How would omni-channel retailers and consumer goods marketers improve their conversion rate without boiling the Internet and breaking the privacy of consumers? How does end-customer define value for money? Can a decision maker measure it in order to recommend products and services? What are they actually worth to customers? How would they automate this learning of their consumers, employees, suppliers and then communicate back on what’s working versus what’s not working? How would they personalize content and what’s the appropriate content, for a given segment or micro-segment, or even a given individual?
Network-Effect of Choice Order Rendering (ENCORE) is one-of-a-kind system for next-generation retail and consumer goods companies, founded on knowledge representation, reasoning and inferencing – a foundation model ensembles – that allows decision-makers to organize signals of values and belief propagation, as content cues, in choice-set for end-consumers based on Self-Organized Cognitive Algebraic Neural Network (SCANN®) and Dynamic Algebraic Causal Subsequence (DACS®), our AI/ML learning methods, that are designed to predict omni-channel demand flow into the network; find causal reasons, propagate latent features, predict behaviors, optimize values, speed, risks and scale, generate options, allocate capacity and assign resources that render belief cues of choice-set at each phase and touchpoint in the consumers’ journey of experience.
What does Encore do?
Forecast Demand Flow and density: Designed to manage network demand flows in consumer journeys that organize choices such as pricing, promotion, assortment, etc.
Optimize Multi-layered-Multi-dimensional Latent Features for Options: Optimization of value, speed and scale through augmented approach to maximize yields, throughput, resource capacity and dynamic resource assignments
Assign Resources Dynamically and Autonomously to Various Workstations: Based on demand flow. through the platform and interfaces, optimally allocate capacity and assign resources to various workstations
Improve Quality of Decisions on Setting Choice-set For End-Consumers: Improve determining the optimal options for a set of decisions in a given business environment and business target, thus recommend consistent feasible choices for end-consumers
Strong content cues increase saliency. Patterns, knowledge and experiences are engineered in a way that reflects the reasoning, explainability and inferencing of the needs and use of consumers and employees without any boundaries, meaning they shop both online and in-store; without loyalty, meaning they try different brands, products, and retailers; without patience, meaning they won’t wait more than two or three days for delivery; without mid-tier, meaning they buy either the value offering or premium; and, without compromise on sustainability.
Workflows are orchestrated in a way that cleanly segments dimensions, latent features and attributes and phases in the consumers’ choices. Model structures reflect the economies of scale, preferences, features, capabilities, etc. to deliver the proactive, personalized service consumers want, when and how they want it – sometimes even before they know they want it – that come along with working in a more digital environment. Companies bring together capabilities from networks (e.g., digital, manufacturing, operations, etc.) to expand the predictivity and explainability in their assortment. Building digital operations is not a goal, it’s an enabler of possibilities (move into banking), experience (partnering with airlines), resilience (increased-share-of-life) – decision effectiveness that are measured.
Building a digital (AI) “service-assist” system has at least three major benefits:
