REAL-TIME INTERACTION

Interaction Execution

Monitor customer activities, update and enrich profiles, and interact with relevant offers. All at the cadence of the customer. Audience, channel, content and offers are selected at the moment of interaction. Which means every moment of your CX is relevant and timely.

The rg1 Difference

Customers take the expectations set by their best experience – whether it be with your brand or another one – and apply them everywhere else. With rg1’s real-time capabilities, you can be the one to set the bar, delivering relevant offers in real time for customers and real revenue increases for your company.

The heartbeat of real time

Build real-time customer experiences using a high-performance scalable engine – the real-time “traffic cop” – to guide interactions and orchestrate decisions, all at customer cadence.

Cross-channel monitoring

Engage customers on any channel with decisions, images, product recommendations, offers, and messages that work wherever the customer is.

Open access

Use APIs in the real-time engine to orchestrate decisions and interactions across systems, making real-time CX available in any enterprise application or touchpoint.

Real-Time Execution

rg1’s execution uniquely empowers brands to use their long-tail customer profiles with high levels of security and millisecond response times to drive the most innovative customer experiences.

Keep the data and decisions on the server side of your technology walls for interacting with customers, protecting data privacy

Whether hitting small millisecond windows for personalization or fitting within a cross-channel journey window, all interactions are personalized at the customer cadence.

rg1’s execution is scalable to take into account a deep understanding of each customer – or long-tail customer records – at the point of personalization.

Trusted by Today's Top Brands

See How It Works

A Real-Time Example

1

The rg1 real-time decision engine calls an machine learning model to provide next best action/offer insight

2

Insights are used along with predefined Smart Assets to make a decision and select raw content

3

Content is passed through an AI model to format it based on context and to personalize to the specific customer

4

The offer is presented via an API and shown to the customer in a mobile app

5

Customer responses to offers and content are captured and fed back into the model engine to continually improve model outcomes

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