Description

Based on all acquisition transactions in the Nordics, we've created a data-based service that displays ongoing market trends. Dynamic filters turn analytics into actionable insights, allowing users to view weekly, monthly, quarterly, and yearly developments in card turnover, number of transactions, and number of cards. Users can also compare user-defined time periods with historical figures. Additionally, the correlation between card spending and number of cards is calculated to showcase which industries are affected by factors such as tourism, card area origin, business cards, and e-commerce development. The service also estimates the resident area of the cardholder based on previous transaction patterns, which can be used to identify the distribution of cardholders for a specific industry in a specific merchant area. These insights enable benchmarking with historical and current market trends, information on business expansion possibilities, reduction of consumer survey costs, improved targeted marketing, and more.

Verticals & Categories (Industries)

In terms of merchant categorization, we use two methods. First, a merchant is assigned one of four vertical values for high-level categorization. Second, the merchant is assigned a specific category to which they belong.

Strikethrough indicates retired categories.

Group By

The "group_by" filter allows you to define the structure and content of the response, similar to the "group by" functionality when writing SQL queries. The fields specified in this parameter will be used to group data in the response. For example, if you want to examine how much different nationalities spend in each country on a weekly basis, you can provide the "group_by" filter with the following: "transaction_year," "transaction_week," "issuer_country," and "outlet_country." This means that the data will be grouped by the transaction year, transaction week number, issuer country, and merchant/outlet country, and you will then receive the card turnover, number of cards, and number of transactions for each case. The result will have the following format:

{
  "transactions_year": 2020,
  "transactions_week": 4,
  "outlet_country": "Denmark",
  "issuer_country": "Finland",
  "card_turnover": 1000.50,
  "number_of_transactions": 35,
  "number_of_cards": 30
}

Dictionary