Customer data driven
A cutting-edge, customer data led approach to curating globalised, localised, or personalised product ranges. Optimise commercially focused assortment and merchandising decisions through data science.
What we do and the problems we solve.
The right product, at the right time in the right place has never been more crucial in the current retail landscape. Retailers need to be able to adapt and optimise assortment to be as competitive as possible and speed of insight is key. Hyper’s solutions enable lightning fast, commercially actionable assortment decisions powered by affinity analytics.
Understand the deep relationships between products, to measure their affinity, substitutability and create detailed features and attributes.
Utilise both purchase and browsing data to identify the relationships between all your products and how customers interact with them.
Create affinities at lightning speed across thousands of products and years of customer behavioural data.
Re-configure and recreate affinity calculations in seconds, augment with key business metrics and visualise clearly in the HyperFinity platform.
Customer decision trees.
Utilise machine learning and AI to automatically create interactive customer decision trees across all your categories at the click of a button.
Products are intelligently clustered on their similarities based on how customers browse, interact and purchase them.
Decision trees are highly interactive allowing the user to trim nodes, merge, re-cluster and rename need states easily.
Combining decision trees with embedded analytics in one platform allows the user to fully understand how and why need states have been created.
Make intelligent, data-led decisions about ranging and assortment across all of your categories. Accurately simulate and forecast endless scenarios at the click of a button.
Fully understand product demand transference across every need state within every category.
Optimise ranges based on customer need states, maximise customer reach and minimise sales cannibalisation.
Interactively simulate commercial range scenarios or utilise machine learning to automatically recommend optimal ranges based on any commercial parameter.