Convenience is a major factor that is compelling customers to prefer online Ecommerce platforms over brick and mortar stores. Consumer behaviour is vastly different for different platforms, such as B2B and B2C enterprises. Customer relationships are generally short termed when it comes to B2C, while B2B platforms enjoy a long term relationship. Such differences also result in different challenges faced by individual platforms. The B2B platform challenges include the management of cash flow, inventory management, customization of products and setting the price accordingly, and delivering an enriching customer experience. This is where Artificial intelligence (AI) and Machine Learning (ML) comes into the picture and can help to boost B2B ecommerce.We already know how the big tech companies including Google, Microsoft, Twitter, Apple, IBM, Qubit, Intel, Pindrop, etc. are enjoying the benefits of implementing AI and machine learning. However, several less well-known companies have started to either develop or use machine learning in interesting and innovative ways.
AI and ML, effectively help to raise the standards of operation of B2B ecommerce, to effectively meet the rising demand as well as the rising expectations of customers. The advantages provided by these platforms are diverse and are effective in providing the user with a competitive edge over their counterparts. Companies are effectively tackling questions such as how to use artificial intelligence, how to use machine learning by employing skilled labour or by taking the assistance of third-party consultants to boost market presence

How AI and ML are helping B2B Ecommerce

• Customer Segmentation

AI and ML are effective in customer segmentation. Customer segmentation is the ability to sort relevant data about customers and to map customers accordingly by using this data. This saves time as well as efforts for the company. In the era of big data, companies have a lot of data pertaining to their customers. The data may be about varied aspects such as geography, demography, detailed purchasing behaviour among others. This data if not properly segmented is of no use to the company. AI, can help to better segment this data and classify the customers and rank them according to their probability of buying. One can easily go through these data points and then can chart out their strategy accordingly.

• Provide Better Customer Service

Customer service is important to develop long term relationships with customers, something which is very important for the success of a B2B ecommerce platform. Customer relationship can be developed by using strategies such as prompt and positive reply, assistance, etc. In the recent past, AI-powered conversational entities have been developed which further strengthen the customer service aspect. Intelligent assistants, chatbots, for example, assist customers over their entire journey of purchase. In addition, they make the whole process interactive and informative.
Some of the queries may be complex, and cannot be solved by these chatbots or intelligent assistants. In such cases, companies transfer such queries to the customer service executives, which may then take up the matter personally and try to solve the problem. Although these AI tools cannot replace human sales representatives, marketing executives and customer care representatives, they are bound to become an integral and indispensable part of the whole customer service department of an organization.

• Predict Buying Pattern

Machine learning and AI can effectively predict buying patterns of customers and by analysing these patterns can ultimately boost the sales of a company. The difference between sales database systems and ML is that the former can track purchases only with sufficient data and the latter can predict the regular habits of a customer.
Efforts have been taken in the recent past to blend natural language processing with predictive analytics. These efforts have made it possible to predict future choices of consumers as well as their shopping behaviour. The amalgamation of ML as well as AI, in B2B ecommerce platform, has resulted in customer getting relevant suggestions about different products and the techniques are becoming increasingly instrumental in differentiating buyers from visitors.

• Offer Personalized Experience

The AI and ML are instrumental in providing customers with a personalized experience, tailor-made specifically for each buyer. The recommendation engine has specifically made this entire process easier. The alignment of products on display, with the interest of the customer, is particularly necessary for any ecommerce platform to boost sales. AI and ML, analyse the past shopping history as well as shopping details to display products of interest to the buyer. They are also instrumental in implementing dynamic pricing for B2B customers, as the value of the transaction is high in such cases.
Recommendation such as warranties, guarantees, discounts, etc. can also be facilitated by using ML and AI. Due to this personalization feature, AI and ML, can recommend upsells as well as cross sells of products to boost the overall revenue of B2B ecommerce platforms.

• Simplified Product Search

The introduction of ML and AI has reduced the consumer dependence on text, to search for the products of their interest. Novel features have been added to assist customers in their efforts to search for their product of choice and have reduced dependence on text-based search engines. Text to speech feature has added speed and convenience to the overall search experience. Digital assistants can be used for checking the availability of the required product. Image recognition has also revolutionized the whole search efforts, in which an image is uploaded which are then processed by an AI algorithm, which then gets back with the most relevant product searches.

• Informed Decision Making

AI and ML are instrumental in making informed decision making. They can analyses data and provide forecasts in real time. Business owners, with the help of these forecasts, can take better decisions regarding the display of relevant products to a particular customer, the pricing strategy that needs to be employed, and decisions pertaining to discounts, warranty, etc. These decisions are made by a solid backup of predictive analytics performed by AI as well as ML, by processing every minuscule data of the customer available to the company. B2B ecommerce platforms are in a better position to chart strategies regarding pre-sales efforts, post-sale customization offers and pricing, when they use ML and AI. They also help organizations to make decisions and to map out strategies in cases of one to one personalization scale.
All in all, ML and AI thus with their advantages, are able to offer an enriching experience to the customers, thus providing a quintessential edge to the B2B platforms employing the same.