Market Insights
Based on all acquiring transactions in the Nordics, we have developed a service that allows merchants and business intelligence users to identify ongoing market trends in real time. With a vast number of dynamic filters, users can tailor the insights to their business and turn the analytics into actionable insights.
Users can see weekly, monthly, and quarterly developments in card turnover, number of transactions and number of cards, and compare user defined time periods with historical figures. The correlation between card spending and number of cards is also included. Using this data, the service can visualize the effects of tourism, trends in card area origin and the use of business cards, giving deep insights into spending patterns for the user’s chosen segments.
The area of residence of a card is estimated from the consumer’s previous transaction patterns, which can be used to identify the distribution of cardholders for a specific industry or merchant area. All these insights enable benchmarking with historic and current market trends, information on business expansion opportunities and improved targeted marketing, all without increased consumer survey costs.
The different resources is explained in a short description with the different possibilities you get with this API, and some hypothesis for how you could maybe use the different data, which is possible to extract.
Resources
A list of resources bellow maps the API’s endpoint hierarchy. Find out more details on information pages made for each resource.
Verticals & Categories (Industries)
We categorize our merchants in two ways. Initially, a merchant is assigned one of the four vertical values for high level categorization. The second assigned value is a specific category to which the merchant belongs.
Verticals
Hospitality | Retail | Transportation | Other
Categories (Vertical)
An overview of relationship between Verticals and Categories.
Airlines & Travel Agencies (Transportation)
Amusement & Attractions (Hospitality)
Specialty Stores (Food) (Retail)
Bakeries (Retail)
Bars & Liquor Stores (Retail)
Building Supplies & Hardware Stores (Retail)
Specialty Stores (Non-Food) (Retail)
Transportation Vehicles & Parts (Transportation)
Eating Places (Hospitality)
Florists (Retail)
Healthcare (Retail)
Hobby, Office & Book Stores (Retail)
Hotels (Hospitality)
IT, Telecom & Electronics (Retail)
Jewelry (Retail)
Miscellaneous (Other)
Other Retail (Retail)
Services (Petrol) Stations (Transportation)
Supermarkets (Retail)
Transportation Services (Transportation)
Wellness, Beauty & Barber Shops (Retail)
Convenience & Variety Stores (Retail)
Digital Services, Games & Betting (Hospitality)
Furniture & Home Interior (Retail)
Clothing, Bags & Shoes (Retail)
Filters
Here you can see some more details about different filters (name of filter in parentheses).
Week number (transaction_week) - Filter by week number
Month (transaction_month) - Filter by month
Year (transaction_year) - Filter by year
Merchant country (merchant_country_a3) - Filter by a country from where a merchant is from
Merchant region (merchant_region_code) - Filter by a region from where a merchant is from
Merchant municipality (merchant_municipality_code) - Filter by an municipality from where a merchant is from
Merchant destination (merchant_destination) - Filter by an destination from where a merchant is from
Estimated residence country (estimated_residence_country_a3) - Filter by a country from where we estimate a consumer is from
Estimated residence region (estimated_residence_region_code) - Filter by a region from where we estimate a consumer is from
Estimated residence municipality (estimated_residence_municipality_code) - Filter by an municipality from where we estimate a consumer is from
Category (category_code) - Filter by a category. The aggregated data consists out of many transactions. These transactions are categorised based on type of merchant where the transaction occurred. For example, if a merchant owns a grocery store, the transaction made in his store will belong under Supermarkets category.
Vertical (vertical) - Filter by a vertical. The verticals are an abstraction level on top of category filter.
Online / Physical (online_physical) - Filter by ECOM or POS. The aggregated data consists out of many transactions. These are categorized on those which took place online (ECOM) or physically in the store (POS).
Business / Private (business_private) - Filter by Business or Private consumers. The aggregated data consists out of many transactions. Some transactions were made with credit cards owned by businesspeople, the others by private persons. Filter out what is needed to narrow down your search.
Domestic / International (domestic_international) - Filter by Domestic or International card spending. The aggregated data consists out of many transactions. If the credit card was issued in country which matches the country where we estimate a merchant is from, we categorise such transaction as Domestic. If there is a mismatch between these two values, the transaction is categorised as International.
Issuer Country (issuer_country_a3) - Filter by cards issued in specific country.
Regional Local (is_regional_local) - Narrow your search by looking at regional local or not regional local transactions. Regional local transactions correspond to the estimated residence region of a card matches the region of a merchant, and not regional local transactions are the case in which we estimate the cardholder to come from another region than the location of the merchant.
Municipality Local (is_municipality_local) - Narrow your search by looking at municipality local or not municipality local transactions. Municipality local transactions correspond to the estimated residence municipality of a card matches the municipality of a merchant, and not municipality local transactions are the case in which we estimate the cardholder to come from another municipality than the location of the merchant.
Important notes on the data
We unfortunately do not have all merchants as customers, so these numbers serve only as indicators of the whole market trend. This is to be remembered throughout the dashboard.
The same cardholder can have multiple cards, so be aware that the number of cards does not equal the number of consumers.
Some weeks can be pay week one year and not the other year, which of course could lead to wrong conclusions on trends, so be aware of holidays, pay weeks, seasonality, incomplete periods etc.
The area of residence of a card is only an estimate based on previous transaction patterns. Therefore, this should only be used as an indicator.
The residence area of some cards cannot be estimated if they have had to few transactions in the past 6 months, so only the estimable cards are accounted for in this analysis
Dictionary
Financial Term
Description
Vertical
Covers a group of industries.
Normalised
To allow the comparison of corresponding normalised values. (Dividing by the same number).
Merchant
The operator of a business
Domestic
Card spend in the same country as their card has been issued.
International
Card spend in a different country as their card has been issued.
Business card
Cards issued as business cards
Private card
Cards issued for private use.
E-commerce, Ecom or Online
Online transactions
Physical or POS
In-store transactions
From API
Description
ISO-A3
3 letters country code
(Denmark = DNK).
Industry/Category
For which a merchant is categorised as
municipality code
The number for a given municipality.
Category code
The number for a given
category/Industry
domestic_international
Domestic or
International (foreign)
business_private
Business or
Private card spending
online_physical
E-commerce or
Physical (POS) card spending
Vertical
The vertical which covers a group of categories
date, hour, day, week_number, month, quarter, year
T
The time options which can be used in filters