The rapid expansion of influencer marketing and social commerce has increased the reliance of brands and agencies on third-party analytics platforms to evaluate commercial performance and influencer effectiveness. In the context of TikTok Shop, platforms such as Kalodata and FastMoss play a central role in providing metrics related to sales, revenue, and audience engagement. Despite their widespread practical use, the methodological consistency and comparability of the data they provide remain insufficiently examined in academic research. The purpose of this thesis is to assess the degree of consistency between commercial metrics reported by Kalodata and FastMoss and to analyse how inter-platform discrepancies affect the interpretation of influencer performance within the TikTok Shop ecosystem. The study adopts a descriptive comparative research design based on manually collected data for a single product and a sample of top influencers over a 90-day observation period. The analysis compares aggregated indicators, time-series dynamics, and influencer-level metrics, as well as relative differences between platforms. The results reveal that while the platforms exhibit substantial discrepancies in absolute commercial values, particularly revenue-related metrics, they demonstrate a high degree of structural consistency. Trend direction and influencer rankings remain largely aligned across platforms despite significant differences in scale. These findings suggest that inter-platform variability is systematic rather than random and is primarily driven by differences in data aggregation logic. The study contributes to the literature on digital commerce analytics by highlighting the distinction between level-based and rank-based indicators and offers practical implications for the use of third-party analytics platforms in influencer marketing decision-making.
La rapida espansione dell’influencer marketing e del social commerce ha incrementato la dipendenza di brand e agenzie da piattaforme di analisi di terze parti per la valutazione delle performance commerciali e dell’efficacia degli influencer. Nel contesto di TikTok Shop, piattaforme come Kalodata e FastMoss svolgono un ruolo centrale nel fornire metriche relative a vendite, ricavi e coinvolgimento del pubblico. Nonostante il loro ampio utilizzo pratico, la coerenza metodologica e la comparabilità dei dati da esse forniti risultano ancora poco esplorate nella letteratura accademica. L’obiettivo di questa tesi è valutare il grado di coerenza tra le metriche commerciali riportate da Kalodata e FastMoss e analizzare in che misura le discrepanze inter-piattaforma influenzino l’interpretazione delle performance degli influencer all’interno dell’ecosistema di TikTok Shop. Lo studio adotta un disegno di ricerca descrittivo-comparativo basato su dati raccolti manualmente relativi a un singolo prodotto e a un campione di influencer di alto profilo, osservati su un periodo di 90 giorni. L’analisi confronta indicatori aggregati, dinamiche temporali e metriche a livello di influencer, nonché le differenze relative tra le piattaforme. I risultati mostrano che, sebbene le piattaforme presentino discrepanze significative nei valori commerciali assoluti, in particolare per quanto riguarda le metriche di ricavo, esse evidenziano un elevato grado di coerenza strutturale. La direzione dei trend e il ranking degli influencer risultano in larga misura allineati tra le piattaforme, nonostante differenze sostanziali nella scala dei valori. Tali evidenze suggeriscono che la variabilità inter-piattaforma sia di natura sistematica piuttosto che casuale e sia principalmente determinata da differenze nelle logiche di aggregazione dei dati. Lo studio contribuisce alla letteratura sull’analisi del commercio digitale mettendo in luce la distinzione tra indicatori basati sui livelli assoluti e indicatori basati sul ranking, e offre implicazioni pratiche per l’utilizzo delle piattaforme di analisi di terze parti nei processi decisionali legati all’influencer marketing.
Dinamiche dell’attenzione e della domanda nel social commerce basato su TikTok: evidenze descrittive da Exolyt e Kalodata
MELIKOVA, MAVZUNA
2024/2025
Abstract
The rapid expansion of influencer marketing and social commerce has increased the reliance of brands and agencies on third-party analytics platforms to evaluate commercial performance and influencer effectiveness. In the context of TikTok Shop, platforms such as Kalodata and FastMoss play a central role in providing metrics related to sales, revenue, and audience engagement. Despite their widespread practical use, the methodological consistency and comparability of the data they provide remain insufficiently examined in academic research. The purpose of this thesis is to assess the degree of consistency between commercial metrics reported by Kalodata and FastMoss and to analyse how inter-platform discrepancies affect the interpretation of influencer performance within the TikTok Shop ecosystem. The study adopts a descriptive comparative research design based on manually collected data for a single product and a sample of top influencers over a 90-day observation period. The analysis compares aggregated indicators, time-series dynamics, and influencer-level metrics, as well as relative differences between platforms. The results reveal that while the platforms exhibit substantial discrepancies in absolute commercial values, particularly revenue-related metrics, they demonstrate a high degree of structural consistency. Trend direction and influencer rankings remain largely aligned across platforms despite significant differences in scale. These findings suggest that inter-platform variability is systematic rather than random and is primarily driven by differences in data aggregation logic. The study contributes to the literature on digital commerce analytics by highlighting the distinction between level-based and rank-based indicators and offers practical implications for the use of third-party analytics platforms in influencer marketing decision-making.| File | Dimensione | Formato | |
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https://hdl.handle.net/20.500.14239/35481