By applying AI-driven driver analysis to Hansaplast Amazon reviews, Caplena and Beiersdorf won an Innovation Award!

Caplena, in collaboration with Beiersdorf, proudly received the prestigious 2023 Innovation Award at the German Market Research Congress in Frankfurt/M on June 14th, 2023. This award recognizes their innovative approach to analyzing online reviews by leveraging AI-powered insights to optimize product quality. This accomplishment is the backdrop for this case study to showcase how Beiersdorf adopted this approach by utilizing Caplena to improve its customer feedback loop in Hansaplast’s product development.

Challenge

Beiersdorf AG, specifically its market research team at Hansaplast, relies on Amazon reviews to gain insights into customer sentiment regarding their products. However, they faced a challenge in analyzing these reviews comprehensively due to the difficulty of categorizing them across the many languages and product categories. The existing analysis focused solely on star ratings, lacking an exploration of the underlying factors that contributed to each rating. To gain a more comprehensive understanding of the reviews, it was necessary to conduct an analysis that could identify themes and sentiments associated with the respective star ratings.

Solution

Beiersdorf's market research team now leverages the automated insights platform, Caplena, to conduct text analysis and effectively address these challenges. The methodology within the platform consists of the following steps

• Topic Identification: Caplena employs an AI model trained on a vast dataset of processed projects to identify and synthesize themes from text comments. This allows for a comprehensive understanding of the Amazon reviews utilized, going beyond pre-defined topics.

• Topic Assignment: After topics are identified, they - and their respective (topic level) sentiment - are automatically assigned to reviews. Although the automatic assignment is of high quality, measured through the F1 score which is transparently displayed at all times, domain experts manually review a subset of the comments to bump up the accuracy to the desired amount.

• Driver Analysis: By assigning topics and sentiment to the comments, the unstructured data becomes structured. A driver analysis is performed using the topics as explanatory variables and the star ratings as the target variable. This analysis provides regression coefficients that indicate the strength and direction of influence each topic has on the star ratings.

Results

By leveraging Caplena, Beiersdorf successfully achieves a structured analysis of unstructured online reviews. A fully interactive dashboard with a driver analysis on the topics allows insight managers to understand which mentions impact the star rating for specific products. This allows decision-makers to act on frequently mentioned and newly arising issues that strongly impact the star rating.