Known as Real-Time Engagement Manager or Management, RTIM uses real-time customer interactions, predictive modeling, and machine learning to deliver consistent, personalized customer experiences across channels. Many users see RTIM as the fastest route to business value because it gives marketers the ability to instantly see critical moments throughout the shopping experience. Marketing teams are increasingly relying on predictive analytics, AI and real-time decisions to maximize customer satisfaction and engagement, personalize offers, and align shoppers' behavior with business goals. They collaborate with CIO organizations to integrate data, streamline processes, use all analytics approaches, and even reshape entire business models to enhance the customer experience.
Forrester Research analyst Rob Brosman coined the term Real-Time Engagement Management in 2012, and three years later, another Forrester analyst, Rusty Warner, proposed the following formal definition: “Enterprise marketing technology that delivers contextually relevant experiences, value, and benefit at an appropriate moment in the customer's lifecycle through preferred customer touchpoints.” Warner also outlined the basic elements of an RTIM system:
Recognize customers across channels and devices and manage content delivery (including call centers and customer service, physical workplaces and digital distribution points)
Understanding the current context in conjunction with a detailed customer history (including highly variable conditions)
Determine the appropriate action, offer or message. This can best be done with predictive analysis combined with business rules (for example: if recommended, it should be in stock) (this customer is most interested in the next offer)
Capturing interaction data for measurement and optimization
Due to the proliferation of digital marketing, RTIM has evolved because an RTIM system is ideally suited to the next best action or next best offer, geo-proximity marketing, e-commerce advice, ad targeting and retargeting, call center decision management and personalization (email, websites, mobile apps, social media).
Forrester identified three key capabilities for RTIM: speed and agility, data-driven personalization, and cross-channel optimization.
It is the ability to send relevant messages the moment the customer engages or interacts.
The ability to integrate online and offline touchpoints and automate messages that treat each consumer as a segment of someone.
To identify ideal strategies and continuously improve engagement, to strengthen machine learning to run A/B/multivariate tests on a regular basis.
Single Customer Image: It combines inbound, real-time customer interactions with offline data.
Improve Marketing Performance: Makes real-time, context-based decisions for the next best bid to increase response rates.
Consistent Personalized Cross-Channel Experience: Stronger holistic channel designs and executes customer journeys.
Flexible and Scalable Placement: It can be deployed in the cloud, on-premises, or hybrid modes; it's installed to scale seamlessly.
Present the Best Offer for Each Interaction: It provides contextually relevant content for each client, based on real-time interactions.
GDPR Compliance: Complies with the General Data Protection Regulations (GDPR).
Real-Time Insights: The real-time decision engine determines the next best action based on detailed, customer insights at the point of interaction.
Simulation Insights: Performs simulation tests to understand potential outcomes and predict how a campaign will work with customers.
Machine Learning Capabilities: The self-learning environment models possible campaign responses with real-time interactions for optimal message placement.
Communication Optimization: Integrates online reactions and campaign data to uncover insights that can increase campaign impact.
Business Rules Engine: It is an easy way to create rules from the simplest to the most complex.
Reporting and Analysis: Understands the impact of the campaign using interactive reporting and analysis tables.
Data Latency is the ability to load and update data in near-real time, while supporting query workloads at the same time.
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The word encode has meanings such as “encoding”, "encryption" or “symbolization” and basically involves the conversion of signals and data according to certain rules.
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