The Immense Benefits of Data Science for the E-Commerce Industry

Data science in e-commerce

Data Science provides predictive forecasting using a variety of data sources such as sales historical data, financial changes, customer behavior and searches. It empowers e-commerce companies by promoting related products to potential buyers. Machine Learning (ML) and Artificial Intelligence (AI) make it possible to make predictions based on what a buyer likes before looking for a product or if they need anything in particular.

ML and AI accomplish this by analyzing customer behaviour trends and establishing a relationship between past purchases. Customer sentiment analysis plays an important role in identifying future sales opportunities and target audiences, allowing for direct marketing strategies and sales promotions.

The insights provided by analytics on consumer behavior help in targeting the right customers, as boosting conversion tendencies is the bottom line for e-commerce businesses. Here are some key e-commerce data science projects that enable e-commerce platforms to provide an unmatched user experience (UX) and increase customer conversion and retention.

Read More: How much does it cost to develop an ecommerce app like Amazon

Customer Lifetime Value (CLV)

Any sales team will work to attract new customers, retain existing ones, and reduce customer acquisition costs (CAC). Ecommerce businesses that help support sales and marketing budgets need to determine how much customer value they have after purchase. Customer Lifetime Value (CLV) helps to calculate how much revenue a customer can bring in his/her lifetime.

Customer lifetime value is estimated based on customer purchases and transaction history with an e-commerce website or mobile app. Since it is difficult to predict how much a customer will buy in the future by focusing on past transaction history, data science can help provide more accurate results.

Customer Feedback Analysis

When it comes to communication, customers have high expectations for businesses. They are constantly supportive and demand rapid responses. Chatbots can help you reinvent the definition of customer service and enhance conversions.

Even in this case, data science can help them. Sentiment Analysis is a technique that aids in understanding how customers feel about a company and resolving any issues. Natural language processing, computational linguistics, text analysis, and other techniques can be used by businesses.

Price optimization

Prices are a very important factor in e-commerce. After all, do you buy earphones that you think are too expensive on Amazon? Or if you think Flipkart will give you a better deal on those earphones, you can buy them from there. So e-commerce websites need to make sure that their prices are attractive and that customers can buy their products cheaply, but they are costly enough to make a profit.

Data Science helps e-commerce websites use very tight ropes and price optimization to walk them. Price optimization algorithms take into account various parameters such as customer's purchase models, competitor prices, price flexibility, customer's position, etc.

Prevent low fraud

As digital marketing grows, so does the number of cybercrimes. Stolen account money, identity theft, shipping-billing-related scams, and many other cyber crimes. According to a report, $ 1.48 billion users are losing their wealth to cyber fraud.

Implementing good results and excellent customer experience is not enough for eCommerce companies to be successful. Online fraud is not only a lack of revenue, it can also damage your company’s reputation.

Read More: Cost to develop ecommerce application development

Improve inventory management

Data Science provides secure e-commerce companies and startups with the ability to manage their inventory more efficiently. In addition, it helps reduce capital waste on unpopular products that do not sell well and do not require restocking.

Since e-commerce businesses deal with dozens of customers and many products on a daily basis, the latest data science is crucial for specific inventory management and assessment for anticipated situations.

Warranty Analytics

Warranty Data Analytics also helps retailers and manufacturers to inspect their products and determine the potential lifetime of their products, issues, returns, and any fraudulent activity. Warranty data analysis is based on data and failure distribution estimates are based on data, including age and number of returns, as well as the age and number of units surviving in the field.

Retailers and manufacturers analyze the data and then check how many units have been sold and how many have been returned due to problems. They also focus on identifying irregularities in warranty claims.

Location of new stores

Location analysis is an important part of data analytics. Before a business decides where to open their stores, they can analyze a ton and find the best location to set up shop.

The algorithm used in this case is simple and yet effective. The analyst analyzes the data with an emphasis on population. Analysis of zip codes and demographic information provides a basis for understanding market potential. And competing markets are also taken into account.

Read More: Machine learning in supply chain management

Merchandising

Any retail firm that relies heavily on merchandise. The goal is to devise techniques for boosting product sales and marketing.

Merchandising helps in influencing customer decision making through visual channels. Rotating objects helps keep the assortment always fresh and fresh. Attractive packaging and branding can help attract the attention of customers.

Merchandising algorithms take insights through data and form customer preference sets, taking into account seasonality, relevance, and trends.

Wrapping Up

The e-commerce industry and data science as a whole have a bright future. And business and data science continue to thrive. It helps buyers with their behavior, shopping experience and many other factors. Above all, it enhances the marketing skills of the e-commerce industry and increases profitability.

The real secret behind running a successful e-commerce business is knowing what your customers want and how you can provide for them. Contemporary AI algorithms such as Deep Learning help e-commerce businesses understand the patterns in data to drive customer retention and conversion. Hire a trusted retail e-commerce software development company that offers the best custom ecommerce data science development company in Newyork to succeed in the highly competitive eCommerce market.

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How To Build A Data Science Career In 2021

For this week’s data science career interview, we got in touch with Dr Suman Sanyal, Associate Professor of Computer Science and Engineering at NIIT University. In this interview, Dr Sanyal shares his insights on how universities can contribute to this highly promising sector and what aspirants can do to build a successful data science career.

With industry-linkage, technology and research-driven seamless education, NIIT University has been recognised for addressing the growing demand for data science experts worldwide with its industry-ready courses. The university has recently introduced B.Tech in Data Science course, which aims to deploy data sets models to solve real-world problems. The programme provides industry-academic synergy for the students to establish careers in data science, artificial intelligence and machine learning.

“Students with skills that are aligned to new-age technology will be of huge value. The industry today wants young, ambitious students who have the know-how on how to get things done,” Sanyal said.

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Your Data Architecture: Simple Best Practices for Your Data Strategy

If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

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'Commoditization Is The Biggest Problem In Data Science Education'

The buzz around data science has sent many youngsters and professionals on an upskill/reskilling spree. Prof. Raghunathan Rengasamy, the acting head of Robert Bosch Centre for Data Science and AI, IIT Madras, believes data science knowledge will soon become a necessity.

IIT Madras has been one of India’s prestigious universities offering numerous courses in data science, machine learning, and artificial intelligence in partnership with many edtech startups. For this week’s data science career interview, Analytics India Magazine spoke to Prof. Rengasamy to understand his views on the data science education market.

With more than 15 years of experience, Prof. Rengasamy is currently heading RBCDSAI-IIT Madras and teaching at the department of chemical engineering. He has co-authored a series of review articles on condition monitoring and fault detection and diagnosis. He has also been the recipient of the Young Engineer Award for the year 2000 by the Indian National Academy of Engineering (INAE) for outstanding engineers under the age of 32.

Of late, Rengaswamy has been working on engineering applications of artificial intelligence and computational microfluidics. His research work has also led to the formation of a startup, SysEng LLC, in the US, funded through an NSF STTR grant.

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What Are The Advantages and Disadvantages of Data Science?

Data Science becomes an important part of today industry. It use for transforming business data into assets that help organizations improve revenue, seize business opportunities, improve customer experience, reduce costs, and more. Data science became the trending course to learn in the industries these days.

Its popularity has grown over the years, and companies have started implementing data science techniques to grow their business and increase customer satisfaction. In online Data science course you learn how Data Science deals with vast volumes of data using modern tools and techniques to find unseen patterns, derive meaningful information, and make business decisions.

Advantages of Data Science:- In today’s world, data is being generated at an alarming rate in all time lots of data is generated; from the users of social networking site, or from the calls that one makes, or the data which is being generated from different business. Because of that reason the huge amount of data the value of the field of Data Science has many advantages.

Some Of The Advantages Are Mentioned Below:-

Multiple Job Options :- Because of its high demand it provides large number of career opportunities in its various fields like Data Scientist, Data Analyst, Research Analyst, Business Analyst, Analytics Manager, Big Data Engineer, etc.

Business benefits: - By Data Science Online Course you learn how data science helps organizations knowing how and when their products sell well and that’s why the products are delivered always to the right place and right time. Faster and better decisions are taken by the organization to improve efficiency and earn higher profits.

Highly Paid jobs and career opportunities: - As Data Scientist continues working in that profile and the salaries of different position are grand. According to a Dice Salary Survey, the annual average salary of a Data Scientist $106,000 per year as we consider data.

Hiring Benefits:- If you have skills then don’t worry this comparatively easier to sort data and look for best of candidates for an organization. Big Data and data mining have made processing and selection of CVs, aptitude tests and games easier for the recruitment group.

Also Read: How Data Science Programs Become The Reason Of Your Success

Disadvantages of Data Science: - If there are pros then cons also so here we discuss both pros and cons which make you easy to choose Data Science Course without any doubts. Let’s check some of the disadvantages of Data Science:-

Data Privacy: - As we know Data is used to increase the productivity and the revenue of industry by making game-changing business decisions. But the information or the insights obtained from the data may be misused against any organization.

Cost:- The tools used for data science and analytics can cost tons to a corporation as a number of the tools are complex and need the people to undergo a knowledge Science training to use them. Also, it’s very difficult to pick the right tools consistent with the circumstances because their selection is predicated on the proper knowledge of the tools also as their accuracy in analyzing the info and extracting information.

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50 Data Science Jobs That Opened Just Last Week

Our latest survey report suggests that as the overall Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments, data scientists and AI practitioners should be aware of the skills and tools that the broader community is working on. A good grip in these skills will further help data science enthusiasts to get the best jobs that various industries in their data science functions are offering.

In this article, we list down 50 latest job openings in data science that opened just last week.

(The jobs are sorted according to the years of experience r

1| Data Scientist at IBM

**Location: **Bangalore

Skills Required: Real-time anomaly detection solutions, NLP, text analytics, log analysis, cloud migration, AI planning, etc.

Apply here.

2| Associate Data Scientist at PayPal

**Location: **Chennai

Skills Required: Data mining experience in Python, R, H2O and/or SAS, cross-functional, highly complex data science projects, SQL or SQL-like tools, among others.

Apply here.

3| Data Scientist at Citrix

Location: Bangalore

Skills Required: Data modelling, database architecture, database design, database programming such as SQL, Python, etc., forecasting algorithms, cloud platforms, designing and developing ETL and ELT processes, etc.

Apply here.

4| Data Scientist at PayPal

**Location: **Bangalore

Skills Required: SQL and querying relational databases, statistical programming language (SAS, R, Python), data visualisation tool (Tableau, Qlikview), project management, etc.

Apply here.

5| Data Science at Accenture

**Location: **Bibinagar, Telangana

Skills Required: Data science frameworks Jupyter notebook, AWS Sagemaker, querying databases and using statistical computer languages: R, Python, SLQ, statistical and data mining techniques, distributed data/computing tools such as Map/Reduce, Flume, Drill, Hadoop, Hive, Spark, Gurobi, MySQL, among others.

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