Choice-Reward With (AI) “Service-Assist” For Service Networks

Rendering Choice and Reward with reasoning and inferences on a cognitive framework that address one of the growing human-level inefficiencies in-between decision-making systems and choice-making agents at various touch-points in customers’ journey

If marketing or service networks has one goal, it’s to reach end-consumers at the moments that most influence their decisions under uncertainty. People form impressions of brands and their services at their touch points such as retail stores, online, ad channels, news, conversations, and product experiences. And, decision-making system has to derive forecasts, optimization, options, assignment and knowledge diffusion in line with customer value. What happens when something triggers the impulse to buy? Those accumulated knowledge impressions – sensory, reasoning and emotional (see Cognitive Framework) – then become crucial because they shape the choice-set: the saliency of brands consumers regard at the outset as potential purchasing options

Encore

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, to gain marketplace advantages; and how would then personalize and timely organize all the content cues of the choice-set for end-consumers?
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Strake

Service operation has always been a conundrum for asset-intensive industries: while high uptime is critical to ensure return on assets, the growing human-level inefficiencies in-between decision-making systems and choice-making agents is how to draw on the knowledge of an accurate understanding of verifying service rendered that customer value, and would value, to gain marketplace advantages
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Actually, the decision-making process is a more circular journey, with ten primary phases of buying system – not necessarily in that order and, more so, depends on the product category – representing potential knowledge grounds where company-brands win or lose: pre-purchase or the process of researching potential purchases; purchase or when consumers buy brands; experience or when consumers experience; and post-purchase or when consumers forms an opinion to repeat purchase or cognitive dissonance. This metaphor does help a good deal – for example, by providing a way for machine to recognize and understand the strength of a brand compared with its competitors at different stages, highlighting the bottlenecks that stall adoption, and making it possible to focus on different aspects of the marketing challenge.

Platform-applications are AI/ML technology-enabled services for customers’ end-customers and employees. Their immediate and primary purpose is to enable enterprise knowledge for users to perform activities that create value, in line with a business’s objectives. For example, a recommendation for product contributes business value by making it easy for customers to find items on their omni-channel applications. Its effectiveness might be measured with conversion-to-sale metrics and enhanced by improvements to recommendation algorithms

Consider the range of skills needed to manage the customer experience in the consumer and service companies. These include identifying active loyalists, as well as understanding what drives that loyalty and how to harness it with word-of-mouth programs. Companies need an integrated, organization-wide “voice of the customer,” with skills from advertising to customer relations, product development, research, analytics and data management.