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Common base platform and capabilities underpinning both Sift Engage and Opolo.

Streaming ETL
  • Centralize data ingestion from batch and/or streaming data sources.

  • Reduce time to market with ready to use industry specific data models.

  • Ingest data via any of the supported protocols then filter, transform and distribute data either into the Real Time Customer Data Mart or distribute to external data stores.

  • Out of the box data ingest adapters​

  • Inherint "data provenance" to provide a trail of each piece of handled.

Real Time Customer Data Mart

  • Maintains the real time state of customer attributes within an in-memory NoSQL database.

  • As customers interact with your business and use your products and services all of the related data is captured, processed and transformed into meaningful attributes which collectively form the 360 degree view of the customer. 

  • As data is received, the customer state for the related customer will automatically update in real time.

  • The customer state is used to drive customer engagement decisions and can also be retrieved from any 3rd party application via RESTful API.


Real Time Audience / Segmentation

  • Build segments with ease that automatically update in real time.

  • Customers will automatically move in and out of segments as their customer profile / attributes within the real time data mart update.

  • Segments can be defined using data within the real time data mart (including by location) or by pulling data from external data stores or by a combination of both.

Integration & Orchestration Hub

  • Manage connections between Sift and 3rd party systems via central Kafka integration and orchestration hub.

  • Multiple out of the box adapters designed to support almost any:

    • push channel

    • pull channel

    • customer inquiry

    • bonus / reward fulfillment

    • service provisioning

    • service alarms

    • case creation

    • etc...

Real Time Geo-Fencing

Leverage real time location for event triggers location specific audiences or analytics.

Supports location data feeds from any data feed and not just mobile SDK data.  Identify customer location through attachment as they attach to your wifi network, use one of your ATM's, interact with a kiosk, or use your own mobile network.

Easily define geo-fences with Google Maps.

Real Time Analytics

Complement your existing analytics capabilities by deploying analytical models for real time execution.

Execute any R, Python or PMML model in real time leveraging real time data within the Knowesis platform.

Real Time Complex Event Processing (CEP)

Proactively engage customers when defined situations occur.  

Situations that indicate opportunities or risks can be configured as real time events and trigger as soon as the condition is true.  The events can be used to initiate a customer engagement strategy or simply for analytics purposes.

Real Time Event Insights

Monitor the number of events triggering in real time.

Use event triggers and live insights to aid pre-analysis when data attributes are not readily available elsewhere.


Component Architecture

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