eCommerce Transformation – The integration of Artificial Intelligence (AI) into electronic commerce has emerged as a prominent catalyst for revolutionary change in the realm of worldwide e-commerce. The fast-paced evolution of this trend is notably fueled by the increasing significance of emerging economies. The unprecedented adoption of digital payment mechanisms such as mobile, internet, and card-based transactions has brought about a paradigm shift in modern business operations, particularly in the domain of online commerce.

AI The Prominent Catalyst For eCommerce Transformation

Artificial intelligence has become a significant factor in e-commerce’s current scenario. Thus most online shoppers have a good understanding of how artificial intelligence works, particularly when it comes to encouraging them to make additional purchases unless they have been socially isolated for an extended period of time. The importance of organizing customers in a systematic way has increased thanks to the utilization of artificial intelligence technology. This has led to more precise and effective marketing strategies.

This possibility of utilizing artificial intelligence to enhance individualized shopping encounters for customers in the retail sector is huge. This technique focuses on how retailers can use artificial intelligence technologies to improve customer gratification, allegiance, and interaction. By making use of AI’s abilities, retailers have the opportunity to enhance their operations and provide personalized suggestions, smooth transactions, and bespoke services that meet the diverse needs of buyers.

The study sheds light on how AI-based solutions are crucial in moulding the upcoming trends in the retail industry and provides valuable insights into the essential factors that drive their broad acceptance. This study emphasizes the crucial significance of AI in enhancing creativity and productivity in contemporary retail, underscoring its capacity to revolutionize the retail industry and provide exceptional customer satisfaction. Nowadays, the ant colony optimization algorithm is widely used in e-commerce businesses as a solution to the issue of user bias in filtering and learning, specifically in the process of comparison shopping. It is applied to train feed-forward neural networks in acquiring user preference information. Improving proficiency is important for both buyers and sellers.


In today’s, online shopping how much is artificial intelligence. How artificial intelligence (Epicentre-of-eCommerce) can help retailers to deliver highly personalized experiences shoppers desire. Ant colony optimization algorithm (To formulate training feed-forward neural networks with ant colony optimization) to obtain the users’ preference information are widely used in today’s e-commerce business for comparison-shopping to solve the problem of the user bias’ filtering and learning. The performance improvements are important to both consumers/buyer and sellers/merchants.

The machine learning is now tightly packed as emerging learning inferences deep inside in functions of e-commerce. The ML integration increases the better chance of favourable probability around e-commerce users who are going to make a purchase in the given time interval. It makes eCommerce service provider powerful in terms of targeting the right customers in order to optimise the marketing budget.

How Will AI Influence E-Commerce?

On the basis of the traditional comparison shopping method, trains the neural networks via backpropagation. Relative to the traditional manual and subjective evaluation, this method can greatly extend auto-evaluation and reduce the arbitrary of the manual evaluation, thereby increasing the objectivity and accuracy of the evaluation.

Within the past few years, online businesses have grown rapidly with business-to-consumer electronic commerce (e-commerce) sales growing to 21.1 per cent. According to an independent market research company e-marketer, e-commerce based transactions hit US$1 trillion for the first time in 2012, thereafter no look back.

In 2016 it got exponential growth over 2012 and 2017 figures till Sept equally critical is delivering this performance with reduced silicon (all grey & thin areas) area and industry power consumption. It also adopts the growth-oriented method of network structure to decrease the learning error. And the sequence of search results is reorganized based on the information, to provide users with the personalized shopping guide service to meet their needs.


Scalability both within and between the product families filled with or backed by artificial intelligence (as an Epicentre-of-eCommerce) technologies was also a key consideration in their development. Thriving economies like India and China’s forecast that the growth in digital shopper will be expanding enormously as the number of people who buy goods online is expected to go four-folds between 2015 and 2018.

AI (Epicentre-of-eCommerce) made predictions easy to know when customers will stop using a service to analyze potential reasons and allow for countermeasures. Sentiment analysis evaluates the public perception of a product based on sources like social media. Sequence mining technique applied to user’s navigation behaviour, to discover patterns in the navigation of websites.

Artificial intelligence as an Epicentre-of-eCommerce

The research on Indian eCommerce market recently revealed that Indian e-commerce grew by 31.4 per cent last 3 months, reaching a total market value of more than US$2.1 trillion. As on date most of the top e-commerce companies are from India, the United States and China. Using AI to provide a personal touch for the customer journey always result in huge value-add to merchants. Merchant/sellers/ retailers that have implemented the magical “personal touch” backed by AI strategically see sales gains of about 10% extra over others. Using the footstep graph to visualize the user’s clickstream data and an interesting pattern can be detected more easily and quickly.

Artificial intelligence as an Epicentre-of-eCommerce for thriving economies – But who is reaping the real rewards?

Africa’s e-commerce contribution to GDP remains low (half the levels seen in emerging economies from other continents). Although most micro and small enterprises in developing countries have yet to start buying or selling products online, recent developments are expanding but there is significant variation among individual countries. Senegal, Nigeria, Morocco, South Africa, Mozambique, Ghana and Kenya (top 7 eCommerce countries on the African continent. Despite a slow start, Africa’s digital development is on the rise and accelerating at full throttle.

Though not the continent’s largest economies) have Africa’s highest e-commerce contribution to their GDPs, and governments in the said countries have made concerted efforts to stimulate Internet demand. Artificial technologies can help in learning and earning more profits and top-up sales It could also boost profitability rates 59% in the wholesale and retail industries by 2035. Machine learning helps in reducing the required efforts bandwidth between the buyer, seller and manufacturers such bandwidth reductions also reduce the cost and required time.

The Future of AI, Retail and ROI

Consumption of goods goes high even at the buyer side because what is bought was needed the most. An empirical research approach can predict and categorize the users’ navigation behaviour much better with high accuracy rather than just trying to repeat past for the future. What Artificial Intelligence as an Epicentre-of-eCommerce means to eCommerce means to African Economies As per PwC, business leaders believe AI is going to be fundamental in the future. In fact, 72% termed it a “business advantage.” In eCommerce, AI-based technologies like Big Data, Machine Learning, Neural Networks, Data Science, Bots and Deep Learning (mainly for secured online payments) are current buzzwords.

These terms are used to from BI Intelligence, To illustrate the various applications of AI in eCommerce and use case studies to show how this technology has benefited merchants/e-commerce service providers. So under Machine Learning’s unsupervised learning category of association problem; the best example could be deduced the pattern of consumer behaviour/likeness. An example of this could be seen when a store implemented this type of algorithm in its system. It found out that there was a strong association between customers(males) who bought light blue shirt also bought black trouser or females who bought expensive shampoo for their long hair also bought good pair of earrings.

To deduce this statement that males who had gone out to buy a blue shirt also tend to black trouser as well. Illustrating a novel sequence mining approach to identify pre-designated user navigation patterns automatically and integrates back-propagation network (BPN) model smoothly. Different consumers have varied, and often very specific, requirements for the product, needs, expect performance, cost of consumption, silicon wafer thin kind of cost for the best thing in mind and other parameters.

By 2025, the artificial intelligence market will surpass $100 billion. Finally, the report weighs the pros and cons of strategies retailers can take to successfully execute AI technologies in their organization. According to some business Insider suggestions that as much as 85% of customer interactions will be managed without a human by as soon as 2020. Along with the required size of the frame buffer in e-commerce artificial intelligence has implications in terms of the amount, type and speed of processing of order needed in the design.

AI empowers online stores and eCommerce platforms

The baseline specifications for any customer interaction interface on e-commerce portal like all areas of search i.e. ranking, query understanding, query expansion, related queries are explained to machines at backend using best algorithms. Once these parameters are determined, the complexity of machine tasks, along with deep learning and other computation tasks, to be supported determines which product family (and member of a given family) is appropriate in a particular situation.

Dramatically better on-site merchandising i.e. with this, this product goes or customer who bought this have also shown interest in this etc or simply called recommendation are the key drivers of any e-commerce business. To safeguard the business from anti-social elements deep learning helps in fraud detection, prevention, velocity measure and makes better business decisions with the deep understanding of entity resolution (avoid multiple accounts of the same person), Image recognition and understanding, Concept extraction, sentiment and trend analysis makes buyers life easy to choose and buy. More the data more the issues and the harder it is to check for inconsistencies.

Customer support via bots and customer logistic needs by giving best anticipatory shipping and cost of it ship a product before an order is placed and time estimation, supply and demand analysis and forecast. Wallet management and funding source optimization. Various scheduling and optimal resource allocation. The applications mentioned above are only a small selection of what machine learning can do for e-commerce, but there are plenty of other options, such as Likelihood to Purchase,  inventory forecast to make production and distribution more efficient by predicting market demands, interest clustering and the list is endless.

In 2012, Senegal reported a 3.3% (e-commerce) contribution to its GDP against Sweden’s figure of 6.3%. Africa as a continent has become more connected with new applications, platforms and services making e-commerce more accessible and easier to navigate, thereby lowering barriers to entry. Clustering e-commerce customers with respect to their interest (the product/category or a specific item). Furthermore, merchants can easily target the right customer to take any action for certain products and categories i.e. automatically sort products into categories to speed up inventory management and improve customer navigation.

Artificial Intelligence Discovers Business‎‎

New digital products such as mobile applications and games with mobile payments at the backend are delivering new growth areas for developing countries. Other initiatives are already producing innovative web-based applications and dynamic new business models. For now, the internet in Africa remains a wide-open space where companies can capture large opportunities if they move rapidly and decisively.

The e-commerce process can be divided into four stages which are information gathering, agreement, transaction and delivery. These stages apply equality to B2C and B2B e-commerce. At each stage, there are potential implications for consumers, enterprises and other organizations as well as governments. Most exciting of all are the possibilities for using the internet to revamp the delivery of education, health, and other public services—transforming lives in the process. McKinsey offers a new and insightful way to estimate the importance of e-commerce — known as iGDP, which measures the percentage that e-commerce contributes to the GDP of a country. iGDP presents a realistic picture of how e-commerce shapes the economy of a country.

Components of e-commerce

  • Payments – mobile payments, eBanking and cards
  • Money Transfer
  • Procurement & Transport
  • Trading (Buy & Sell)
  • Backend intelligence technologies i.e artificial intelligence etc.

The internet’s contribution to Africa’s overall GDP is low. Senegal, South Africa and Kenya, though not the continent’s largest economies are in the lead. Short term opportunities – In Zimbabwe, there are lots of opportunities for eCommerce stores to provide good quality and much-needed products such as

  • Fast Moving Consumer Goods – FMCG
  • Food and groceries
  • Internet and data services
  • Low-cost electronics goods (mobile handsets, laptops and other devices)
  • Software services
  • Online tech support
  • Travel and leisure
  • Online/mobile booking for professional services.

How to recommend how much to spend and what Augmented experience give a very succinct response to this with artificial intelligence combined with other data intelligence.

Shopping, of course, is not the only industry to leverage recent advances in machine learning. The list of companies and industries is growing by the day in addition to the various applications of machine learning. Common applications of machine learning in today’s technology include voice recognition, fraud detection, email spam filtering, text processing, search recommendations, video analysis, etc. In addition, these current technologies are being improved daily, with these improvements being fuelled by greater data analytics, reduction in the cost of computation, and advancements in the state of the art of machine learning research.

Identify Exceptional Target Prospects

Just to elaborate one item from the above list in African markets and opportunities are even more dramatic – In just the past five years, Africa’s mobile phone market has expanded to become larger than either the EU or the United States with some 650 million subscribers. At the same time, Internet bandwidth has grown 20-fold as hundreds of thousands of kilometres of new cables have been laid across the continent to serve an increasing number of its 1.2 billion Africans

Which side do you sit? – eCommerce will surely boost the African economies but to fully exploit the potential, we need to answer a very important question; Which side we put our self to boost our eCommerce business and its growth to help the economy?

There are 2 sides of the eCommerce coin, either in front of a consumer or at the back as a service provider and for any economy to scale up and serve better, the service providers need to focus and should serve more local requests for all types of services and have front-facing requests coming from across the globe.

  • Payment infrastructure availability – Africa has payment instruments such as e-wallets and m-wallets
  • Local goods delivery
  • Diaspora dependent households – Africa has a very high percentage of diaspora dependency, which can be, optimize in a good way to use e-commerce.
  • Bulk buy and sell in small quantities in direct to consumer from manufacturer or producers or stockers
  • Prepaid airtime top-ups, airtime credit (loan) and microloans through mobile money.
  • Along with many players in the payment jungle, it is very easy to conclude that cash is expensive to handle and cheques are due to be phased out very soon. Individuals and corporates need electronic methods of completing payments, most of which will involve mobile technology which is easy, fast, low cost and quick.
  • Smartphones will contribute and penetrate this area of transactions and most of the payment services offered will be online i.e USSD, SMS, Mobile App.
  • Utility bill payments, the third most commonly offered product by mobile money providers which represents the second-largest contribution to the global product mix by value. Bill payments like water, electricity, internet, gas, school fees
  • A number of small card readers and associated applications are being developed for smartphones this means the big and bulky POS machines which are costly in CAPEX and OPEX might soon be phased out
  • Cross Border remittances in under a minute, which should be very cheap and flexible in terms of accessibility.
  • Merchant Payments – an important and crucial key success factor for this 3 trillion euros market.
  • Contactless payment systems based on near field communication (NFC), QR or scan codes will contribute and offer a viable alternative for low-value transactions in developed countries.
  • Hospitals, schools and colleges will need to provide alternative payments technologies, like cash, internet and cheques will be phased out due to speed and cost.
  • Railway stations, bus terminals, movie theatres, game shows, events and all mass sales need to come on mobile payments.
  • How to ensure all daily sales of water bottles, soft drinks and all micropayments comes to mobile

No – To explain the reason we need to look at history and current market trends.

  • Limited adoption of ICT in SMEs & Lack of dynamism between ICT firms and SMEs.
  • Old payment instrument still plays and sound very well – It is found that many people are still using the old way of making payments; the popular method of payment, which is physical cash payments.
  • Feel & Touch – Prefer to feel and touch the product before committing payment or my dollar.
  • Trust – Trust on online products is still very low along with using my payment details on online store

Points to Note:

All credits if any remains on the original contributor only. We have covered all basics around the role of AI and Machine Learning in eCommerce. Machine Learning is all about data, computing power and algorithms to look for information. In the upcoming post, we will talk about Generative Adversarial Networks. A family of artificial neural networks which a threat and blessing to the physical currency market.

Books + Other readings Referred

  • Research through open internet, news portals, white papers and imparted knowledge via live conferences & lectures.
  • Lab and hands-on experience of  @AILabPage (Self-taught learners group) members.


Feedback & Further Question

Do you have any questions about Supervised Learning or Machine Learning? Leave a comment or ask your question via email. Will try my best to answer it.

Machine Learning (ML) - Everything You Need To Know

Conclusion –  Before we conclude AI as an Epicentre-of-eCommerce; we need to find an answer for “Is Africa reaping the rewards? Or in short, is Africa ready for such business? The answer is Yes – Saying yes can be very immature at this juncture but as there are possibility and hope for increased activity. There is potential not just from internet mode eCommerce but from mobile commerce due to the high penetration of internet and mobile phones in the global markets.

Designing adaptive hypermedia for internet portals that provides personalization strategy featuring case base reasoning with compositional adaptation. working well in todays time of ecommerce. Mining consumer navigation history for recommendation with neural network techniques can always help in making better business model. There was few discussion to mention here about the topic.

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Posted by V Sharma

A Technology Specialist boasting 22+ years of exposure to Fintech, Insuretech, and Investtech with proficiency in Data Science, Advanced Analytics, AI (Machine Learning, Neural Networks, Deep Learning), and Blockchain (Trust Assessment, Tokenization, Digital Assets). Demonstrated effectiveness in Mobile Financial Services (Cross Border Remittances, Mobile Money, Mobile Banking, Payments), IT Service Management, Software Engineering, and Mobile Telecom (Mobile Data, Billing, Prepaid Charging Services). Proven success in launching start-ups and new business units - domestically and internationally - with hands-on exposure to engineering and business strategy. "A fervent Physics enthusiast with a self-proclaimed avocation for photography" in my spare time.

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