Understanding How AI and ML Improves Variability across B2C Enterprises

Artificial Intelligence and Machine learning solutions help B2C enterprises in deriving best strategy for business growth.

Artificial intelligence and its subsets are driving the pace of digital transformation across global B2C enterprises. Even before the COVID 19 outbreak halted business operations, disruptive technologies such as AI, machine learning and natural language processing were getting promptly adopted across the industry. A report by the United Nations Conference on Trade and Development (UNCTAD) states that the digital economy contributed 15% to the global GDP in 2017.

With COVID 19 outbreak, many factors thwarted efficient business operations. Prolonged stringent lockdowns, fluctuations in customer demands, and disrupted supply chain were some of the challenges faced by B2C companies. As B2C enterprises are driven by customer demands, identifying changes in customer's preference is the key to business growth. Henceforth, companies which integrated artificial intelligence, machine learning and NLP, performed comparatively well from those which did not opt such adoption.

A report by Adobe Digital Economy Index states that e-commerce revenues grew by 77% year-over-year in May to generate US$82.5 billion in revenues. Hence, this article focuses on the top use cases of AI and ML help in maintaining the volatility across B2C enterprises.

Improved Products and Services

The saying, 'The more, the Merrier' is perfectly apt for businesses aiming to provide different products and services. By incorporating AI and NLP, new products get designed based on customer preferences and demands. This will aid in the improvement of sales and an increase in the company's revenue.

Customised Conversation with the Customer

A very common customer trait is that they seek solutions for their queries whenever possible. Due to difference in time zones, answering such queries is often not possible for human customer assistants. Henceforth AI-chatbots and conversational AI lessens the burden of the B2C enterprises for providing 24*7 assistance to their customers.

Understanding the context behind customer behaviour

For any business to thrive, it is quintessential to understand customer behaviour. Surveys, reviews and feedbacks are common areas through which context behind the customer's response gets identified. By incorporating text analytics, the relevant information about customer's preference can be accumulated and categorized for proving products and services according to customer's demand. Additionally, these solutions also aid in identifying what factors are important for a particular purchase and what factors must be implemented for product growth.

Managing the back office data work

The unstructured and structured data generated on a routine basis is huge and explosive. Gaining insights from this data helps the business to understand the customer's preferences and demand better for improved business outcomes. The traditional data processing methods are unable to manage and monitor data for drawing timely appropriate insights for the enterprises. Henceforth by installing AI and ML algorithms, this structured and unstructured data can be easily maintained and monitored.

Managing Supply and Demand chain

Supply and demand play an integral part in business development and growth. However, miscalculation and mismanagement in the supply and demand domain leads to a loss in revenue and sales. By incorporating predictive analytics, the data can be analyzed accurately, which helps in identifying the demand of products across different geographies and areas. This aids the enterprises for a continuous supply of products without disrupting the business growth.

Identifying Discrepancies across the entire business route

Integration of AI and ML aids in monitoring the data traffic so that discrepancies across businesses get identified. This also helps in mitigating the incidents of fraud throughout business operations.

Predicting the Revenue

AI-based solutions and ML-algorithms analyses the accumulated data about different products and categorizes them according to the number of sales and amount of revenue, in the form of a database. Based on this the companies can understand the trends, compare the sales and revenue, analyze the factors behind the fluctuation in sales and predict the cash flow and revenue for a particular quarter.

Inculcating the Best Strategy

By incorporating AI and ML solutions, businesses can analyze the factors that might and might not work for product growth. Based on this, they can adopt the best strategy for their business.

Conclusion

With the digital era, incorporation of AI and ML-based solutions is imperative for steady and successful business growth. As competitiveness across business increases, with a customer-focused approach, AI and ML aids enterprises in devising the correct strategy for business growth.