Retail Case Studies
At Addepto, we specialize in creating custom machine learning solutions that meet our clients’ current needs as well as to adapt to future changes.
We have had the opportunity to work on various projects for many companies in the E-commerce & Retail sector.
Having the right price for both the customer and the retailer is an important advantage of optimization mechanisms.
Data obtained from multi-channel sources determine price flexibility, taking into account location, individual customer purchasing attitude, seasonality, and prices of competitors. Calculating the value extremes together with the frequency tables helps evaluate the variable and the excellent distributions of the predictors and the profit response.
This solution will help your business attract customers, keep attention, and implement individual pricing plans.
145% increased customer satisfaction
Modern Data Warehouse
If you want to understand the sales behavior of your products in the field by analyzing product baskets, sales correlations, and inventory, you need a modern data warehouse.
Data from a variety of sources: XML files from ATMs, business applications, and transaction systems can be loaded and used for detailed forecasting.
You can expect improvements such as cost-effectiveness, great performance for interactive analytics functions, and the possibility of new analysis and implementation of machine learning tools.
30% cost decrease
Managers in an FMCG environment often have to forecast the amount of inventory and supplies needed to meet demand.
The use of forecasting assumes that past trends will continue with little change in the future. If you want to achieve results with high accuracy, it is not enough to use historical trends.
A customized machine learning system that uses all available data from a variety of sources will enable you to predict demand with high accuracy using advanced self-learning algorithms.
25% cost decrease
Inventory and Demand Prediction
If your company operates in retail, you need an effective solution to accurately forecast demand and inventory needs. Machine learning technology can help.
ML tools can perform historical data analysis, which enables companies to anticipate inventory needs and better serve customers: from data collection and processing, building and training an ML model using fresh data, and accurately predicting inventory needs (all large-scale).
Your business will experience a reduced margin of error to ~ 2,000 SKUs, improved internal merchandising processes, and increased incremental revenue thanks to improved shelf availability.
20% increase in revenue
Sentiment Prediction & Text Classification
Internet traffic and customer awareness make promoting products on the online market extremely difficult.
Companies need an automated tool to help them promote products online, classify and select the best search terms without manual input.
Natural language machine learning system offers a solution that classifies Google search terms and predicts the sentiment of each comment on a social network based on historical data.
130% increase in customer engagement
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