Big Data is a trendy toy for everybody who is anybody in the highly competitive realm of eCommerce… a sophisticated toy that brings a mind-blowing ROI, when played with correctly.
Data Analytics is the process of studying Big Data. In this article let's get down to the bottom of all things related to Data Analytics development for eCommerce.
Why do you need it?
Because a jaw-dropping 35% of Amazon's sales flow from its recommendation engine algorithm that’s based on big data, and you don’t want to miss out on the power of big data analytics to drive sales.
What Is Data Analytics?
There are varying definitions of the term Data Analytics, so we provide our interpretation for you:Data analytics is a body of scientific approaches related to qualitative and quantitative analysis that deals with gathering, cleaning, processing, and analyzing massive volumes of data to convert them into actionable insights for businesses.In simple English, the know-how amassed by the data analytics domain allows companies to collect Big Data, format it & interpret it to improve the analytical process and produce valuable business intelligence.
Data Analysis vs. Data Analytics vs. Data Science
In order to avoid confusion and bring further clarity into the subject, let’s go over some terms that are closely related to the subject of this article and are often used interchangeably (which they should not be).Data Analysis vs. Data Analytics
Data Analysis is a process of collecting, cleaning & processing data points. So when we talk about analytics, it includes analysis as one of its pillars, but adds a forward, empirical dimension to past history, which is future prediction and prescription, derived through the process of analysis. Analytics is a more general term that includes future applications of the know-how, while analysis is the process itself, focused on past facts only.Data Analytics vs. Data Science
Data science also utilizes big data for its purposes, but it is even more forward-looking and overarching than Data Analytics. Data Analytics allows us to answer questions about the past and make predictions about the future, while Data Science helps us to understand which questions to ask. Whereas data analytics processes data & dwells on past trends, Data Science creates new frameworks for the future. It bases its work on predictive analytics, prototypes, custom analysis, and creating new models armed with the knowledge of past trends, indexes, peaks, and lows. Role & Functions Of Data Analytics In eCommerce Data analytics is the opening action item in any process optimization nowadays. You look at relevant past trends and make adjustments to improve underperforming methods and/or further amplify what's performing well. eCommerce data analytics helps giants become even bigger. It also helps propel smaller players who know the value of big data into the top echelons of eCommerce. How exactly do modern eCommerce development solutions help advance the soon-to-reach $4.5 trillion industry?Pricing & Revenue Management
Price optimization is a process many customers are blissfully unaware of. Pricing with the help of big data, analytics & AI is becoming more advanced and more affordable at the same time. The product used to be leveraged exclusively by bigger players, like Amazon & Walmart, but now even smaller eCommerce players can take advantage of smart pricing technology. Pricing optimization software is gaining traction because it allows companies to gain loyalty with their customers, who can rely on products to be competitively priced every time.Product matching
This algorithm allows customers to find products online with exactly the same parameters across dozens of descriptors, like size, color, dimensions, weight, style, materials used, customer reviews, etc. If you think this is an easy task, there are 2000 shades of the color “beige” on USA websites.Price Matching & Price Optimization
Once you have identified an item as the same product you are comparing it to, it’s time to compare the prices & adjust them according to the company's strategy.Dynamic Pricing
Amazon adjusts its pricing every 2 minutes. That’s a fun fact, right? It’s literally introducing automatic by-the-minute changes to its inventory.Smart Repricer solutions
If you are selling on Amazon, eBay, and other big platforms, customers are comparing much more than the price alone. They compare the delivery terms, fulfillment methods, product visibility, etc. This provides an opportunity for repricer solutions to help beat the competition.Minimum Advertised Price (MAP) solutions
MAP is a pricing software solution for monitoring your resellers, so that they adhere to contracted minimal levels of pricing.Recommendation Engines
Recommendation engines are super powerful (remember, 35% of Amazon sales come from them, right?). They are used in all sorts of varieties with companies competing and doing A/B testing for the most accurate ways to name and present these sections: “People who viewed similar products also purchased X” “You may also like Y” etc.Market Basket Analysis
Market Basket Analysis is one of the oldest tricks in the book used by data analysts in eCommerce. It allows predicting the most likely combination of products, based on past purchases. Milk and bread often go together. Got yourself a burger? How about a Coke?Warranty Insights
Warranty analytics works through returns, repairs, and customer claims to detect anomalies in products and predict ongoing quality issues.Inventory Management
This is another role for big data analytics tools in which a massive data processor helps manage an inventory, ensuring enough stock for popular items, timely deliveries, SKU controls, etc.Merchandising
Assortment planning
Use of analytics to manage inventory helps merchants avoid out of stock or oversupply situations. Monitor the market and make sure your store has everything that's trendy and topical.Competitive benchmarking
This merchandising solution has a mix of assortment management and price optimization features. Competitive benchmarking software allows businesses to monitor the market and react to trends & changes accordingly.Sentiment Analysis
Social listening is a tactic used by pretty much all major brands. Big Data allows sellers to pinpoint negative and positive sentiments across diverse channels and convert them into actionable insight for marketing, customer care, and even R&D.Role Of Data Analyst
A data analyst is a person who performs data analysis. There are 3 main functions of a data analyst job:- Clean data (a big part of the job reaching 70-80%)
- Performing mathematical calculations
- Processing, analyzing & optimizing