Data Science in retail

Online and brick-and-mortar retail offers huge potential for the use of machine learning and artificial intelligence. From managing promotions to selecting the location of the next store, the possible applications for generating added value with data science are almost limitless.

Generating this added value from data is our daily mission. We have been supporting the retail industry with our data science solutions since 2010. Rely on our experience and transform your data potential into tangible competitive advantages.

A selection of the questions we have answered in our data science projects for the retail sector:

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How can the actual impact of promotions be determined?

Linking of different data sources (receipt data, master data, promotion features) and use of time series analyses to identify and adjust seasonal trends in the KPIs. Result: Report with statements on the influence of price promotions on the shopping basket.

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Which is the best location for building the next store?

KPIs, market characteristics, customer data, regional key figures: Analysis of the success factors of existing retail outlets. Creation of a reliable knowledge base for location planning.

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How can the distribution area for advertising brochures be optimized?

Determination of the effect of past sales promotions in zip code areas based on the sales generated. Determination of a scattering effect that expresses the changes in euros and thus decisively supports the successful planning of future campaigns.

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How to increase the response rate of email campaigns

Differentiation of existing customers according to their sales potential based on information such as their sales history and existing demographic data. Development of a scoring model to evaluate customers and manage future campaigns.

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How can product prices be adjusted more dynamically?

Average receipts, historical price promotions, time of day and information about the market environment: inclusion of different variables for dynamic pricing in order to always be able to set the optimum sales price with the help of algorithms.

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How can out-of-stock situations and long storage times be avoided?

Forecasting of expected sales volumes based on internal and external factors influencing customer purchasing behavior. This makes it easier to control procurement processes and further reduce overall lead times.

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Get started now:
We look forward to exchanging ideas with you.

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Your expert on Data-Science-Projects:

Lutz Mastmayer
projects@eoda.de
Tel. +49 561 87948-370







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