A feature of pricing in retail is that prices need to be determined for a large number of products (called SKU = stock keeping unit). Further, not all of these products are the same in the eyes of buyers. Some goods are key in the consumption of buyers and buyers compare prices for these goods from different sellers, preferring the seller whose price for this product is lower. These products are called Key Value Indicators. Other goods are rather related and the buyer, having purchased the main product, is ready to pay a higher price for the related product. These products are often called Longtail. Accordingly, it is necessary to be able to distinguish between KVI and Longtail products, since the pricing for them is different. In addition, the demand for a certain product, including that controlled by price, affects the influx of visitors to the store or to the seller's website, which in turn affects the purchases of other goods, and these dependencies also need to be able to evaluate. Also, sellers often have unique products that are difficult to compare directly with competitors and, as a result, set a price for them. And, finally, in real (not online) stores it is not always possible to change prices frequently, since you need to physically change the price tags. This is a time-consuming task. For this reason, optimal prices must also be predicted.

Solution
All of these calculations must be entrusted to algorithms, which at the end will calculate the optimal price for each SKU and explain why this price is currently optimal. All this information about prices and the factors on which they depend is available in the commercial manager's workplace (a special program for managing prices and assortment). There, the commercial manager, if desired, can agree or disagree with the recommended price and see the sales forecast, subject to acceptance of the automatic price or the price chosen by the manager himself.

The important thing is that the manager switches from the mass calculation mode to the mode of targeted adjustments with the ability to model: the recommended price is this - sales are this, but what if you increase/lower the price, what will happen to sales? Algorithms make it possible to calculate and see this.

Let's look at what we need to know to calculate the tariff for transporting cargo by rail from station A to station B. This example also works well for taxi services, road freight and air transport.
So, the carrier company needs to transport cargo from station A to station B on the client's order. To do this, you first need to deliver an empty (empty) car to station A and pay for it, since the client only pays for the transportation of his cargo. After the cargo has been delivered to station B, the empty car again needs to be picked up and delivered to the next customer and again paid for. Moreover, we do not know in advance where this next client is, since at the time of concluding a transaction with the first client, we do not know who will be next after him. All these costs for empty mileage are part of the cost of transportation, and it must be included in the tariff.
Further, all the time that the car is occupied for a specific client, this car is not available to other clients, which is logical. There is a loss of profit that must be included in the tariff. Why do we call this lost profit, and not just marginality, since this transportation is actually our service? The fact is that during the time when the car is unavailable for other orders, not only transportation, but also loading/unloading and, mentioned above, empty mileage are included, only not in terms of kilometers, but in terms of days. Accordingly, it is necessary to estimate the time the car is fully occupied (called "turnover") and include it as a cost in the tariff.
So far, we have only considered cost factors for the service (without fixed costs, of course). How can we determine the rate? We can use the cost-plus method, when we take the cost and add a fixed marginality. However, is it wise to do this? There are several reasons why it is not wise. Here, at least, these. The carrier company may have advantages over its competitors that it would like to reflect in the price. For example, empty runs from different stations, that is, the cost of removing a car after delivery of cargo, may differ. Since the cost price is different, then different prices can be set. Further, at the moment in a given location there may be a shortage or surplus of cars (balance of supply and demand), I would also like to be able to take this into account. Finally, different customers may have different sensitivity to transportation costs (for example, if transportation is a large or small part of the customer's value chain). If the advantage in empty mileage can still be taken into account relatively easily, then to take into account the balance of supply and demand, elasticity curves need to be calculated, and to take into account the specifics of the client, segmentation needs to be done.

Solution
It is necessary to use algorithms and machine learning methods. For each shipment, they can calculate empty mileage to the level of origin and destination station, taking into account the advantages in empty mileage over competitors. The algorithms will also calculate the turnover of the car and the optimum points on the elasticity curves to balance supply and demand. Algorithms can segment customers based on dozens of different parameters.

We have produced something and we need to determine the price for this product. Let's look at an industry whose consumers of products are legal entities, since the evaluation of products for B2C is close to the retail cases described above. So, let us produce products from which other products will then be produced. For example, polypropylene, which will later be used to make food containers, or car dashboards or bottle caps. How to determine the price of polypropylene. In fact, two factors are important here: the rating agency's quote and the client.

In the fitness industry, pricing primarily influences two factors - the likelihood of contract renewal and the propensity to consume paid additional services during the contract. Moreover, the consumption of paid services also affects the probability of renewal. Accordingly, we need to choose a price not only based on the prices of competitors, the season, the price in a given location, but also the likelihood of customer churn and the estimated damage from his loss associated with the consumption of additional fitness services. The price for additional fitness services must be dynamically calculated based on the needs of a given client, identified on the basis of a recommendation service that compares this client with other clients, and also takes into account the time of additional training, the people with whom this person trains and many other factors. All this is very difficult to do manually, but is successfully done by an algorithm.