sellVISE

sellVISE is an intuitive widget for your mobile sales environment that makes product recommendations based on modern BI technologies to suit your customers interests. Cross-selling and up-selling potentials are thereby automatically considered by the data collection of the historical activities of your customers.

Artificial intelligence engine for smart product recommendations

Artificial intelligence ​​for smart product recommendations

sellVISE consists of an integration layer (elastic.io and NiFi) as well as a holistic data repository (DataLake) and a data warehouse (DWH) based on Hadoop to save all product data (including eCommerce, ERP, retail, CRM, PIM , etc.).

Machine-Learning

sellVISE consists of an integration layer (elastic.io and NiFi) as well as a holistic DataLake and DWH based on Hadoop to be able to store all data about products (incl. eCommerce, ERP, retail, etc.)

sellVISE has several machine learning engines (developed in TensorFlow, Spark, Mllib, Elasticsearch, etc.) to be able to process data including:

 

  • Sentiment analytics: discover people opinions, emotions, and feelings about a product or service
  • Image processing: to understand visual similarity among products
  • Customer analytics and segmentation: to gain insights about the taste of each customer segment to be able to recommend suitable product for each segment
  • Product tagging and classification: marketplaces typically rely on the seller to categorize products. This will always result in some incorrect categorizations. sellVISE relies on machine learning techniques to accurately predict the appropriate tag for a given product
  • Product recommendation
SaleSphere without sellVISE integration
SaleSphere with sellVISE integration

Cutting-Edge Product Recommendation Algorithms

  • Collaborative filtering (memory based approach, clustering based approach, matrix factorization, & deep learning):
    • User-item filtering: “Users who liked this item also liked …”
    • Item-item filtering: “Users who are similar to you also liked …”
    • Hybrid
  • Content-based filtering: “find similarity among products based on the associated features that describe each product (e.g., image, text description & user reviews, product tags, etc.)
  • Hybrid-based filtering
Machine Learning with SaleSphere
Machine Learning with SaleSphere

Are you ready for the next step in sales?
Do not wait any longer, start digitization in sales now! With SaleSphere, new strategies and options are available. Do you have any questions? Contact us now.

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Machine Learning with SaleSphere

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Newsletter “Enabling Sales via Digitization”

We will inform you about the topic “digitalization in sales” and the news about SaleSphere, the sales app for modern sales and field service. First learn about updates, features, case studies and much more.

Register now and receive a welcome guide for sales enablement!