OFD (Fiscal Data Operator) is a special organization that ensures the transfer of data on the sale of goods and services received from cash registers operating in the online cash register mode to tax authorities. OFD plays a key role in compliance with the legislation on the use of cash register equipment (KKT) and ensures fiscal security.
The fiscal data operator's work algorithm is as follows:
1. The buyer makes a purchase, after which the cash register machine generates a transaction and sends it to the fiscal storage device.
2. The receipt is saved in the fiscal storage device and signed with a fiscal attribute. The data is redirected to the OFD server.
3. The operator generates a response fiscal attribute sent back to the cash register machine. The settlement information is transferred to the Federal Tax Service.
4. The buyer receives a receipt (paper and electronic). The receipt contains a QR code and a link. The buyer can then use them to verify the receipt in special applications. The entire chain takes a few seconds. The document received by the fiscal data operator is stored in the database for five years. The OFD client can access the information in their personal account.
In addition, many OFDs offer additional services, including data analytics. Businesses can receive sales reports, analyze consumer behavior, and track changes in demand, which helps in making strategic decisions.
OFD is mandatory for all organizations and sole proprietors using cash register equipment for settlements with individuals. This applies to retail stores, restaurants, cafes, pharmacies, service enterprises and many other types of businesses. The obligation to connect to OFD applies to companies and entrepreneurs operating under all tax systems, including the simplified tax system (STS) and the single tax on imputed income (UTII). However, the role of OFD is not limited to fulfilling the requirements of the law. Companies are increasingly using OFD data for business analytics. This data allows them to receive up-to-date information on sales, consumer preferences, peak sales hours, returns and other aspects of trade. Thus, OFD is useful for any organization that seeks to improve its business processes, increase customer loyalty and increase revenue.
The Gradus Agency offers a fundamentally new algorithm for processing OFD data. Gradus Retail Index analytics helps to predict the behavior of product prices and make the right conclusions. Using the service can cover from 70 to 90% of the market not numerically, but in a balanced manner, without extrapolation, the accuracy of the data will be no less than 90%. Collected together and correctly interpreted through machine mechanisms, the data are the basis for clear planning. Analytical calculations help to make a balanced decision on what discount to give for a promotion, how to forecast inventory balances, in what price segment to introduce a new product, how to optimize business processes within the company. The company works with both offline and online sales segments, evenly covers both network and non-network retail. OFD data is now actively sold, this is a relevant business tool, but this is not always enough for accurate forecasts, as a rule, the information is impersonal and does not give a clear picture.
OFD collects and processes various data on sales and transactions performed at checkouts. Among the key data:
Sales data: information on the number of goods sold, their cost, and revenue volumes. This data allows you to evaluate sales dynamics and identify leading products.
Data on products and categories: analytics by product groups helps you understand which product categories are in demand in different regions, on different days of the week, or even hours of the day.
Average check: the average check indicator allows you to evaluate purchasing power and provides information on customer behavior.
Seasonal fluctuations: analysis of time fluctuations allows you to plan deliveries and promotions, focusing on periods of increased demand.
Return data: information on the number and reasons for returns helps improve the range and service, and identify problematic products.
Time peaks: by analyzing the hours and days with the maximum number of purchases, a business can regulate the number of personnel and optimize working hours.
Regional analytics: allows you to identify differences in customer preferences in different regions and adapt marketing campaigns.
