Ecommerce Data Analysis with Pandas: A New Opportunity for Retail Industry

July 2, 2021
Ecommerce Data Analysis with Pandas: A New Opportunity for Retail Industry

Data analysis is the core of effective ecommerce. It doesn't matter what industry you work in; data can uncover deep insights about your audience's behaviour, characteristics, and needs. As a result, you'll get a chance to segment your audience based on these insights and deliver more personalized services. Big data will help you in price optimization, supply chain management, and even secure online payments.

How can you achieve this technically? Consider Pandas - a famous Python library that provides flexible data structures to manipulate and clean labeled and relational data. In this blog post, you will learn more about opportunities to use big data and how the Pandas library can help you process  your data.

Image by Proxet. Different Data Structures
Different Data Structures

New Retail Opportunities: How Data Changes the Way We Sell Products

According to Gartner, 40% of data analytics projects will relate to customer experience. This means that ecommerce companies should focus their strategy on improving customer experience. Using online retail analytics and customer data, companies can offer more personalized services. What does that mean? Data is the future of retail. If retailers can gather and analyze all their consumers’ activity, they can predict customer needs and provide a better physical and digital experience.

“If ecommerce companies can continue to access the biggest data lakes, their market positions will be maintained, and the value of their equity will skyrocket. And that’s why the biggest opportunity in retail now is in technology, specifically artificial intelligence and machine learning technology. For investors, the biggest opportunities in retail are in retail-related artificial intelligence and machine learning. For retailers, the biggest opportunity to sell more is in giving consumers a better experience based on real learning from trillions of data points.”

Richard Kestenbaum, Co-founder of and Partner at Triangle Capital LLC.

How can retailers improve their sales with the help of data analysis? Here are just a few ways:

  • Build impersonalized and personalized recommendation engines
  • Use machine learning and deep learning algorithms for market basket analysis
  • Segment customers according to their shopping preferences
  • Use competitor data to determine the product price at different times
  • Manage inventory more efficiently
  • Use analytics techniques to predict the demand for a product and forecast sales

Let’s go straight to how Pandas, a Python library for data analysis, can help process ecommerce data.

Pandas and Data Analysis: How to Manipulate Your Data

Pandas is a famous Python library specially designed for data analysis. Its back-end source code is written in C or Python, which results in highly optimized performance. The Pandas open-source library provides flexible data structures to manipulate and clean labeled and relational data. Pandas ranks 4th among the most popular libraries. Many machine learning data analytics tools are based on Pandas. Ecommerce data analysis with Pandas allows you to:

  • Track and calculate statistics such as average, distribution, and median of columns
  • Create diagrams and graphics with the help of different data visualization tools
  • Clean data and remove unnecessary fields
  • Manipulate your data with the help of merging, joining, reshaping, etc.

The main benefits of the Pandas library include:

  • Simple data representation
  • Fewer lines of code in an online shopping dataset
  • Ability to work with large data sets
  • Customization and flexibility

Python pandas provides two types of data structures: Pandas DataFrame and Pandas series. Pandas DataFrame organizes data into rows and columns and supports input types such as dict of 1D ndarrays, lists, dicts, or Series, 2-D numpy.ndarray, structured or record ndarray a series, another DataFrame. Pandas DataFrame allows you to load data from different databases and data formats, segment records within a data frame, define the necessary Python ecommerce functions with certain computational tasks and apply them to your data frame records.

Ecommerce Big Data to Personalize Customer Experience

Analyzing big e-commerce data leads to better strategic decisions, better understanding customer needs, and better control of operational processes. Here’s how companies can use ecommerce big data for a smooth customer experience:

  • Determine customer preferences and build customer profiles
  • Track response times and improve customer service experience
  • Send emails with personalized discounts and give personalized shopping recommendations
  • Stock the right inventory and define the shortest routes for product delivery

Here are the most common use cases of AI and big data in the ecommerce industry:

Pricing Optimization

With the help of big data’s predictive analytics, digital retailers can better decide on pricing strategy and price optimization. Big data analysis engines analyze data from different consumer touchpoints and offer customers the best deals and dynamic pricing while keeping the highest possible margin

Supply Chain Management

Predictive analysis can help suppliers prevent failures, minimize downtime and delays, define the causes of different problems and prevent them in the future with an ecommerce dataset download. Companies that use supply chain management can maintain strict quality control and analyze performance data more efficiently, often in real-time or near real-time. Supply chain management helps companies keep up with demand and never run out of supplies.

Secure Online Payments

Big data helps ecommerce businesses analyze streams of real-time big data and detect suspicious transactions. Big data solutions process customer information and past purchases, trace repeated patterns to define suspicious transactions and suggest the most effective strategies for dealing with cybercrime and optimizing cash flow management.

Retail Data Analyst: How it Affects Ecommerce

Retail analytics tools provide data about customer buying behavior to make smarter decisions in sales and business processes.

“Comprehensive ecommerce data analysis is an essential tool to avoid these common forecasting pitfalls and faulty applications. Using consumer demand signals across social media, search patterns, and market performance, a company can gather plenty of external data and easily make sense of whether a trend is growing, declining, or stabilizing”

Kathy Leake, CEO of Trendalytics and a 3x founder

The most common and relevant upcoming trends for the ecommerce data analyst are:

  • Automation of daily merchandising tasks will help companies decrease expenses and improve business operations
  • Democratization of analytics will help unify analytics automation platforms
  • Improvements in user experience will lead to more convenient shopping and retail mobile apps
“By using analytics, companies will increase sales per visit, optimize prices, increase sales and profit, and increase sale value and margins. Also, analytics helps to increase brand loyalty and create a better shopping experience. Our software development services aim at providing better user experience and helping ecommerce companies use analytics to improve customer-facing experiences, employee productivity, operational improvement, and product innovation.”

Vlad Medvedovsky, Founder and CEO at Proxet (ex - Rails Reactor), a software development solutions company

New Points for Growth in Ecommerce

If you want to improve your ecommerce business in 2021, stay tuned for the latest growth trends and approaches in digital transformation.

Augmented Reality in Online Shopping

According to IBM’s 2020 U.S. Retail Index Report, the pandemic has accelerated the shift to digital shopping by roughly five years. Everything started with a “try-before-you-buy” experience when previewing furniture and products in your home with everyday brands like IKEA and Home Depot, to trying on luxury fashion, such as Louis Vuitton and Gucci, virtually. Retailers also use AI to reimagine the digital shopping experience with virtual storefronts.

Voice Search

Nowadays, many homes adopt smart speakers, so consumers will utilize voice search to shop online, order food, and organize their daily routine. Ecommerce businesses have an opportunity to optimize their keywords and content to be visible in voice search results. For example, Nestlé has created a cookbook feature in compliance with voice search. The online cookbook provides instructions and assistance to the questions and queries asked while cooking.

Individual Experiences with the Help of On-site Personalization

It doesn’t matter whether it’s a B2C or B2B industry; all buyers are looking for personalized, custom shopping experiences online. AI can collect the data to provide personalized product recommendations and detailed customer service. A recent study shows that scaling advanced personalization capabilities had a 25% revenue lift for retailers. The best performing retailers also used more personalization strategies, such as personalized merchandising and loyalty and reward programs. They also deploy more complex personalization capabilities by building infrastructure that unlocks value. It enables companies to introduce predictive algorithms and access integrated customer data to respond to customer behavior quickly.

All the features we have mentioned above are possible to implement with Pandas - a Python library that will help you better process and manage the data in your ecommerce business. If you need a consultation on how this can be implemented in your business, don’t hesitate to contact Proxet, a software development company specializing in solutions for startups, SMBs, and enterprises. We work closely with our clients on strategy, design, and development and guide them through the process from the beginning to the very end to ensure that we always stay on the same page.

Related Posts