Next Best Action and real-time orchestration: How decision AI is transforming customer engagement

Next Best Action marketing is now one of the most strategic approaches for optimizing real-time customer engagement. In a world where companies are trying to massively personalize customer interactions, the ability to deliver the right recommendation, at the right time, through the right channel has become a decisive competitive advantage. Real-time CDP orchestration enables retail and e-commerce brands to move beyond the limits of traditional batch approaches and deliver personalized experiences powered by decision AI.

Table of contents

• Real-time orchestration: a new paradigm in digital marketing
• Next Best Action: from theory to practical implementation
• Decision AI technologies: intelligent decision engines
• Technical architecture for omnichannel orchestration
• Implementation and strategic support

Real-time orchestration: a new paradigm in digital marketing

Traditional marketing orchestration, based on daily or weekly batch processing, no longer meets the need for instantaneity in modern customer journeys. Consumers now interact across multiple channels and expect a seamless, immediately consistent experience at every touchpoint.

This shift is driven by several structural factors. The multiplication of digital and physical touchpoints creates complex, non-linear customer journeys. Decision cycles are accelerating, especially in retail and e-commerce, where immediacy is becoming the norm. Expectations around personalization are rising: every interaction needs to be tailored to the individual’s context and preferences.

Real-time orchestration turns this complexity into a business opportunity. Scal-e has developed a differentiated approach with a massively distributed architecture that processes customer events and adapts relationship strategies based on micro-behavioral signals: cart abandonment, product views, cross-channel interactions, and transaction history.

Scal-e’s Customer Journey Orchestration module is a good illustration of this approach. It allows you to design dynamic journeys that continuously adjust according to customer interactions. The drag-and-drop interface lets marketing teams configure complex scenarios without technical intervention. For example, when a visitor views a product page, this can automatically trigger a personalized sequence that factors in their past preferences and business constraints.

This real-time orchestration is powered by Scal-e’s natively integrated CDP, which centralizes all customer data from online and offline sources. This unified view makes it possible to orchestrate consistent experiences across more than 30 natively integrated activation channels, from email and SMS to push notifications and web interactions.

The competitive edge lies in the ability to capture the “moments of truth” in the customer journey—those micro-windows where purchase intent or engagement is at its peak. Intelligent orchestration enables immediate responses to these signals, proposing the most relevant next action based on pre-configured decision rules.

Next Best Action: from theory to practical implementation

Next Best Action (NBA) goes far beyond traditional product recommendation algorithms. It is a global decisioning system that determines, for each customer, the optimal relationship action by factoring in all business constraints and value opportunities.

Scal-e provides a complete infrastructure for implementing NBA strategies, enabling the integration of custom-built AI decision models developed by specialized partners. This hybrid model combines Scal-e’s easy-to-use no-code environment with the power of advanced predictive algorithms tailored to each organization’s specific needs.

In practice, implementation rests on three key technical components, as documented in client projects. The CVM datamart centralizes all customer data in real time: profiles, transactions, behaviors, cross-channel interactions. The scoring module calculates predictive indicators directly from the Scal-e interface in a no-code mode. Partner AI decision models then select the optimal action by combining these predictive scores with business rules.

A concrete use case illustrates this orchestration within a large services company. The Scal-e platform hosts a self-learning decision AI model, developed by an expert AI partner, which instantly analyzes customer history, recent behavior, and commercial context. Decision rules are easily configured in the Scal-e interface: defining target populations, selecting suitable offers, setting up campaign scenarios, and choosing the best communication channels.

This no-code approach is a major advantage for marketing teams. Scal-e’s drag-and-drop interface allows business users to configure decision strategies without IT involvement. Business rules are defined intuitively using the Population, Segment, and Score modules, which provide a visual editor based on set theory: unions, intersections, and exclusions of populations.

The intelligence of the system lies in the machine learning capabilities of the partner models. Every customer interaction feeds the predictive algorithms integrated into the platform, continuously refining recommendation relevance. The model performance dashboard enables business teams to understand the choices made by the AI algorithms and make the necessary iterations to optimize results.

Decision AI technologies: intelligent decision engines

Decision AI differs from classic predictive AI by its ability to turn insights into operational actions. While many solutions stop at providing propensity scores, Scal-e is building a partner ecosystem that connects specialized AI decision engines to automate marketing decision-making.

This approach is based on integrating external AI tools through the Scal-e infrastructure. Documented projects illustrate this strategy: data science partners develop bespoke AI decision models for complex CVM needs, while other partners bring in decision engines for cross-channel orchestration. These technologies connect to the Scal-e platform via APIs: Scal-e sends customer data, the external tools perform their calculations, and send back recommended actions.

Using the outputs from these partner models, the Scal-e platform can orchestrate several decision dimensions at once. The customer dimension assesses product affinity, channel preferences, and optimal interaction timing. The business dimension accounts for commercial objectives, stock constraints, and product margins. The contextual dimension analyzes seasonality, external events, and market trends.

This multi-dimensional orchestration produces real-time, actionable recommendations. Partner AI tools can, for instance, automatically propose relevant alternatives when a product is out of stock, adapt messaging to the customer profile, and orchestrate the optimal journey across the more than 30 channels natively integrated with Scal-e.

Today, Scal-e offers two implementation approaches depending on an organization’s data maturity. The score calculation approach in no-code mode allows marketing teams to create predictive indicators through Scal-e’s native drag-and-drop interface. The external AI integration approach enables the use of dedicated algorithms developed by specialized partners to tackle complex business challenges.

In parallel, Scal-e is developing its own native AI engine, which will allow teams to configure and audit decision models directly from the Scal-e interface. This will offer an alternative to external solutions while preserving transparency and business control.

This flexibility is a competitive differentiator compared to the many “black box” solutions on the market. Business teams maintain control over the rules via the Scal-e interface, while benefiting from the power of partner AI algorithms or, in the future, the native AI engine under development. This transparency encourages user adoption and ensures alignment with commercial objectives.

Technical architecture for omnichannel orchestration

Real-time orchestration requires a robust technical architecture capable of handling large data volumes with maximum availability. Scal-e’s approach is based on a massively distributed architecture that optimizes performance and scalability for large enterprises.

The SaaS technology stack is structured around several complementary layers. The ingestion layer collects and normalizes multi-source data via APIs, native connectors, and ETL. The distributed architecture automatically rebalances load between servers. The activation layer orchestrates cross-channel campaigns through integrated connectors and APIs to external AI tools.

This cloud-native architecture delivers several documented structural advantages. Technical scalability allows the infrastructure to adapt to client contexts, as demonstrated in major telecom accounts managing multi-million-customer datasets. A single version of the codebase enables corrective and evolutionary updates with no impact on clients. Energy efficiency optimizes the carbon footprint, with external Greenly audits showing Scal-e’s environmental impact to be 20 times lower than competing solutions.

System integration is a critical success factor for omnichannel orchestration. Scal-e offers more than 30 natively integrated channels, enabling NBA recommendations to be activated across all customer touchpoints. If a specific channel is not available natively, Scal-e can either develop it during the project or connect to an existing market solution via its flexible integration capabilities.

The centralized CVM datamart unifies all customer data: profiles, transactions, interactions, and behaviors. This 360° view powers the integrated no-code predictive models and connections to external AI tools, ensuring consistency across cross-channel experiences. Real-time APIs allow external systems and partner AI tools to instantly query customer data to personalize interactions, as documented in e-commerce platform integrations and other third-party systems.

Orchestration is based on an event-driven logic that processes every micro-signal from the customer. Product views, add-to-cart events, abandonment, email opens, and clicks: each event can trigger evaluation of the next best action, either via internal no-code scores or through connections to external AI tools. This responsiveness turns static customer journeys into dynamic experiences that adapt to emerging intent.

High-performance capability is a major technical differentiator. The Scal-e platform has proven its ability to handle multi-million-customer volumes in large enterprise contexts, ensuring that real-time orchestration is feasible even for leading retailers, while maintaining smooth connections with the most sophisticated AI partner tools.

Implementation and strategic support

The success of a Next Best Action project depends as much on methodology and team enablement as on technology. Scal-e has developed a structured project approach that ensures user adoption and achievement of business objectives, including coordination with specialized AI partners.

Client support is a core pillar of the Scal-e offering. As part of the standard subscription, support includes a dedicated Customer Success Manager who guides teams on best practices and helps them implement their strategy within the platform. This tailored approach ensures continuous optimization of decision parameters and ongoing evolution of rules in line with model performance feedback.

Business teams can test different decision strategies, measure the impact of AI models via the performance dashboard, and fine-tune parameters based on observed results. This learning-by-doing approach ensures optimal alignment between technology and business goals.

The solution’s scalability is a major asset for growing organizations. The Scal-e roadmap, driven by client feedback and external expert analysis (CDP Institute, Gartner, Forrester), regularly delivers new features.

Scal-e’s recognized expertise in the CDP space is a strong guarantee for real-time orchestration projects. As the first French vendor to be certified as a Real CDP by the CDP Institute, Scal-e is also cited by Forrester in its Wave reports as one of the most innovative Marketing Cloud platforms worldwide, particularly for its ability to integrate decision AI through its partner ecosystem.

Conclusion: the future of real-time customer engagement

Next Best Action and real-time orchestration are the natural evolution of digital marketing toward greater intelligence and personalization. Companies that master these technologies are building a sustainable competitive advantage in a market where consumers are increasingly demanding around experience.

Scal-e offers a unique combination of no-code infrastructure power, specialized AI integration capabilities, and technical performance. Its natively integrated CDP, massively distributed architecture, and AI partner ecosystem form the three pillars of a differentiated solution in a market dominated by less flexible global players.

The urgency is real for retail and e-commerce companies: real-time personalization is rapidly becoming a standard customer expectation. Those that fail to master intelligent orchestration risk losing relevance against more technologically agile competitors.

Discover how Scal-e can transform your customer engagement strategy with a personalized demo of our real-time orchestration platform and decision AI integration capabilities.

FAQ

How is Next Best Action different from traditional product recommendations?

NBA incorporates all business dimensions—stock, margin, objectives, marketing pressure—via sophisticated decision models, whereas classic recommendations focus only on product affinity. NBA determines the overall optimal action: product, channel, timing, and personalized message.

How does Scal-e ensure performance with large data volumes and complex AI models?

Scal-e’s massively distributed architecture automatically rebalances load across servers. The platform has proven its ability to manage multi-million-customer volumes while hosting partner AI models in large enterprise environments.

Can we use our own AI models with Scal-e?

Yes. Scal-e offers two levels: no-code score calculations for standard needs, and integration of bespoke AI decision models developed by specialized partners based on each organization’s specific requirements.

What type of support does Scal-e provide to coordinate AI partners?

Scal-e includes training, ongoing support, and a dedicated Customer Success Manager who coordinates the entire ecosystem. Support covers strategy, configuration, integration of partner models, and optimization to ensure project success.