In this article, the focus will be to understand why it may be difficult for the B2B industry to adopt (artificial intelligence) AI full-fledged as compared to the B2C industry.
The B2B (Business to Business) and B2C (Business to Consumer) types of industries covered maximum share worldwide. B2B referred to the situation where one business makes a commercial transaction with another business. For example, Tesla buys battery packs from Panasonic for their electric cars, in this case, the transactions happened between two companies also the volume and price might be of big value and for the long term. Further B2B can be divided into Horizontal model and Vertical Model.
Whereas B2C referred to as the process of selling products and services directly to the consumers who are the end-users of its products or services. Most companies that sell directly to consumers can be referred to as B2C companies. It is the most used and popular business model. Started with posters, pamphlets, banners to the radio, TV to stores, and further current practices of using the internet (E-commerce). For example, Amazon sells products directly to consumers through e-commerce, or Lego selling toys through its stores worldwide.
In many cases, the overall volume of B2B (business-to-business) transactions is much higher than the volume of B2C transactions. The primary reason for this is that in a typical supply chain there will be many B2B transactions involving sub-components or raw materials, and only one B2C transaction, specifically the sale of the finished product to the end customer. For example, an automobile manufacturer makes several B2B transactions such as buying tires, glass for windows, and rubber hoses for its vehicles. The final transaction, a finished vehicle sold to the consumer, is a single (B2C) transaction.
The point which draws a difference between B2B and B2C industry with reference to AI are:
1) Regulation and legal concern
The regulation and legalization of using data are some of the important factors when drawing comparisons and understanding the uses of AI in B2B and B2C industries.
Typically, in the B2C industry mostly companies collect not only persons’ detail but also their financial details, for example, Amazon collects the credit card information of their customers but at the same time, they focus and invest a lot in the security part. In case if any data of few customers are lost or misused (which is least expected) there are no regulations as well as rules to help customers or B2C companies not responsible.
In the case of the B2B industry, the rules and laws are more complicated and strict although clients never provide all financial details except accounting nos. also, the money transferred after lots of checks internally. Another point is the loss or misuse of financial data of a client can totally affect the business. Another example is the healthcare industry where the data is much more sensitive (medical and financial data) effective laws are present but not controlling AI advances of recent time.
2) Managing privacy concern and aggregate data use
B2C companies manage privacy concerns and customer data uses easier as compared to B2B. The consent approval by clicking the agreement from the customers to use the data before buying the product or services makes it easier for B2C companies. In fact, 99% of the time consumers never read that agreement or legal consent of no. of pages because they are focused to buy the product or service. For this reason, B2C companies may need to worry less about their terms and conditions; consumers are often disposed to sanction companies to further use of their data for marketing purposes in exchange for the product or services. This is not customarily the case in the B2B world where data privacy is of much more preponderant concern to businesses that want to protect their proprietary information and processes.
Further, if any B2B company wants to use AI for marketing, product development, or other purposes, and asked for consent to use some data of his clients. The client is going to scrutinize the terms of the agreement and may involve lawyers to ask for the addition of special clauses tailored to it, or request revisions to the terms before anyone signs anything.
3) Data Volume
In B2C business the volume of customers is in millions or billions, and to understand the behavior, prediction, and handling of extensive data of each customer manually is an impossible task for which AI is one of the best options. For example, if a company generates $100 Million in a year through the sale of their product or service by thousands of customers and to learn the pattern of each can only be done by machine learning system or AI.
Whereas in the case of B2B the same $100 Million can be generated by 20 or 30 clients and to handle the data and understand their behavior or prediction pattern didn’t require AI as it might not be suitable for the company.
4) Interpretability and Transparency
Another important difference is the understanding of data and transparency in the system. In the case of the B2C industry, they never explain or generalize that how come AI providing the conclusion or recommendations for a specific client. For example, Lazada (Part of Alibaba group) suggest few other relevant products to any customer after buying a single product, it will not create any risk to Lazada customer base same in the case of Netflix whenever any customer watch a TV series or a film the Netflix AI machine learning model suggest the same kind of other options. Overall the recommendations and suggestions accepted and used for more leverages from customers. Any wrong suggestion or recommendation never impact the businesses or risk for the B2C industry.
Whereas in B2B is not an easy task any suggestion and recommendation to the client required proper background and benefits detail. Moreover, the client has the power to demand maximum information to satisfaction. The wrong suggestion can terminate the relationship between company and client and loss of business subsequently.
5) Culture of Data science, risk, and experimentation
Another major point is uses of data science or digital advancement by B2C companies on regular interval makes them successful and provide lot easier working environment. The experimentation trait by the B2C industry helps them to understand the different processes and opportunities available through AI.
Whereas in the case of the B2B industry digital changes are happening but at a very slow speed which reduce the opportunities also the risk-taking abilities on AI platform are less visible in B2B companies.
One can say the difference arises because of the different functioning as well as the establishment of B2B companies which is older than B2C companies born in the last 20 to 30 years only.
At last, this is just an overview of thought generated based on different information and not support that B2B industry cannot integrate AI in their business but only giving insight that major changes required from B2B industry to compete in a futuristic AI-driven world where B2C companies are already presented significantly.
Thank you for reading. We hope this gives you a good understanding of Why the B2B industry still hesitant to use Artificial intelligence as compared to B2C or C2C? Explore our Technology News blogs for more.
