Network Effect of Choices and Options Rendering

In light of the modern-day consumer’s shopping spending intelligence, retailers and consumer goods companies address human-level inefficiencies on how to draw on the knowledge of an accurate understanding of what customers value, and would value, proactive reasoning in “managing the probable” and explainable inferences in “leading the possibilities” to gain marketplace advantages; and how would then personalize and timely organize all the content cues of the choice-set for end-consumers?

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:

“Give me ordered recommendations I wouldn’t have thought of myself” – One common content cue of choices is to remind shoppers and consumers of items they looked, looking and intend-to-buy. This can be annoying or intrusive if not executed well. Instead, consumers appreciate being recommended products or services that complement what they’ve already browsed or bought
“Trigger me when I’m in shopping mode” – Timing is just as important as content cues. Perfecting the timing requires a close look at consumer behaviors, context, frquency, networks, patterns, conditions, etc.
“Inform me no matter where I‘m connected with…” Customers expect interactions that seamlessly straddle offline and online experiences. This could be challenging for some because it requires collaboration between disparate store-locations of the company from physical store to online to mobile