Retail and
E-Commerce
(B2B, B2C)
Data and AI solutions from industry experts
Discover the potential of your core customers through customer segmentation and provide intelligent personalized recommendations for sustainable sales growth.
Business Benefits
Workflow
- What can customer analysis achieve in a specific case?
- Development of individual scenarios
- Workshop: Your company x ADEAL
- Conception of the MVP
- Analysis of data sources and IT architecture
- Implementation and piloting of the MVP for the selected scenario
- Concepts for further use cases
- Draft roadmap for the implementation of further use cases
- Rough draft of a use case-driven data and AI strategy
FAQ
-
Which customer analyses can be performed?
We calculate the ABC, RFM and Life cycle segments of customers, as well as KPIs such as CLV, lefietime and much more. -
What data is required?
The analyses can already be performed with the basic data. A table with the columns "Date", "Customer number" and "Purchase total" is sufficient for this. By adding further data, the analyses and findings can be further improved. Of course, we are also there to support you in the areas of data integration, data extraction and data processing.
Request presentation
of our customer experience approach.
Please feel free to contact us. We will hold a presentation and show the product at a convenient time for you.
Send us a letter to the
info@adeal-systems.com
Give us a phone call
+49 (0)6102 574 386 0
RaaS
Recommendations as a Service
Our project "AI Services for German SMEs" was funded under the Distr@l program of the state of Hesse: Validierungs-/Wachstumsförderung
Many small and medium-sized enterprises (SMEs) lack the resources and know-how for an automated analysis of their customers' behavior. However, the results of this analysis form the basis for good digital marketing and sales.
Our AI service RaaS solves this issue for SMEs. This pre-built AI service is intended for business users and is thus very different from common technical AI platforms and frameworks. The design of this service is based on an integration of multiple machine learning approaches such as customer segmentation, transaction clustering, statistical consumption analysis and recommendation engines.
The use of our recommendation service provides significant benefits for SMEs such as increasing customer loyalty, increasing marketing team productivity and improving customer service quality. The primary application group is German SMEs from the retail and e-commerce sectors.