Returning customers are an asset for every online shop, especially if they exhibit regular purchasing behavior. The Predictive Basket recognizes precisely these patterns and makes suggestions to customers in advance about which purchases they will make again in the near future.
The technical documentation of the REST-API is contained in the search application itself and can be accessed via [your NG server]/fact-finder/swagger-ui.html . It can be also accessed via the following URL:
**Example: https://ng-demo.fact-finder.de/fact-finder/swagger-ui.html
Supplying the Predictive Basket with Data
In order for us to set up the predictive basket with your product information, we need the sales data in an organized structure. A CSV text file is a good format.
By following the recommendations below, it is possible to import the export file into the Predictive Basket without major adjustments. This minimizes project costs. If you are unable to create this structure, we can most likely still use your differently structured data by creating an import routine - just contact us.
Please pay attention to the following points when creating:
Create the export file in CSV text format (fields separated by a separator such as semicolon or [Tab]).
If possible, create the text file in the UTF-8 character set. If you decide to use a different character set, you must let us know.
Create a new row for each product (record) purchased.
Each row must contain the same number of fields.
Make absolutely sure that none of the separators used appear in the order data itself. Otherwise, the field structure will be interpreted incorrectly during import, the field contents will be mixed up and the corresponding data record cannot be imported.
There must be no column separator at the beginning and end of each line, unless the first or last field is present but is empty in some records. If a middle field is to remain empty, the separator must still be written. (Example: content1;content2;;content4; - five fields, fields three and five are empty).
The first line of the CSV file must contain the field names. The structure is identical to the data lines (see example below).
Example data structure without field delimitation
Bestellnummer;Verkaufsdatum;Kundennummer;Produktnummer;Menge;Preis
02101;2020-04-02T15:20+01:00;0023928;23899823;1;699.00
02102;2020-04-02T15:20+01:00;0034228;23892326;2;199.00
02103;2020-04-02T15:20+01:00;0123421;23899854;1;849.00
...Example data structure with field delimiters
"Bestellnummer";"Verkaufsdatum";"Kundennummer";"Produktnummer";"Menge";"Preis"
"02101";"2020-04-02T15:20+01:00";"0023928";"23899823";"1";"699.00"
"02102";"2020-04-02T15:20+01:00";"0034228";"23892326";"2";"199.00"
"02103";"2020-04-02T15:20+01:00";"0123421";"23899854";"1";"849.00"
...Additional Information
In order to be able to present a complete result later, the following information must be provided. Fields marked with an asterisk (*) are mandatory.
The field names given are to be understood as recommendations. The naming is not case sensitive. By following the recommendations, it is possible to import the export file into the Predictive Basket without major adjustments. This minimizes project costs.
Order number *
This information is important to clearly assign the records. The order number field should be the first field in your export.Date of sale *
The date of sale is required as a timestamp according to ISO 8601. Alternatively, you can also provide the date in the format YYYYmmdd.
Customer number *
Please enter a customer ID here; this can be anonymized. However, the customer ID must remain the same across sessions.
product number*
Unique ID of the product that was purchased.
Quantity*
Quantity of the product purchased as an integer.
Price
The valid price for the product. The field should not contain any currency symbols and should use the same decimal separator throughout. The decimal separator must be a period, not a comma. There must be no thousands separator. Example: 99