REVIEWER: Andy Bryan, head of Customer Management for Otto UK.
BACKGROUND: Otto UK is part of the international Otto Group, the worlds largest mail order company.
PLATFORM: Mainframe, Teradata data warehouses.
PROBLEM SOLVED: In a business climate of increasing competition, the existing tools and processes in place at Otto UK were simply not able to keep pace with the new developments being made in our approach to business planning. We needed to be able to respond more quickly to change and be smarter in our approach to marketing to our approximately 1.5 million customers.
PRODUCT FUNCTIONALITY: Prior to KXEN, we were using SAS to build analytical models, but it was a time-consuming and labor-intensive process, taking around three days to build each model. With KXEN, what used to take three days now takes less than an hour. We dont need as many resources, we can build more models and we have the time to do more exploratory-type analysis. For our marketing, we used to only be able to deploy campaign-level key performance indicators (KPIs), but with KXEN weve been able to add the important extra dimension of customer-level KPIs. This gives us a more accurate prediction of returns from each campaign at a customer level, allowing us to optimize both our targeting and our mailing volumes. Additionally, adopting KXEN means weve been able to apply a best practice approach to all our modeling activity. And more importantly, weve been able to move beyond simple marketing campaign optimization into more strategic work such as ROI analysis. In short, were making better decisions.
STRENGTHS: KXENs sheer speed of building models gives us more flexibility and more time to do the important things. The availability of comprehensive, readily absorbed training is another strength, as was the way KXEN allowed us to apply best practices across the modeling activity. The other main strengths are its ease of use, the way it readily integrates with Teradata (our chosen data warehousing solution) and its fast implementation. It was only two months from signing the deal to our first KXEN-driven campaign.
WEAKNESSES: KXEN can seem like a black-box type solution to some modelers. There is a danger that de-skilling the modeling process will lead to pitfalls if carried out by untrained staff. Because it is vital that the front-end data set creation and back-end model application processes that surround the modeling steps are worked through using best practice methodology, we will continue to train modelers to complete the end-to-end process. In addition, the time saved will enable modelers to spend more time creating improved data sets to model and more time making use of the models to improve business decisioning.
SELECTION CRITERIA: Key selection criteria included the efficiency of models created with the solution, the solutions overall ease of use, the total cost of ownership, how well and how easily the solution integrated with our Teradata data warehouse and the speed with which it could build models. KXEN greatly reduced our modeling times and scored best in the other areas, too.
DELIVERABLES: Currently, we are using KXEN to deliver analytical models for optimizing our marketing campaigns and for creating management reports to aid improvements in future campaigns.
VENDOR SUPPORT: KXEN has provided comprehensive three-day training, and there is technical support available if needed. During the early implementation stages, we received one-to-one onsite support from KXEN, which included overview presentations and tailored training using our data. This helped us to build our best practice methodology and internal documentation.
DOCUMENTATION: We havent really needed to use the comprehensive KXEN documentation. KXEN is very user-friendly, and our internal documentation guides users through the process.
KXEN Analytic Framework
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San Francisco, CA 94105
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