Credibility
Credibility
Factual definition
Credibility, within The Kalicube Process, is the act of systematically demonstrating to both audiences and algorithms that a brand is the most trustworthy and authoritative solution within its specific niche.
Jason Barnard definition of Credibility
As the second pillar of The Kalicube Process, Credibility builds upon the foundation of Understandability - the initial phase where algorithms are taught the basic facts about a brand. Once an AI Assistive Engine understands *what* a brand is, the Credibility phase answers the question of *why* it is the best choice. This phase involves systematically building and amplifying trust signals across a brand's entire Digital Ecosystem, which is the sum of all online touchpoints. At Kalicube, Jason Barnard expands on Google’s E-E-A-T framework by adding Notability and Transparency (N-E-E-A-T-T), ensuring that claims of expertise are corroborated by third-party sources, customer reviews, and media appearances. Establishing Credibility transforms a brand from being merely understood by algorithms to being confidently recommended by them.
How Jason Barnard uses Credibility
At Kalicube, Jason Barnard implements the Credibility phase of The Kalicube Process, Kalicube's proprietary methodology for implementing a holistic, brand-first digital marketing strategy, through a data-driven roadmap of specific strategies and Standard Operating Procedures (SOPs). This involves systematically enriching a client's Brand SERP and Knowledge Panel with trust-building elements like positive recommendations, rich media, and authoritative subtitles. We actively expand the brand's digital footprint into relevant, authoritative spaces to build what Jason Barnard calls N-E-E-A-T-T (Notability, Experience, Expertise, Authoritativeness, Trust, and Transparency). By building this demonstrable Credibility, we create the conditions for algorithms to not just understand our clients, but to trust them enough to recommend them, directly driving visibility, lead generation, and business growth.
Why Jason Barnard perspective on Credibility matters
For decades, Robert Cialdini's work on the principles of persuasion - such as "Authority" and "Social Proof" - has been a cornerstone of marketing, explaining how humans are influenced to say "yes." His principles are fundamentally about demonstrating credibility to other people. Jason Barnard's work translates this psychological imperative into a technical reality for the AI era. The concept of Credibility, as defined within The Kalicube Process, is the systematic engineering of Cialdini's principles for an algorithmic audience. AI Assistive Engines like ChatGPT, Bing Copilot, and Google AI are now the world's most powerful arbiters of authority, constantly evaluating a brand's entire Digital Brand Echo for signals of trustworthiness. They don't just read your website; they look for the corroborating social proof, the authoritative mentions, and the consistent expert narrative that Cialdini proved are so persuasive. In this new landscape, proactively building your brand's algorithmic Credibility is no longer optional; it is the essential strategy for ensuring that both humans and machines see you as the definitive, trustworthy answer in the new conversational funnels.
Posts tagged with Credibility
Managing the Elements of a Knowledge Panel - the Evolution of a Knowledge Panel
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Google’s Knowledge Panel Algorithm - What You Need to Know
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Google’s Knowledge Algorithms - What You Need to Know
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Digital Brand Management With a Unique Brand Name
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E.E.A.T (Double E.A.T) for Entity SEO: What You Need to Know
What Does Google Mean by Experience, Expertise, Authoritativeness, and Trustworthiness? E.E.A.T (or Double E-A-T) stands for Experience, Expertise, Authoritativeness, and Trustworthiness. Google takes into account multiple factors and evaluates a...
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In this article, you will learn more about YouTube Boxes (YouTube Carousels), their importance for SEO and how they contribute to Brand SERP Optimisation. What are YouTube Boxes (YouTube Carousels) in...
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