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Increase Your Profit Based on eCommerce Big Data Analysis
Big data analysis is used when deciding product development, marketing strategies, inventory management, pricing, and many other business areas.
Using big data analysis will have a huge positive impact on the growth and profitability of your eCommerce business.
Collecting and analyzing data about customer behavior, preferences, and purchasing patterns will give you valuable insights into what is working and what isn’t. You’ll make data-based decisions and more accurate trend predictions.
In addition to your effort, utilize all information you can find online. There are numerous case studies, and failure or success stories from other eCommerce businesses valuable for gaining inspiration and ideas for your strategies.
But!… Remember that other companies’ big data and conclusions may not necessarily be relevant to you. Each business has unique products, customers, and sales patterns that require a tailored approach to data analysis. Do not make decisions based on others’ big data analysis just because you like their findings.
What is big data in eCommerce
eCommerce big data is an enormous volume of information about online shopping activities, customer behavior, website success, social media interactions, purchasing patterns, and more.
By utilizing advanced software and analytical techniques, businesses can extract valuable insights from this data to improve omnichannel customer experience, optimize pricing models, and enhance supply chain management, ultimately leading to increased profitability.
Simply said, the more you know about the past and the present, competitors, threats and opportunities, new methods and techniques relevant to your niche, the higher are chances you’ll make good decisions.
Big data eCommerce – How Much is “Big”
How much is “big” in the term Big data? Unfortunately, there is no straight answer.
The specific amount of information you need to analyze can vary based on your business’s unique needs and goals. It’s important to collect relevant data points aligned with your business objectives and avoid getting bogged down by irrelevant or extraneous data.
To determine the right amount of data to analyze, start with a clear understanding of what questions you are trying to answer.
Here are steps you should follow before investing time and money in gathering data:
- Identify your key performance indicators (KPIs)
- Outline your objectives
- Develop a targeted approach to data collection and analysis.
There are 10 eCommerce KPIs (Key Performance Indicators) to consider tracking:
- Conversion Rate – the percentage of visitors to your site who make a purchase
- Average Order Value (AOV) – the average value of each transaction on your site
- Cart Abandonment Rate – the percentage of shoppers who add items to their cart but don’t complete the purchase
- Customer Lifetime Value (CLTV) – the total amount of revenue a customer will generate for your business over their lifetime
- Return on Ad Spend (ROAS) – the ratio of revenue generated to advertising spend
- Traffic Sources and Referral Traffic – the sources that drive traffic to your site and the quality of that traffic
- Email Open Rate and Click-Through Rate – the percentage of subscribers who open an email or click on a link inside
- Bounce Rate – the percentage of visitors who leave your site after viewing only one page
- Inventory Turnover – the rate at which you sell and replace your inventory
- Customer Acquisition Cost (CAC) – the cost of acquiring a new customer through marketing and advertising efforts
- Retention Rate – the percentage of customers who make repeat purchases from your business
- Customer Satisfaction Score (CSAT) – a metric that measures how satisfied customers are with their experience on your site
- Social Media Engagement – the level of interaction and engagement your brand receives on social media platforms
Where to Look?
There are several ways you can gather big data for your e-commerce business:
- Website Analytics: Use tools like Google Analytics and Google Search Console (both free tools) to track website traffic, conversions, most visited pages, engagement times (how much time users spend on your website), and bounce rate.
- Social Media Analytics: Social media platforms such as Facebook, Twitter, and Instagram provide user engagement and demographics analytics.
- Customer Feedback: You can collect customer feedback through surveys, product reviews, and social media comments to gather insights into customer preferences and opinions.
- Sales Data: Collect sales data from your e-commerce accounting records.
- Third-Party Data: You can purchase third-party data from market research firms or use publicly available data to supplement your data collection efforts.
Once you have collected big data, use data analytics tools and statistical software to process, organize, and understand them in order to gain insights and make informed decisions that will drive growth and revenue for your business.
How Much Money You Need to Gather and Analyze Big Data
The cost of gathering big data for your e-commerce business can vary depending on the volume, the complexity analysis required, and the data sources involved.
If you are outsourcing the data collection and analysis to a third-party provider, the cost will depend on the scope and duration of the project and the fees charged by the service provider. Some providers charge hourly rates, while others charge flat fees or project-based pricing.
Typically, the cost of outsourcing big data analytics can range from a few thousand dollars to tens of thousands of dollars, depending on the size and complexity of the project. Do your research and choose a reputable service provider with experience in e-commerce data analytics to ensure you get the best value for your investment.
Alternatively, you can use free or low-cost tools, such as Google Analytics or social media analytics tools, to gather and analyze data yourself. However, learning how to use these tools effectively may require time and effort.
Tools to Analyze Big Data
There are many statistical tools and software available for data analysis. The choice of tools and software depends on your specific needs, eCommerce statistics and trends you need to generate, and the data type you have. Here are some commonly used tools and software:
- Excel: Excel is a basic yet powerful tool that can be used for simple data analysis, such as calculating averages, standard deviations, and correlation coefficients.
- Minitab: Minitab is a paid statistical software package used for data analysis, quality improvement, and statistical process control. It provides tools for basic and advanced statistics, graphical analysis, regression analysis, and design of experiments.
- Tableau: Tableau is a data visualization tool that allows users to create interactive dashboards and visualizations from various data sources.
- SPSS: SPSS (Statistical Package for the Social Sciences) is a software package used for statistical analysis, data management, and data visualization.
- SAS: SAS (Statistical Analysis System) is a software suite used for data management, analytics, and business intelligence.
How Can Online Businesses Use Data Analytics to Increase Sales
Here are 8 examples of how online businesses can use data analytics to increase sales:
- Personalized marketing: Use customer data and behavior analytics to create customized marketing messages based on individual preferences and interests.
- Customer experience outsourcing: Generate all the data related to your customer service: expenses, time spent, number of employees, repetitiveness, facilities costs, etc. Make a smart, data-based decision about outsourcing your customer service or keeping it in-house.
- Pricing optimization: Analyze pricing data, competitor pricing, and demand trends to optimize product pricing for maximum profitability.
- Inventory management: Track inventory levels and analyze sales data to ensure the minimum inventory is available at the right time, reducing stockouts and overstocks.
- Fraud detection: Monitor transactions and customer behavior to identify potential fraudulent activity, such as stolen credit cards or fake transactions.
- Customer retention: Analyze customer data to identify patterns in shopping behavior, preferences, and feedback, to improve the customer experience and increase customer loyalty.
- Product recommendations: Use purchase history and browsing data, create powerful post-purchase strategy, and suggest additional products that a customer might be interested in purchasing, leading to increased sales.
- Supply chain optimization: Use data analysis to optimize entire supply chain operations. Increase efficiency of all inbound and outbound business logistics tasks, such as shipping routes, order fulfillment processes, and delivery times, for more efficient and cost-effective operations.
Some of the listed metrics will increase sales and some will help you decrease expenses. By leveraging e-commerce big data, you will gain valuable insights into your customers, operations, and market trends, leading to informed decisions and profit boost.
Leverage eCommerce – How to Use Big Data to Outrank Competition
If you decide to gather and utilize big data, here are steps to follow, no matter which platform you use on your website (WooCommerce, Shopify, Magneto, Vix, etc.):
- Gather as much information as possible using eCommerce analytics provided by the platform you use, Google Analytics, and Google Search Console (all are free).
- Put your data into Excel
- Use the filtering option to extract relevant information focusing on KPIs you choose
- Find similar data relevant to your niche- perform competitive analysis
- Apply common sense to recognize patterns and draw conclusions
- Create strategy and make decisions.
More concretely, using big data, you can analyze customer behavior in many ways and gain insights into their preferences, interests, and habits.
Here are several examples of how you could use big data to analyze customer behavior and improve customers’ experience ecosystem:
- Identify Customer Segments: Use big data analytics to segment your customers based on demographics, purchase history, browsing behavior, and other significant data points. Thus you will identify groups of customers with similar characteristics and interests, allowing you to tailor your marketing messages and product offerings accordingly.
- Predictive Analytics: Useful statistical method to anticipate customer behavior and preferences based on patterns and trends in historical data. Based on results, make informed decisions about product development, inventory management, and marketing strategies.
- Product Recommendations: Analyze customer purchase history and browsing data and recommend additional products they might be interested in. Offering relevant and personalized product suggestions is a proven method to increase sales. Additionally, selling to existing customers is up to 50% less expensive than acquiring new ones.
- Abandoned Cart Analysis: Analyze abandoned cart data to understand why customers leave items in their carts without completing the purchase. Identify pain points during checkout or concerns over pricing, shipping, or return policies. Sometimes, having unnecessary fields in your checkout process (such as two address fields or the second phone number) will increase the abandonment rate up to 60%.
- Customer Satisfaction: Analyze customer feedback and reviews to identify areas where you can improve customer satisfaction and experience. Based on results, address common complaints or concerns and turn them into opportunities, improve product quality, and/or enhance customer service.
Real Word Examples
Here are real-world examples of using big data and the results achieved based on decisions extracted from the gathering and analyzing endeavor:
- Netflix used big data to analyze viewer behavior and provide personalized recommendations. They increased user engagement and retention rates. According to an online survey, 61% of Netflix users said they would cancel their cable subscription if they had to choose between it and Netflix. Additionally, Netflix’s revenue has increased from $1.36 billion in 2010 to $25 billion in 2020, with a user base of over 200 million.
- Walmart: By optimizing its supply chain using big data, Walmart has reduced delivery time by 15% and saved millions of dollars in transportation costs. In 2020, Walmart’s revenue was $524 billion, making it the world’s largest company by revenue.
- Uber used big data to provide personalized experiences to users. They increased revenue by 200%. In 2019, Uber generated $14.1 billion in revenue, with a user base of over 100 million.
- Wall Street: Without big data to predict market trends, hedge funds wouldn’t be able to achieve high returns. Renaissance Technologies’ Medallion Fund has achieved a 71.8% annualized return over 30 years. In 2020, the global hedge fund industry managed over $3.6 trillion in assets.
- Healthcare: After gathering and analyzing big data, IBM Watson identified a treatment for a rare form of leukemia that doctors had missed. The healthcare analytics market is projected to reach $84.2 billion by 2027.
- Airbnb: Based on big data to personalize the customer experience, Airbnb has achieved a 30% increase in bookings. As of 2020, they had over 7 million listings in over 220 countries, with a valuation of $35 billion.
- Amazon constantly uses big data to optimize supply chain operations. They optimized inventory and reduced order delivery times. In 2020, Amazon’s revenue was $386 billion, making it the largest retailer in the world.
- City Planning: Using big data to optimize transportation systems, Chicago reduced traffic fatalities by 28%. According to the World Economic Forum, cities that use big data to optimize transportation systems can reduce commuting times by up to 20%.
5-Sentence Conclusion: Why Big Data is a Must for Long-term Success in eCommerce
Today, collecting and analyzing vast amounts of data is relatively easy and cheap (compared to the pre-internet era).
If you want to outrank competitors, investing time and money into the process is essential.
With big data, you’ll have insights into customer behavior, preferences, and buying patterns to help you make successful decisions and strategies.
Large companies wouldn’t be where they are without the power of big data analysis.
With Excel, Google Analytics, and Internet data, you can also gather eCommerce big data, discover your strengths and weaknesses and address them accordingly.
The probability of making wrong decisions is significantly lower with data-driven conclusions.