Data Sheets

Custora + Your Data Science Team

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Custora + Your Data Science Team How we work together Q U I C K G U I D E S U C C E S S @ C U S T O R A . C O M Overview At Custora, we know that infusing customer obsession into every touchpoint and every internal decision requires more than the right set of tools. It requires a "secret sauce" combining people, process and technology. Customer-centric companies empower their data and analytics teams to unlock and activate customer insights by... Our Philosophy Interaction Models Custora automates repetitive tasks (like segment pulls and campaign reporting) so that in-house modeling teams can focus on higher value projects. Instead of logging a ticket, business users log into the Custora app to access and export what they need. Free up time and resources 1 Minimizing rote work and freeing up time for substantive analysis. 2 Leveraging a combination of proprietary and 3rd party models to fit each use case. 3 Turning insight into action by seamlessly distributing data and analytics across every team and tool. Depending on the needs of the in-house data science team, Custora works closely with our customers to identify the partnership approach that will maximize the impact of your internal capabilities and augment ongoing data science initiatives. Even when in-house data scientists succeed at maintaining and running proprietary models at scale, adoption among business users can be a challenge without a point-and- click interface. By feeding model outputs into Custora, users throughout the organization gain easy access to insights and self-serve experimentation. Operationalize internal models Custora's predictive stats (like CLV, p.return, price sensitivity, product affinities) can be used by data science teams as building blocks for further modeling efforts, like price optimization, real estate acquisition strategy, forecasting and more. Advance further modeling efforts Transparent Collaborative Every insight on every customer can be programatically extracted We perform and share holdout valida- tions with data science teams to confirm model accuracy We are very open about the modeling tech- niques and approaches we use. We embrace opportunities to brainstorm how we can improve our models and to provide our input as your team works on custom, in-house models. 4 Continually building upon previous modeling efforts with new data and methods.

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