First Look: SAP Real-Time Offer Management

May 9, 2013

in Advanced Analyitcs, Decision Management, Product News

Share

As part of the ongoing blog series on Marketing Decision Management Solutions I got an update from SAP on Real-Time Offer Management. This is based on an acquisition made at the end of 2006 of a real time recommendation technology developed for the CRM market, with early adoption mainly by the competitive service industries – Telco and FSI. With the emergence of loyalty and mobile more broadly, this technology has evolved into SAP Real-Time Offer Management and is being adopted by industries such as Retail, Consumer Products and Hi-Tech who want to engage their customers over multiple digital and social channels.

The intent behind SAP Real-Time Offer Management is to generate offers that are targeted, contextual, customized and optimized. One can analyze a lot of historical data to become intimate with customers, finding out their intentions using consumption, purchasing behavior etc. At the same time context is the core for real-time analysis, finding relevance by considering what is going on right now. Mass promotions that are neither contextual nor targeted, contextual but not targeted offers or targeted offers that are not contextual are all equally flawed – what is required is offers that are both optimized and customized to be targeted and contextual.

SAP Real-Time Offer Management solution contains several elements:

  • Multi-channel contextual recommendation engine
    Available either stand alone or as part of SAP Precision Retailing as an on-demand solution
  • Automatic learning engine that uses responses to drive next best action or offer
  • Integrated tools for end to end offer management i.e. offer design, simulation, analysis, management, integration etc.
  • Specific industry solutions and connectors to different SAP solutions, such as CRM, ERP, eBanking, etc.

SAP Real-Time Offer Management supports the classic marketing flow – define offers, feed these into the system, and then connect a customer to the optimal recommendation by combining a trigger containing real-time contextual information (agent, basket, location etc) with real-time information fetched from customer profiles and history. These could be cross-sell, up-sell, loyalty offers, next best action etc. Responses are captured and fed back into the system for reporting, self-learning and optimizing future interactions both with the specific customer and with similar customers in similar contexts.

Like all such systems this combines some offline and online activities. Offline predictive analytics are used to process large amounts of data into predictive analytic models(e.g. scan all shopping baskets to identify items that are likely to be bought together). Online context is combined with data and online predictive analytics to come up with an optimal offer or action while also capturing the response data for learning over time. SAP Real-Time Offer Management uses its own online analytic engine that learns as offers are presented and interacted with. It also allows all the constraints to be defined and other analytics to be used, either stored in the database or by calling external scoring service.

The newest version can use HANA as its database as well as pushing its self-learning processes into HANA for increased performance. It also has access to the HANA Predictive Analytics Library (reviewed here) for more analytic algorithms. This use of HANA allows more data to be accessed in real-time. Over time more of the solution will run in HANA and take advantage of the HANA infrastructure and real time performance.

SAP Real-Time Offer Management also supports offers with location – a place attribute – and this location/range can be used as part of selecting offers. This can be used, for instance, in different mobile scenarios such as on consumers’ mobile device or in mobile terminals embedded in cars and GPS devices. Finally it can also be integrated both with batch campaign allocations AND with a real-time offer approach and can balance the two for a specific moment of interaction.

For more information please consult the solution description at sap.com

Share

Previous post:

Next post: