When it comes to implementing AI, many factors influence your business’s cost. This includes your preference for a custom or pre-built solution, as well as the type of AI required.
For example, if your business relies on chatbots to streamline customer support interactions or a data analysis system to interpret large quantities of data, you’ll have to pay for those solutions.
E-commerce Pricing
E-commerce pricing is a vital part of a successful online business. The most popular e-commerce platforms offer tools to help businesses optimize their pricing strategies and maximise sales and profits.
One of the most common pricing strategies for online stores is to set a price based on the average prices of similar products. This allows the business to see what it can realistically charge for its products and how much margin they can expect to generate.
Another popular strategy is to use value-based pricing. This method takes into account the cost of freight and packaging expenses and sets a selling price that reflects what customers believe to be worth.
AI-based pricing algorithms can study pricing data over time and identify profitable price points for your products. The system then adjusts the price to optimize sales and boost your margins.
Retail Pricing
Retailers need AI-powered pricing tools that can handle a large volume of data from different sources without analysts’ intervention. They also need to be able to respond quickly to price changes from their competitors.
As e-commerce channels continue to evolve, it’s becoming more and more common for retailers to change their prices multiple times per day. These rapid price changes are challenging for traditional data scientists-led methods, because they require new and updated data to calculate how customers will react.
Fortunately, AI technology offers retailers a way to model scenarios that will anticipate how customers will respond to these changes. With this, they can then implement pricing plans that will lead to the best possible results.
Another aspect of retail pricing that AI can help with is item mix optimization. It can identify the items that work together to create a complete solution for the customer and maximize the impact of these relationships. It can account for factors like cannibalization, pantry loading and seasonality – boosting traffic, basket size and sales of related products.
Corporate Bond Pricing
Corporate bonds are debt instruments issued by companies to raise capital in exchange for a series of interest payments and full repayment at maturity. Issuers typically set bond offering terms in a prospectus to best fit their financing needs.
A company’s credit rating is a major factor in the pricing of corporate bonds. Several credit rating agencies, such as Moody’s Investor Service and Standard & Poor’s, provide independent analysis of each issuer’s credit quality.
When a company’s credit rating declines, its bonds may lose value. In these cases, bondholders may sell their bonds below face value on the secondary market to reduce their losses.
When determining the price of a corporate bond, investors calculate the “tree method” to determine its value and its yield. This yield is a product of the interest rate and the default risk premium for the issuer.
Financial Markets
Financial markets facilitate interaction between investors and debtors, and transfer risk (primarily through derivatives). They provide individuals, companies and government organizations with access to capital.
Investors can choose from stocks, bonds, currencies and derivatives to trade. The authorities set certain protocols and regulations to ensure that investors get a fair deal.
AI can also help market surveillance, by detecting potential illicit activity. For example, it can identify spoofing – where an order is cancelled and then placed again at a higher price – using natural language processing.
In addition, it can predict share price reactions for different sized companies and news types. This could be valuable to asset managers who are looking to increase their gross returns.
In recent years, the financial markets industry has faced some of its biggest challenges in its history. From full digitization to technology-driven disruption, these challenges require organizations to rethink the way they operate.