Fast decision making in trading

The past year has presented trade networks with hitherto unknown challenges, with the onset of the epidemic in Hungary due to increased demand for certain products and difficulties in supply chains, followed by various restrictions. As a result of the events, the reordering of shopping habits has accelerated, and in this often impulsively changing period, it is necessary to react faster than usual. The more conscious use of data, more accurate predictions and the role of tools to support rapid decision making will thus be appreciated.

It has become common market opinion that physical service locations such as bank branches, grocery and other commercial outlets are increasingly being replaced by webshops. There is some truth to this, but in the meantime, the world's largest online marketplace, Amazon, is working on open a grocery storethat, by the way, already in addition to its long-running retail stores. That this will work is not a question, Amazon sits on a wealth of data of amazing size, practically knows the individual's buying habit better than the buyer himself, so he can very easily make decisions about what location and what product to open physical stores with.
Of course, it is difficult to catch up with Amazon in terms of data, but today any major Hungarian commercial service provider has a central transaction system, loyalty cards are widespread, efficient inventory records are kept and there is a lot of information about the environment of stores, traffic and purchasing power. The question is, for what purposes this information can be put into the service of the most effective business. We have collected a couple of points where data can be a business value for classic commercial networks:

Optimization of network operational efficiency

For a commercial company with hundreds of employees or at least 10-15 stores, the company's data has huge cost savings and sales potential, which can be used to improve the operational efficiency of the network. This is what the following will be about our webinar too.

Product range, demand forecast, selective pricing

Understanding the buying habits and behavior of customer groups is impossible without the use of data, but without them, sales can drop by 10-20% of a trader. With a more accurate forecast of demand, selective pricing that is more closely aligned with demand and customer habits, profit generation can be significantly improved.

Logistics, warehousing

The improvement of operating costs is achieved not only through the optimization of network operations, but also through the optimization of logistics and warehousing costs. The positive impact of accurate demand forecasting for individual products and product groups is directly reflected in the customer experience and indirectly on the revenue side.
In the case of products that are difficult to stock or perish quickly, an accurate demand forecasting model plays an important role in minimizing losses and saving direct costs.

Marketing

It is almost obvious how useful the use of data is in the field of online marketing, but here you should not rely solely and exclusively on solutions provided by marketing tools, customer behaviors mapped from internal transaction data, messages delivered to specific target groups on the basis of these, pay off many times compared to non-personalized messages.