5 data sources e-commerce company should excavate their Artificial Intelligence efforts

by Amit Kataria Data Science and Business Analytics

Many e-Commerce companies are looking to leverage artificial intelligence to reach more customers, improve their products and boost their sales.The trend can already be seen in e-commerce websites that feature recommendation engines, chat-bots and smart assistants.

  1. Browsing and search histories : Search and browse histories in different channels across multiple devices can offer wealth of information that organisations can leverage to help them understand the customer behaviour and offer relevant and helpful recommendations to individual consumers. 

Such practices have been successfully used by many enterprises. Amazon makes recommendations to its users based on their online activity on the website.This has helped further propel the e-Commerce company to becoming a global tech giant. 35% of its sale is generated by its recommendation engine. That success rate of more than three clicks. With AI and ML engines becoming increasingly accessible to businesses of all sizes, solutions like Pureclarity, which can guarantee recommendations for visitors based on their journeys are likely to see increasing demand.

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  1. Inventory and sales data: Past sales performance,inventory records and present Consumer behaviour is significant data that can be carefully studied to identify and analyse the trends and make smart products and service demands predictions.This information is essential in proper inventory management to help avoid financial investments unnecessary for storage or stockpiles.Integrated data from the other important transactions such as shipping and purchase orders as well as reports from various vendors and suppliers can help business plan and manage their procurement process, inventory control and product development.Use input like demographics, sales price,item promotions, competitor information and even weather.

  • Payment information : with more consumers embracing digital e-Commerce as a way to purchase goods and services, there is also an increasing number of online frauds online accidents. 2018 study shows that 63% of businesses have sustained losses due to fraud. Fortunately AI and machine learning can now help identify fraudulent or suspicious online payment activities through data analysis of online transactions. Collected payment information can be used to identify patterns of  legitimate and fraudulent payment behaviour. 

Activities tagged as fraudulent can also be confirmed and rejected through intelligent systems.The anomalies and trends from this data can then be interpreted and recorded to help companies adopt and detect suspicious activities that can use new and more sophisticated deception facts.

  • Customer reviews -  online reviews have also  become essential in the success of any product or service. 91% of consumers are more likely to use a business because of positive reviews. Unfortunately this has also led to proliferation of fake fake reviews online. 

Companies can leverage AI to identify legitimate reviews from fraudulent ones. While fake reviews typically include language tics to reflect those of normal online users, they can still be difficult to spot. Rigorous collection of data, capable filtering systems and artificial intelligence can go a long way towards ensuring online reviews are trustworthy. It's extremely hard to apply machine learning trained by humans to understand double sentiment as things like sarcasm are complex to code. 

However self learning systems sift through all industry products and categories to identify general sentiment and tone better understand cases of double sentiment. Identifying authentic user reviews are meant to be taken literally and can give business honest feedback about their products which they can use to improve their services.Additionally these reviews can also give them insights about the strengths and weaknesses of their competition.  

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  • Social media - AI powered solutions offer actionable insights from the collected data of a brand's and their audiences social media profiles.  These insights can help businesses track their engagement stats on a brand Facebook page that can be used to run more successful ads based on special demographic and behavioral targeting. AI enhanced sentiment analysis of social posts,meanwhile  can help e-Commerce brand reputations by identifying situations that call for immediate intervention.By feeding social media signals into AI engines companies can have a better idea of what their customers want , learn the language that can improve engagement, predict the time to publish posts to optimize the clicks and conversions and create a more relevant and meaningful content to gain a competitive advantage in the market.

  • Using AI and reliable data - AI can help e- commerce companies understand their clients better, automate their operations and offer seamless customer experiences.While technology can certainty help businesses keep pace with the ever- evolving demands of today's consumers, data collection and analysis will also be equally important.Leveraging AI with the right data allows organisations to pinpoint problems and opportunities to make the necessary changes and adjustments to their business.


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About Amit Kataria Innovator   Data Science and Business Analytics

12 connections, 0 recommendations, 57 honor points.
Joined APSense since, August 23rd, 2017, From Delhi, India.

Created on Jul 19th 2020 14:07. Viewed 505 times.


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