Retail Analytics
Retail and especially online retail play a pioneering role in the areas of data science and analytics compared to many other industries.
Many years ago, retail companies in the European Union began systematically evaluating data in order to optimally manage prices, quantities and offers.
In the big data , retail companies are faced with very large amounts of data from transaction, storage and third-party systems, which can often only be meaningfully evaluated with special tools. The enormous amount of data is also offset by high optimization potential, which can be identified and raised with retail analytics.
Individual prices
Determination of customer-specific price elasticities and demand functions using choice sets.
Sales forecastings
Predict sales KPIs such as units, sales and profits using statistics and machine learning.
Recommender systems
Analysis of historical shopping baskets to generate customer-specific purchase information.
Price elasticities
Statistical modeling of price-sales functions at store for optimal pricing.
The increasing digitalization of retail enables retail companies to use data science, statistics and machine learning to optimize daily requirements, pricing and disposition planning.
Case studies in the field of retail analytics.
With retail analytics, you use internal and external databases and model them with models from statistics and machine learning.
The areas of application of data science in retail are diverse and often scale very well over the number of customers and transactions. Even the smallest improvements and optimizations can have a huge impact on revenue and margin.
Use our expertise in the retail analytics environment and arrange a consultation to clarify your options and needs.
- Retail pricing analytics
- Price simulation in retail
- Demand modeling