The continuous exploration of needs : sentiment feedback from Twitter for product innovation and improvement (at the example of the Apple iPhone)

Businesses must know what their customers want so that they can provide the optimal value for them. If they are unable to learn about their customer’s needs and provide products that are no solution to their customer’s pains, their product, or the company itself, ceases to exist. Thanks to customer’s social media posts about the products they buy, an opportunity emerges to aggregate and analyse their statements to learn from them. Exploring the aggregated needs will be done by building upon Kuehl et al.’s (2016) approach for “Needmining” and utilizing the Bidirectional Encoder Representations from Transformers (BERT) machine learning model with Twitter posts on the Apple iPhone. The performance of the model was insufficient; nonetheless, insights regarding the needs of customers were identified. This paper further investigates how the insights could be used for generative design to develop new, computer-generated design iterations. The problem with this is that the input to such generative design models is quantitative, while the collected sentiment is qualitative in nature.