AI based recommendations
Kantonen, Eero (2024)
2024
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-2024090224642
https://urn.fi/URN:NBN:fi:amk-2024090224642
Tiivistelmä
Thesis explores the implementation of Rek.ai on Valmet.com, aiming to enhance user experience. Key metrics like AI statistics, website statistics and monthly reports were tracked and analyzed over a period of two months. Analyzing these key metrics between the two months did assess the added value brought by these AI-generated recommendations. In addition to the analytical approach, the development and
implementation of custom JavaScript code that was added to the Valmet.com was included. The implementation details were documented, and a technical overview of the code was provided. Valmet´s diverse range of services and five business lines with specific needs made the website navigation challenging. The goal of the thesis was to enhance the user-friendliness of Valmet.com and increase its value for various stakeholders by addressing critical user experience aspects. These goals were reached.
A detailed analysis of the rek.ai dashboard was conducted to assess the success of its integration to Valmet.com. Key metrics like AI statistics, website statistics and monthly reports were traced and analyzed over a period of two months from June to July 2024.
In conclusion, thesis aimed to enhance the user-friendliness of Valmet.com website by implementing rek.ai autocomplete feature and on page recommendations. By implementing rek.ai services like autocomplete and on page recommendations to Valmet.com, the search process was improved and finding relevant
information across the Valmet.com website was made easier for the users.
implementation of custom JavaScript code that was added to the Valmet.com was included. The implementation details were documented, and a technical overview of the code was provided. Valmet´s diverse range of services and five business lines with specific needs made the website navigation challenging. The goal of the thesis was to enhance the user-friendliness of Valmet.com and increase its value for various stakeholders by addressing critical user experience aspects. These goals were reached.
A detailed analysis of the rek.ai dashboard was conducted to assess the success of its integration to Valmet.com. Key metrics like AI statistics, website statistics and monthly reports were traced and analyzed over a period of two months from June to July 2024.
In conclusion, thesis aimed to enhance the user-friendliness of Valmet.com website by implementing rek.ai autocomplete feature and on page recommendations. By implementing rek.ai services like autocomplete and on page recommendations to Valmet.com, the search process was improved and finding relevant
information across the Valmet.com website was made easier for the users.