prudsys RDE Modules

Business Cases

Applications for this module:

Product recommendations

Content recommendations

Shop personalisation

Licence

RDE | Recommendations module available as:

  • In-house server version
  • Hosted server version
  • SaaS version
  • OEM version

Overview licence models
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RDE | Recommendations

prudsys RDE | Recommendations

prudsys > prudsys RDE > RDE | Recommendations

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prudsys RDE | Recommendation

The prudsys RDE | Recommendations module enables the optimum use of cross- and up-selling potential in e-commerce, telemarketing and high street shopping. It generates recommendations for appropriate products and content based on actual user behavior. This ensures a high level of personal relevance.

Your benefit: A high acceptance rate for recommendations, longer browsing time in the shop and increasing turnover

Business scenarios

The prudsys RDE | Recommendations module enables comprehensive optimisation and sales process personalisation at all customer touchpoints, without manual intervention. The range of solutions covers product recommendations, the personalisation of content in online shops and newsletters, social search and the personalised ranking of product lists (e.g. search results pages). So, for example, the module will analyse enquiry sequences to show the order in which products are usually sold and the order in which content is usually viewed.
With the real-time learning function, the module uses the ongoing interaction with the user to learn about user behavior and uses this information to make recommendatiosn in real-time.

How it works

The RDE | Recommendations module bases its recommendations on an evaluation of historic transaction data and on real-time learning from the ongoing interaction with users and visitors.

Historic data is not absolutely necessary but it has the advantage that it improves the quality of recommendations. If information about previous transactions is available, the module will not only tell you what products were viewed or sold together but it will also indicate other deciding factors in a purchase.

Advantages

  • Recommendations from real-time learning of user behavior
  • Optimum use of cross- and up-selling potential
  • No down-buying and no top seller problems
  • Fully automatic 'Install-and-Forget' procedure
  • High-value recommendations even for small shopping baskets, in long tail and for new products or content
  • Includes environmental factors (e.g. channel, time, weather)

Key features prudsys RDE | Recommendations

Applied algorithms:        
Number of algorithms 34, e.g.

  • Shopping basket analysis cs cf. Amazon Item2Item Collaborative Filtering
  • Sequence analysis
  • Sequential shopping basket analysis
  • Reinforcement learning

Integrated performance measurement:

  • Supports A/B testing and multivariate tests
  • Any number of possible control groups
  • 205 statistical parameters (e.g. clicks, conversion rate, total sales, turnover through recommendations)

Output channels:

  • Internet (e.g. e-commerce, content providers)
  • Call center ( e.g. indication for call center agents)
  • Stationary trade/high street shopping (e.g. checkout, sales display, terminal or scale)

 

Applications:

  • Product recommendations (e.g. on category pages, product detail pages, shopping cart view pages, order completion pages, personalized shop areas)
  • Optimisation search results (e.g. sorting of search results by relevance, view recommendations based on search query)
  • Content recommendations (e.g. banner management)
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