and share our real-life solutions.
Integrating Data from Multiple Sources
This is a quite common problem since the data in our projects is often scattered across entirely different platforms, websites, CRMs, inventory management systems, and social media channels. The fragmentation hinders the possibility of a holistic view for our clients, and implementation of cutting-edge solutions, like machine learning models for fraud detection.
We unify disparate data sources into a single ecosystem by setting up centralized data platforms that sync data in real time, enabling fast decision-making without any friction.
We discussed some of the latest applications of machine learning in retail in our recent article!
You are welcome to read it for some of the more intricate insights!
Maintaining Data Quality and Consistency
This is another crucial challenge with serious consequences, as inconsistent and inaccurate data hinders eCommerce customer data and analytics, resulting in poor recommendations and missed opportunities.
We excel in providing data quality management of the highest quality and conduct effective automated validation checks, regular audits, and deduplication strategies, ensuring data is consistently clean, accurate, and actionable.
Achieving Real-Time Analytics
This is essential for the industry, as quick decisions are required for responding to dynamic situations including sudden traffic spikes, price adjustments, or inventory shifts.
We implement scalable analytics solutions utilizing streaming data pipelines and event-based architectures, allowing data teams to monitor essential metrics, such as cart abandonment rates and inventory status. Our clients always make timely, data-driven adjustments that keep them agile and responsive to evolving market conditions.
Ensuring Data Security and Compliance
Dealing with financial data, and compliance with all relevant regulations like GDPR and CCPA is mandatory, and failing to secure data appropriately can lead to severe penalties and reputational damage.
We know how to implement data management strategies of the highest security requirements, specifically tailored to eCommerce analysis with extensive usage of advanced encryption protocols, strict access controls, and routine security audits. Additionally, we educate our clients on the compliance requirements, helping to avoid costly penalties.
Implementing Advanced Customer Segmentation
Effective segmentation is vital for targeted marketing, yet many businesses still struggle to achieve it due to the large volumes and complexity of data in this industry.
We rely on our proficiency in Artificial Intelligence and machine learning to develop models that effectively segment customer data, based on behavior and demographics. For our team, ensuring the highest data quality is of the utmost importance since we are in charge of implementing winning solutions for customer segmentation.
Handling High Data Volume During Peak Periods
Dealing with high volumes of data is a common challenge for the industry, especially when the peaks from seasonal events are happening. Due to this, data infrastructure efficiency can be impacted, leading to slower processing times and data bottlenecks.
Keeping this challenge in mind, we always build scalable architectures using cloud-based solutions. Our clients have stable access to real-time eCommerce performance analytics even during the busiest periods.
Extracting Actionable Insights from Complex Data
The raw E-commerce data by itself does not bring any business value, as many organizations struggle to make sense of complex data and derive any significant insights.
We have vetted data visualization experts in our team, who develop custom dashboards and highly tailored reporting tools to support specific business objectives. We work closely with our clients, to make sure that they correctly understand the analytics results, as we turn complex datasets into actionable recommendations.
Managing Costs and ROI of Data Analytics Investments
As with any software development project, keeping budget in control is a common concern that we are very familiar with. Making highly accurate estimates for a data analytics project is the proven practice for an experienced vendor like us, and is one of the advantages of strategic technology consulting that our clients receive.
Still, keeping the estimated budget in control is more difficult. To ensure transparency and proper resource management, we deliver phased implementations, making sure that early value is showcased, demonstrating to our clients the tangible benefits of technological initiatives, and helping companies maximize their analytics investments and see sustained ROI over time.
Dmytro Tymofiiev
Delivery Manager at SPD Technology
“To deal with these challenges, a holistic approach with the blend of tech proficiency, expertise, and practical experience is required. We at SPD Technology are able to provide you with strategic guidance to overcome the toughest challenges related to data analysis for eCommerce. We successfully overcame all aforementioned challenges in our projects, during our 18+ years of development experience for this industry.”
Building Tech Infrastructure for eCommerce Data Analysis – Tools, Technologies, Talent and Resources
Setting up an E-commerce analytics infrastructure requires orchestrating a set of components in an effective ecosystem for generating actionable insights, introducing marketing analytics, as well as improving customer acquisition efficiency. Let’s take a look at and discuss the key tech elements.
Tools
To capture, store, and interpret data, effective tools are required for each respective purpose. Here is our classification of the necessary ones:
Data Collection Tools
- Google Analytics & Adobe Analytics: provide critical insights into website traffic, user behavior, and conversion metrics.
- Google Tag Manager: Allows businesses to manage and deploy marketing tags on websites or mobile apps without conducting any modification to the code.
Customer Data Platforms (CDP)
Segment, Salesforce CDP, and Tealium AudienceStream are prominent examples in this category, as they effectively consolidate customer data from entirely different sources into a unified profile. These platforms showcase the true power of Artificial Intelligence for customer behavior analysis and provide personalized experiences based on the tracking of customer behavior data in real time.
Data Visualization and Business Intelligence (BI) Tools
These tools are designed to help businesses obtain a visual representation of complex data sets, and the most common off-the-shelf solutions include Tableau, Microsoft Power BI, Looker, and Google Data Studio.
Marketing Automation and Personalization Tools
Solutions from HubSpot, Marketo, Optimizely, Dynamic Yield, and Braze offer targeted email campaigns, A/B testing, and behavior-based messaging, which allow businesses to drive conversions through personalized content and automate marketing workflows.
Technologies
These technologies form the technical backbone of a modern eCommerce data analytics ecosystem, harnessing the full potential of data.
Data Integration and ETL (Extract, Transform, Load)
The list of most common data integration technologies include Apache Kafka, Fivetran, Stitch, and Apache NiFi, and they enable experts to collect data from different sources and in different data formats, standardize it, and prepare for future processing. While ETL processes make sure that data flows seamlessly across different platforms and is fully accessible for BI tools, data integration technologies ensure that data from various sources can be consolidated into a single source of truth
Data Storage and Warehousing Solutions
eCommerce datasets are typically enormous, so Snowflake, Amazon Redshift, Google BigQuery, AWS S3, Azure Data Lake, and Azure Synapse Analytics are the most common solutions with required scalable storage and data warehousing. These solutions centralize and organize diverse datasets, enabling businesses to efficiently store, query, and analyze large volumes of structured and unstructured data in data warehouses or data lakes, depending on the specifics of the data and needs of an organization.
Want to learn more about how to build a data warehouse?
Read our article and get our practical insights on this topic!
Data Processing and Big Data Technologies
Apache Spark, Hadoop, and Google Cloud Dataflow facilitate the processing of large datasets, enabling eCommerce companies to analyze millions of data points rapidly and truly benefit from the business impact of Big Data, uncovering the untapped potential of customer information.
Machine Learning and Predictive Analytics Solutions
The platforms including Google Cloud AI Platform, AWS SageMaker, DataRobot, H2O.ai, Scikit-learn, and TensorFlow represent off-the-shelf solutions that support the creation, deployment, and monitoring of ML models, enabling predictive analytics in eCommerce and personalized customer experience.
Human Resources
In addition to tools and technologies, there is a need for a proficient team of skilled professionals, will ensure that data will be collected, processed, and applied most efficiently:
- Data Engineers: they are responsible for designing, building, and maintaining data pipelines, as well as managing ETL processes and overseeing data accessibility.
- Data Analysts: these experts interpret data for decision-making, with the help of advanced Business Intelligence tools.
- Data Scientists: they develop, train, and refine ML models that can offer predictive analytics for eCommerce with personalized recommendations, along with other advanced features.
- Machine Learning Engineers: these experts focus on deploying ML models into production and maintaining their performance, working with data scientists to improve the models.
- Project Managers and Business Analysts: being an essential part of any project, these professionals keep everything on track by overseeing project timelines and budget, aligning analytics initiatives with business goals, and ensuring frictionless collaboration between all teams.
- Data Governance and Compliance Specialists: like with any data-related initiative, these experts are invaluable as they ensure that data practices comply with regulations like GDPR and CCPA.
Financial Resources
Investments across several areas are required if you want to set up an effective infrastructure. Here are those cost categories:
- Software Licensing Costs: E-commerce analytics software, BI tools, CDPs, and marketing automation platforms require recurring subscription costs.
- Cloud Infrastructure Costs: while cloud computing infrastructure benefits your organization greatly, you should be ready to be billed based on the usage of required solutions.
- Personnel Costs: keeping skilled professionals in your team, especially data analysts and ML engineers, can take a significant chunk of the budget.
- Training and Development: investing in upskilling and training of the team members is a natural process in the evolving technological landscape of data analytics for eCommerce, especially if you hire them in-house.
- Maintenance and Support: to perform at the highest capacity, analytics platforms require constant optimization, so you should be ready to pay for routine updates, system monitoring, and support services.
- Consulting and Implementation Services: finally, there is a high possibility that you will require external professional help to implement your E-commerce analytics software setup in a complex environment.
Consider Top-Tier eCommerce Analytics Services
Partnering up with a professional data analytics services vendor not only gives you instant access to the most effective modern tools and tech stacks but also allows gaining tangible, precise results in the shortest time possible. Accelerated time to value due to professional support will bring more benefits to your organization in the long run, compared to the long and costly process of setting up a digital commerce analytics environment with your internal experts.
Additionally, the professional involvement of a reputable vendor ensures the highest standards of data quality, as well as adherence to compliance and regulatory requirements. When qualified experts are in charge of eCommerce business analytics, the risk of costly errors is minimized, ensuring the effective budget management and sustainable growth of your data initiatives.
Partner with a Reliable eCommerce Business Analytics Development Vendor
Partnering up with us, you will get a reliable, vetted software development company with first-hand experience in building the world’s leading eCommerce solutions and unlocking the full potential of trending technologies like Artificial intelligence and the Internet-of-Things. We know how to drive innovation through data initiatives, and are ready to dig into the complexities of your business and get results.
Strategic Partnerships with Leading eCommerce Businesses
We are well-known for helping top eCommerce companies reach the top of the industry in long-term partnerships. Our 7+ year-long cooperation with BlackHawk Network is a prime example of this, as we continue to offer our 78+ team of dedicated experts to build scalable, secure, and data-driven solutions for this client.
Data and AI/ML Engineering Expertise Blend
We pride ourselves as an innovative AI/ML development company, driving the evolution of artificial intelligence for the retail industry, as well as eCommerce. Our team holds the necessary practical experience and knowledge in combining data with AI/ML engineering to turn complex commerce data into understandable, actionable insights.
Data Analytics Services Focused on ROI
All services we provide, from advanced analytics in eCommerce to cutting-edge data visualization, are focused on maximizing the return on investment for our clients. We ensure that all our data initiatives directly contribute to the business value of an organization, and treat each project as a unique scenario with its specific challenges.
Customized Data Solutions
We tailor each eCommerce and data analytics project to the needs and vision of our clients. Whether the goals are to improve customer experience, streamline operations, or optimize inventory management, we know how to do it based on data and are ready to offer our 600+ team and a powerful recruiting department to build the right solution for you.
SPD Technology’s Success Stories
Let our data-driven eCommerce analytics projects speak for themselves, as we briefly overview two of the most impressive case studies in this category.
High-Load Support AI Chatbot for an Online Fashion Store
Business Challenge
Our client is a French B2C eCommerce company operating in the Fashion industry, with a challenge to automate customer support with an AI-powered chatbot for resolving customer queries in the online store, one of the truly groundbreaking applications of generative AI in eCommerce. Our team was tasked to develop this chatbot, along with detailed integration guidelines.
SPD Technology Approach
To build this chatbot, we leveraged the LangChain agent powered by Mistral LLM, designed to handle user queries by retrieving data from AWS S3-stored content via a PostgreSQL-based vector database. This architecture choice allowed us to receive quick and precise responses by turning data into vectors. We implemented guardrails to ensure response reliability and GDPR-compliant data anonymization.
Mistral LLM model and AWS embeddings model were refined through prompt engineering and resulted in efficient query handling. When the models were ready, we constructed the LangChain agent stack and fine-tuned the system with black-box testing, using the RAG Triad method to ensure language consistency and response quality.
Packaged as an API with detailed documentation, the chatbot was deployed on EC2 instances with Kubernetes, enabling autoscaling to manage high traffic seamlessly.
Value Delivered
- Efficient Automation of Customer Queries: we developed an outstanding solution that can process 99% of queries in under 10 seconds, providing lightning-fast responses and significantly improving customer experience, as well as customer retention.
- 100% Reliability of the Solution: we managed to make the system constantly available during peak hours, able to process 30 requests per second.
Ultimately, we delivered mission-critical functionality for the client, providing another impressive example of how Artificial Intelligence transforms customer service and elevates the burden of human employees, letting experts focus only on sophisticated customer queries.
Leveraging AI/ML for Expanded Product Range and Enhanced eCommerce Product Listings
Business Challenge
Our client is an online retail business based in the United States, operating via a drop-shipping model with a powerful B2C web solution providing access to a diverse selection of over 1,000,000 products with a wholesale catalog totaling over 300,000 items. Our company was initially contacted to help with performance optimization, SEO enhancements, and UI/UX improvements, but after we completed those goals, the scope expanded.
SPD Technology Approach
We achieved massive website responsiveness improvements by implementing SQL query optimization, streamlining navigation, and improving UI/UX. The SEO activities of our team included keyword research, content optimization, and strategic backlinking to improve search rankings and drive organic traffic, while cleanup and indexing adjustments boosted site crawlability.
During our collaboration, the client set a goal of expanding a product listing, so we developed a scalable parsing solution to achieve this. Leveraging our proficiency in generative AI development we found a way to use ChatGPT to classify over a million products with 90% accuracy and used OCR with image captioning to clean irrelevant content from product images. There was also an autosuggestions feature, where we implemented ChatGPT with Algolia for improved relevance. Outstanding scalability was achieved by using AWS Batch and Elastic Beanstalk, running processes in parallel across multiple instances for robust performance and resource efficiency.
Value Delivered
- Improved User Engagement and Operations Efficiency: we leveraged the latest AI/ML technologies to accurately categorize products, clean up irrelevant data, and improve search functionality for the client.
- Significant Product Expansion: our team was able to expand the product range for the client by adding over 1,000,000 products from major global retailers.
- Massive Performance Boost: we achieved a tangible increase in loading speed with a Google PageSpeed score improvement from 29 to 97 (web) and from 12 to 90 (mobile), improving user experience and getting better positions in search engine rankings.
Overall, we delivered the planned scope of work and showed an innovative approach to AI/ML for E-commerce data analysis in this project, finding new ways to effectively implement critical functionalities for the client.
Conclusion
With the global e-Commerce analytics market size expected to reach USD 64.58 billion by 2032, according to Business Research Insights, data analytics for eCommerce is more relevant than ever before, as the industry is currently booming. Businesses of any size across the globe are benefiting from data-driven insights for smarter and faster marketing decisions, more efficient inventory management, superior customer experience, and precise pricing strategies.
If you are ready to gain a competitive edge and tap into new growth opportunities for your organization, impossible without data, we are here to help. SPD Technology is at the forefront of building innovations and bringing the transformative power of data to our customers, so get in touch with our team, and we will deliver a custom solution for you!
FAQ
- How is Data Analytics Used in eCommerce?
Data analytics is a core component for eCommerce businesses and plays a major role in enhancing customer experience and improving operations, resulting in significant revenue increases. The key areas of ecommerce data analytics include:
- Customer Segmentation and Personalization
- Customer Journey Analysis
- Inventory and Supply Chain Management
- Pricing Optimization
- Marketing and Advertising Optimization
- Customer Lifetime Value (CLV) Prediction
- Fraud Detection and Prevention
- Customer Support and Feedback Analysis.
- What is the Most Popular eCommerce Analytics Tool?
Currently, Google Analytics is dominating the market with an astonishing 89.85% market share, if 6sense is to be believed. This leading position became possible due to the platform having a comprehensive set of features that effectively track key metrics, use customer data for behavior analysis, and determine the success rate of marketing efforts. Other notable eCommerce analytics tools include Shopify Analytics, Adobe Analytics, Kissmetrics, Mixpanel, and Hotjar.
- What are the Benefits of Analytics in eCommerce?
There are several major benefits of eCommerce analytics including, but not limited to:
- Improved Customer Experience
- Higher Conversion Rates
- Better Cost Efficiency
- Enhanced Decision-Making
- Competitive Advantage.
- What is the Role of Analytics Tools in eCommerce?
Modern tools play a vital role in eCommerce data analysis, as they automate data collection, interpretation, and visualization. Additionally, organizations benefit from features like KPIs tracking, automated reporting, and predictive analytics, which are common among current solutions.
- What is eCommerce Analytics?
This is the process that helps businesses obtain a comprehensive view of their performance by gaining data-based insights on customer behavior, sales activities, and operational efficiencies.