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Nobody said it was easy to attract and retain customers. Especially in today’s increasingly competitive landscape. Customers, who often rely on their favorite brands, but are open to new products and offers, can choose from a wide range of attractive suggestions within a click. 

In this context, offers need to be valuable and precise. They need to be recommended at the right time and touchpoint for each type of client and visit. We often speak about the importance of Customer Experience (CX), which today includes a series of advanced techniques, that can be baptized as the new generation of Intelligent CX, thanks to AI and decision models.   


This article will discuss these techniques and how we can make the best out of them. 


Personalizing prospect’s experience with AI   


Today we can personalize the experience of unknown visits (prospects) with champion recommendations in real-time, in all touchpoints, like digital. This is particularly relevant for banking and insurance, as the majority of consumers browse and visit different websites anonymously, to catch the best offer or coverage. 


To pioneer this recommendation or experience technique, it is important to unify prospect’s interactions and infuse them in decision models with AI in real-time. This allows us to:   


  • Predict socio-demographic attributes such as 'age', among other characteristics, for a completed and enriched prospect profile, ready to cluster in a specific audience or segmentation 


  • Identify browsing similarities with other visits or clients with learning algorithms during the prospect’s discovery phase, to generate recommendations and activate Next Best Experiences (NBX). 


Understanding which are the most relevant contents, offers, and recommendations for our prospects is important, but the key relies on the capacity to obtain and trigger them in hyper-personalized recommendations in real-time This is our competitive edge, with which our clients can increase their traffic by 40% and generate 1.7% more returns, thanks to our omnichannel customer journeys. 


Moving towards an Intelligent 360 Loyalty  


To carry out a succesful loyalty program, it is important to emphasize the influence of data quality. It is what fuels those decision engines and IA models, and the key towards providing real value to our clients.  


Data or probabilistic matching is a powerful way to improve the quality of our data. It compares and cross-checks different sources of information (like databases) where there is no direct way to cross-reference them. This allows to identify the presence of a specific client in those sources, and link their information with other service or product related data. As a result, we can spot relationships and improve our offerings and engagements with new recommendations.  


Moreover, thanks to data or probabilistic matching we can discover clients that belong to the same household. In conclusion, this technique or feature is a great ally to activate sophisticated and intelligent loyalty experiences with decision models and AI, thanks to a higher data quality. 


Real-time, ‘phygital’ and intelligent Customer Journeys  


At SAS, our decision models and AI help calculate in real-time and precisely attributes like buyer intent, propensity or churn; data outputs that we can activate in the moment through hyper-personalized recommendations in all touchpoints (web, app, customer care or cash receipt) thanks to our omnichannel and ‘phygital’ Customer Journeys, that can integrate different channels and systems into the equation. This is particularly relevant for the banking and insurance sector, as call centers, advisors, brokers or physical touchpoints remain a key part of the customer experience. 




This results in a higher retention, optimized offers and services and an increase in customer value by more than 2%. In addition, thanks to the inclusion of new channels like Facebook and Google Ads, and the creation of 1st party data segmentations, our clients obtain better returns on advertising investments (ROAS).


Let’s talk about E-commerce, Algorithms and transparency


Algorithm Recommendation Techniques (also known as AI Recommendation Engines) help analyze and predict whether a person prefers one or another product based on their profile and history.

There are different types, like the Bayesian Personalized Ranking (BPR) or Factorization Machines. These algorithms use product or item recommendations based on implicit feedback from customers (interactions, viewing time, number of transactions...) or explicit feedback (comments, ratings, scores...). A clear example is YouTube or Spotify, which make use of these techniques through our implicit feedback, to make better video and song recommendations.  


At SAS, we enable the activation of these recommendation techniques in real time, thanks to our AI and CX modules, which are based on transparency (no black boxes), openness, traceability, and responsible AI. This allows our clients to govern models and comply with audit and regulatory processes of AI, GRPD, and CX easily.


An all-in-one collaborative platform that covers the entire marketing flow: from marketing planning-, to the creation of customer journeys, segmentations and hyper-personalized experiences,-and the end-to-end measurement of the whole process.



Drive an Intelligent and Competitive CX in real-time with SAS CI360


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