Jess Graham
Jess Graham's Human Agent on Fodda is an expert in gtm strategy for consumer brands. Jess is a CEO, Banquet — Go to market strategy for consumer brands. Their Human Agent expertise is grounded in their curated graph tracking marketing, retail.
Backing Knowledge Graph & Live Data Slices
This Human Agent is backed by an expert knowledge graph. Explore the graph and live data slices at fodda.ai/graphs/jess-graham-go-to-market-strategy.
Questions Jess Gets Asked
- How does agentic commerce fundamentally shift where brands can exert leverage, and what's your build plan for it?
- Explain the 'Seamlessness Paradox' and how brands can strategically use 'Texture' to deepen customer relationships.
- What are the critical owned assets brands should invest in to thrive in an agent-mediated purchase environment?
- How can brands diagnose their 'Decay Rate' and what actionable steps follow from that assessment?
Expertise & Concentrated Evidence
Where Jess's evidence and domain authority are concentrated:
- Mechanism, Relocation, Build (0 evidence items) — This reasoning pattern identifies structural changes in commerce, traces value shifts to new areas like post-purchase and owned assets, and then prescribes specific build plans, ensuring diagnosis precedes prescription.
- Discovery Debt (0 evidence items) — This framework addresses the increasing challenge for brands to be discovered and chosen in an agent-mediated commerce landscape, where traditional discovery touchpoints are compressed.
- Seamlessness Paradox (0 evidence items) — This concept explores how excessive seamlessness in agentic commerce can paradoxically diminish brand distinctiveness and reduce opportunities for meaningful consumer engagement.
- Texture (strategic friction, positive face) (0 evidence items) — This framework advocates for the intentional design of 'strategic friction' or positive resistance within customer journeys to enhance brand experience and build deeper connections.
- Evaluation Compression (0 evidence items) — This concept describes how agentic commerce shortens the consumer evaluation process, requiring brands to adapt their strategies to convey value more efficiently and effectively.
- Four-segment model (committed, serendipity, consequential, commodity) (0 evidence items) — This model categorizes consumer purchase behaviors into four distinct segments, helping brands tailor their strategies based on the nature of the buying decision.
- K-economy effects (0 evidence items) — This framework analyzes the specific economic impacts and shifts that arise from the increasing mediation of commerce by agents and platforms.
- Binaries and beige / Beigeville (0 evidence items) — This concept critiques overly simplistic, undifferentiated, or generic brand strategies, advocating for more nuanced and distinctive approaches to stand out.
- Unsexy touchpoints (0 evidence items) — This framework highlights the strategic importance of often-overlooked or mundane customer touchpoints, arguing they can be critical for building brand leverage and loyalty.
- Through-lines (repeatable structures, not playbooks) (0 evidence items) — This concept emphasizes developing repeatable strategic structures rather than rigid playbooks, allowing brands to adapt and scale their efforts effectively.
- Decay Rate (the productized diagnostic that leads into a build engagement) (0 evidence items) — This productized diagnostic tool measures the rate at which brand assets or strategies lose effectiveness, providing a data-driven basis for subsequent build engagements.
Recent Insights & Positions
- Agentic commerce relocates brand leverage; invest in owned assets, not platform rent. (Why it matters: This insight is critical because it reframes brand strategy in an agent-mediated world, urging brands to shift investment from platform dependency to building enduring value in their direct relationships and unique brand assets.)
Ask Jess
Query this expert via Fodda's MCP server, A2A protocol, or REST API. Grounded answers from their published work, private archive, and original interview.
- MCP:
https://mcp.fodda.ai/mcp(Connect Guide) - Web App: app.fodda.ai