  This article proposes a method to uncover opportunities for exploitation and
exploration from consumer IoT interaction data. We develop a unique
decomposition of cosine similarity that quantifies exploitation through
functional similarity of interactions, exploration through cross-capacity
similarity of counterfactual interactions, and differentiation of the two
opportunities through within-similarity. We propose a topological data analysis
method that incorporates these components of similarity and provides for their
visualization. Functionally similar automations reveal exploitation
opportunities for substitutes-in-use or complements-in-use, while exploration
opportunities extend functionality into new use cases. This data-driven
approach provides marketers with a powerful capability to discover
possibilities for refining existing automation features while exploring new
innovations. More generally, our approach can aid marketing efforts to balance
these strategic opportunities in high technology contexts.
