Big data analytics is growing fast and brings a lot of innovation, especially in the e-commerce industry. According to Emarketer, retail e-commerce sales will reach a staggering $4.9 trillion in 2021, following a 25.7% surge in 2020. That means that both sales and marketing teams use a lot of data each day.
Big data in e-commerce will help retailers process the available data sets, forecast product demands, and create the best pricing strategies. However, statistics show that 51% of retailers cannot access data that impedes measuring marketing ROI. Read our article to learn more and even predict trends in your industry.
Big Data in E-commerce: How it Works
Big data analytics is all about analyzing large data sets to find hidden patterns, correlations. Thus, it helps retailers and e-commerce business owners make the most available data and make informed decisions. E-commerce businesses must become customer-centric. And that’s where bid gata comes into the game.
The more company analyzes its customers and their behavior, the more traffic and sales it gets and the more it can predict trends in e-commerce with big data.
Analyzing delivery information, inventory, payment, and sales data is necessary for an e-commerce business’s effective operation. If the company decides to integrate big data into its business processes, it can generate more revenue and acquire crucial information about its customers.
Big Data analytics is all about revealing hidden patterns, market trends, and customer preferences. With the help of big data analytics, business owners are empowered to derive values from information and make optimal business decisions. With the help of big data, e-commerce retailers can:
The examples of business processes include:
- Track the patterns of customer behavior, and build a sales and marketing strategy based on this
- Recognize and address different issues so that consumers can enjoy making online purchases
- Control inventory, supply chain, forecasting criteria, competent pricing strategies, and sales strategies
- Use micro-moments to predict consumer preferences and patterns of action
- Detect fraud-related behaviors and any risks to their systems, use more secure online payment
E-commerce Data Analytics Tools
Let’s review the most popular tools for integrating big data into your business.
Fanplayr is a behavior personalization tool designed to create, execute, and optimize your business strategy. The platform uses big data to provide customers with the right offers at the right time. Fanplayr relies on a robust segmentation engine. It also provides a package for in-depth analytics and has a team of experts.
Kissmetrics is an advanced product for e-commerce data analysis and marketing analytics that helps you define your big spenders and concentrate all your efforts into attracting them to your business. It allows to:
- Integrate it into Shopify
- Generate increased sales
- Optimize and improve your checkout funnel and achieve dynamic customer service
- Track and increase new and repeat purchases
- Track lifetime value by each product category
- Track reports on subscription revenue
Spring metrics offers customers two tools: Conversion Analytics and Smart Conversions. With the help of these tools for the big data customer experience, you can review site traffic and segment data and engage visitors with real-time offers based on big data.
With the help of Woopra, you can track everything that your users can do. You can track your users’ paths to uncover critical obstacles and opportunities at every point in the customer experience—from campaign conversions to product engagement. Also, you can analyze how long users continue engaging with your brand and what motivates them to stay with you.
2021 E-commerce Trends
Apart from big data, e-commerce has the following trends, and it proves how big data is changing the world.
It is interesting to see how this trend evolves currently. In 2019, Gartner predicted that 100 million consumers would shop using AR by 2020.
“Polls have shown some really powerful numbers in regards to AR too: 35% of people say that they would be shopping online more if they could virtually try on a product before buying it, and 22% would be less likely to visit a brick-and-mortar store if AR was available via their favorite e-commerce store. AR grants a person the ability to not just see a 3D model of a product but lets a user see how it looks if they were actually wearing it. Some products and industries lend themself better to traditional shopping methods, but AR is going to shake things up sooner than later.”
— Michael Prusich, Director of Business Development at 1Digital Agency.
You should optimize your e-commerce store for voice search as more homes start to use smart speakers, and consumers will utilize voice search to shop online, order food, and organize their lives. The rise of voice search creates an opportunity for e-commerce businesses in terms of keywords and content.
On-site increased personalization
Personalization is the future of sales and marketing. When customers feel that all the offers are in-line with what they want, they trust a brand more. AI-powered personalization becoming increasingly relevant:
“As brands harness and leverage more data, they’ll be able to create incredibly relevant experiences for shoppers that feel tailor-made.”
— Kaleigh Moore, freelance writer and e-commerce specialist.
Variety of payment options
Customers have different needs for paying, so they may even cancel a potential sale if they can't pay by their preferred option. If you offer many ways to pay, you can get all the chances to increase conversion rates on mobile devices. In addition, customers can save their payment information on your site and checkout even faster while you get optimized pricing and increased sales.
Data Science and E-commerce
Data science will transform e-commerce, and that’s the fact. Here is how Data Science helps to boost sales in e-commerce.
Improve customer feedback analysis
You should build your business around customer feedback. If it is negative, then it will affect the sales processes in your company. Here, Data Science enables e-commerce companies to discover their weaknesses by gathering relevant feedback for every service and collectively analyzing it to improve customer experience.
Helps improve advertising
To improve sales in e-commerce, you have to advertise the product or service to the right customers. Data Science helps to enhance advertising analytics and advertise your services to the right people. Moreover, the advertising platforms operating on Artificial Intelligence and Machine Learning help target your ads better and personalize your offerings.
Enhance inventory management
Data science provides secured big e-commerce firms and startups to maintain their inventory more efficiently and decrease the waste of capital on not-so-popular products which are not selling much, so there is no need to restock.
Here are the most popular dataset for e-commerce and retail available for free e-commerce dataset download
- Fashion-mnist: Ideal for product categorization. MNIST contains nearly 60,000 training images and 10,000 test images of fashion industry products in 10 classes.
- Innerwear Data from Victoria’s Secret and Others: Data from 600,000+ underwear items retrieved from popular retail sites. Includes product description, price, category, rating and more.
- Electronic Products and Pricing Data: Contains a list of over 7,000 electronic products.
- Men’s Shoe Prices: List containing 10,000 men’s shoes and prices.
- Women’s Shoe Prices: List containing 10,000 women’s shoes and prices.
- E-commerce Item Data: Suitable for recommendation systems. This dataset contains part numbers and related product descriptions from the outdoor clothing brand product catalog.
- Fashion Products on Amazon.com: This is a pre-crawled dataset created by retrieving data from Amazon. It consists of approximately 22,000 fashion items on Amazon.
- E-commerce Tagging for Clothing: Contains images from e-commerce sites with bounding boxes drawn around shirts, jackets, sunglasses, etc. It contains 907 items, of which 504 items were tagged manually.
- Online Retail Dataset (UCI Machine Learning Repository): Contains all transactions for eight months (01/12 / 2010-09 / 12/2011) for a UK online retail company.
- Brazilian E-Commerce Public Dataset: contains more than 100,000 anonymized orders from Brazil placed on List (100 thousand orders) from 2016 to 2018 is made at several trading floors. In addition, it includes many measurements from order status, price, payment and transportation efficiency to real written customer reviews.
- Online Auctions Dataset: Retail dataset containing eBay auction details for Cartier watches, Xbox game consoles, Palm Pilot M515 PDAs and Swarovski beads.
- Retailrocket Recommender System Dataset: This data has been collected from a real e-commerce website for 4.5 months. In addition, it contains information about visitor behavior, including events such as clicks, shopping cart additions, and transactions.
- E-commerce Search Relevance: Contains the URLs of the images, the rating on the page, a description of each product, the search query that led to each result, and much more of the five main English-language e-commerce sites.
- Best Buy Search Queries NER Dataset: Contains manually tagged search queries on bestbuy.com in search queries. There are phrases tagged with important entities such as brand, model name, category name, etc.
Partnering with a data services company is a great way to fill gaps in your organization’s capabilities and improve sales. There are a lot of sophisticated analysis tools and experienced professionals that can solve any data-related business problem you can throw at them. They know when to use big data and when another tool would be better for the job.
At Proxet, we take great pride in helping our partners identify what data they need and what they can do to use it better. If your organization has a data challenge it needs help solving, please get in touch, and we will help you get the most out of the e-commerce industry.
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