ADVISORY
Advisory Work
Where Operators
Shape Products.
Advisory roles where real GTM experience informed product direction — at companies building the infrastructure for how revenue teams operate. From Salesforce AI R&D to next-generation revenue intelligence.
Salesforce
AI Advisory Board · 2017–2019
Gong
Advisory · Coming Soon
Engagio
Advisory · Coming Soon
AI Advisory Board · 2017–2019
Salesforce
From 2017 to 2019, Erdem served on Salesforce's AI Advisory Board, contributing directly to the product R&D direction of Sales Cloud Einstein, Pardot, and the broader revenue intelligence platform. The engagement was grounded in frontline operator experience: what actually breaks in enterprise GTM, where AI creates leverage vs. noise, and how revenue teams realistically adopt new tooling.
The advisory work spanned three interconnected workstreams: mapping the complete AI use-case landscape across the revenue funnel, defining product roadmap priorities for both greenfield development and platform enhancements, and framing the theoretical foundation for predictive database marketing as a growth engine methodology.
The long-term vision articulated during this period: Salesforce evolving from a CRM platform into a deployable revenue model — with AI-native infrastructure that empowers growth-stage companies to build predictable GTM systems from the ground up via Salesforce Ventures.
21
AI Use Cases Mapped Across the Revenue Funnel
A complete framework covering Prospective Account Generation, Selling, Closing, and Post-Sales — with current-state assessments and recommended AI solution design for each stage.
2
Product Development Tracks Defined
Greenfield product proposals (Einstein chatbot for sales, account-level engagement AI, input assistant) and a detailed roadmap for Sales Cloud platform enhancements (call analytics, forecast intelligence, prospective contact object).
1
Predictive Database Marketing Framework
A foundational methodology paper connecting customer equity optimization, CAC/LTV modeling, and centralized data architecture — submitted as R&D input for Salesforce's AI-driven marketing products.
AI Use-Case Framework — Full Funnel Coverage
21 AI solutions mapped across four funnel stages, each with a current-state assessment and recommended next-state design. Submitted to Salesforce AI R&D as a structured product input.
| AI Solution | Stage | Current State / Recommendation |
|---|---|---|
| Lead Generation Management | Prospecting | Prospective target account generation with associated personas and contacts |
| Personalized Content Management | Prospecting | Page/CTA personalization and conversational chatbots with machine learning |
| Stakeholder / Person Analysis | Prospecting | ✓ Completed |
| Sales Intelligence | Prospecting | Full signal listening and proactive surveillance |
| Opportunity Identification / Scoring | Prospecting | ✓ Completed |
| Key Account Planning | Prospecting | ✓ Completed |
| Sales Appointment Setting | Prospecting | Trigger-based engagement with optimized scheduling |
| Key Account Management | Selling | AI-based retargeting at account level |
| Sales Activity Analysis | Selling | Required production estimations, production line traffic lights |
| Sales Call Analysis | Selling | Predictive topic recommender, sentiment-based scoring, keyword targeting |
| Comprehensive Needs Analysis | Selling | Product recommender engine |
| Prospect Engagement | Selling | Full content permutation with recommender for personalization at scale |
| Solution Analysis / Co-creation | Selling | ✓ Completed |
| Forecast Management / Sales Analytics | Closing | Change analytics, impact simulations, Bayesian random walk, cash-flow forecasting |
| Configuration / Pricing Management | Closing | Price simulations, dynamic value estimations |
| Win-Win Deal Negotiation | Closing | Simulation-based negotiation support |
| Opportunity Closing | Closing | ✓ Completed |
| Sales Talent Management | Post-Sales | ✓ Completed |
| Sales Coaching Support | Post-Sales | Live conversation recommender system |
| Sales Training | Post-Sales | Course recommender and gamification |
| Success Management / Monitoring | Post-Sales | Sales rep recommender system |
Product Roadmap Contributions
Short-to-long-term vision for Salesforce's AI product direction — submitted as structured R&D input across two development tracks.
Greenfield · New Products
Net-New Product Development
Einstein chatbot for sales with Pardot engagement studio-like ML flow and call recording–based learning
Account-level engagement rollups with AI-driven engagement value definitions (Sales Cloud Einstein)
Lean project management app with tags and grids — AI time-management personal assistant for reps
Sales Cloud Einstein field autofill — input assistant for reducing manual data entry friction
Platform Enhancements · Sales Cloud
Sales Cloud AI Improvements
Association rule mining and lift-based fitness recommendations for account/contact selection prior to engagement
Call data advanced use cases: sentiment labeling, topic recommender, keyword-based marketing automation, stage mapping
Forecast object with time-series, Bayesian random walk, and pre/post state change impact simulations
Prospective contact object with ETL process for account-level campaigning and campaign reportability
Short–Mid Term
Near-Term Strategic Goals
Vertical development: Functionality toward predictive six-sigma sales production
Horizontal synergies: Bringing Marketing, Sales, and CS together for account-centric, data-driven methodologies
Uniform centralized source of truth to reduce cost-to-precision in targeting and delivery
Long-Term Vision
Salesforce as a Revenue Model
Make the entire customer equity optimizable by empowering Customer 360 with AI
Create deployable solutions via Salesforce Ventures — predictable GTM infrastructure for early-stage companies
Rethink every available solution with data, AI, and analytics — build systems of systems across the revenue motion
Predictive Database Marketing for the Growth Engine
A theoretical and applied framework — submitted to Salesforce AI R&D — for using customer data, predictive modeling, and AI to optimize every stage of revenue generation from targeting through retention.
Customer Intimacy
Know customers on an intimate basis. Customize products, pricing, and communications to individual behavior and predicted future value — not segment averages.
Operational Efficiency
Use CRM and AI to reduce cost-to-serve and utilize non-marketing resources efficiently. Automation should amplify human judgment, not replace it prematurely.
Marketing Efficiency
Use customer data to improve marketing productivity — less churn, more cross-sell, greater customer profitability. Database marketing is fundamentally a segmentation and targeting tool.
"One of the necessities of the growth engine is a uniform centralized source of truth — which naturally diminishes the cost to achieve precision by making data points associated with targeting, delivery, and accounting all comparable and available at one single location. Uniformity of strategy, process, people, and technology is the core of the methodology."
Erdem Tokmakoglu — Predictive Database Marketing for the Growth Engine, submitted to Salesforce AI Advisory Board, 2017–2019
Advisory · Coming Soon
Go
Revenue Intelligence · Conversation Analytics
Gong
Advisory engagement with Gong — the revenue intelligence platform that captures and analyzes every customer interaction to drive predictable growth. Details and content coming soon.
Content in progress — check back soon
Advisory · Coming Soon
En
Account-Based Marketing · B2B Orchestration
Engagio
Advisory engagement with Engagio — the account-based marketing and sales platform that enables B2B revenue teams to orchestrate go-to-market motions across accounts and personas. Details and content coming soon.
Content in progress — check back soon
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