Business

Techniques That Helped Jeremy Miner Jump From $0 to $2.4 Million

Published

6 years ago

July 10, 2020

When Jeremy Miner, the CEO of 7th Level Communications, first started out in sales, he noticed something: nothing he was doing was working (similar to many salespeople’s first experiences). He, like us, had been taught many techniques from the old sales model and from so-called ‘sales gurus,’ but he wasn’t achieving the six figures a year that they said he could make following their techniques. At the same time, he was in college studying Behavioral Science and Human Psychology, and he was struck by how what he was learning about the human brain contrasted from what he had been taught in sales. He was studying how the brain makes decisions and how people are persuaded to do something. It was the complete opposite of the traditional selling techniques.

“I knew I wanted to succeed in sales. To do so, I knew I needed to take a giant leap outside my comfort zone. Following the status quo wasn’t going to work,” Miner said. “So, rather than just listening to the methods I had been taught, I decided I’d go in search of another sales training program with the behavioral science elements of sales. I searched… invested in many training courses… attended many events… and read many books. But none of them had the questions that I needed to ask to get my prospects to persuade themselves in a step by step sequence rooted in human psychology.”

So… he created it himself. “You may think that would’ve been easy, since I was studying behavioral science in school. Far from it! But as I continued my trial and error process, I eventually got to a place where I mastered the series of questions that I now call ‘Neuro-Emotional Persuasion Questions’ (more on these soon). And, the year I finally felt I had mastered it, I ended up making $2,370,485 dollars in the year in straight commission as a W-2 sales rep.”

Techniques That Took Jeremy Miner to $2.4 Million

Miner now teaches students around the world how to practice the new mode of selling, which means ditching the traditional model. He goes in depth into each of the neuro-emotional persuasion questions in his course. These questions are intended to help the prospect convince themselves that they need what you’re selling.

Asking questions more than presenting. “I now tell my students that prospects should be the ones talking for about 80 percent of the conversation. To guide this, ask questions. “Engage, don’t tell” is one of the three main forms of communication that I teach in the new model of selling. The “Old Model’ of Selling DOES ask some questions. But, 99 percent of salespeople don’t ask the ‘right’ questions at the right time in the conversation. They just ask ‘surface’ questions which only get you the superficial answers from your potential customers.

Rather, it’s critical to ask specific, skilled questions that bring out emotion from your prospects on what their problems are doing to them. These could be what I call ‘problem awareness’ questions where you ask what problems they have, and how they’re affecting them. These are followed by ‘solution awareness’ questions, where you ask what they have done in the past about solving their problems, what has worked, and what hasn’t, which helps them view you more as a trusted authority who is there to help them, and not just sell to them,” Miner said.

Helping the prospect recognize the consequences of not solving their problem. “Another type of NEPQ question that is particularly effective is what I call ‘consequence questions.’ Once you have established what the problem is and what the solution could be, it’s important that the prospect states out loud the consequences of not resolving their problem. In other words, they hear in their own voice what would happen if they don’t solve the problem (buy your solution) — what they’d be missing out on. Perhaps this would be lost social media exposure if they don’t purchase your social media organic reach service, or they lose a sense of safety if they don’t immediately purchase your security device system,” said Miner.

When they are the ones to say it out loud, they’re more likely to persuade themselves. Contrast this with if you filled in the blanks for them and said, “You’ll lose social media exposure if you don’t purchase this today.” The fact that you were the one to say it totally changes the effectiveness of the statement. Even if that’s completely true and they believe it, too, they don’t want to hear you tell them — they’ll likely get defensive and get off the call.

Engaging and discovering in a helpful conversation.

So, it shouldn’t just be following a script or giving a pitch, but it shouldn’t just be asking questions, either. Rather, the best sales conversations work in a banter between salesperson and prospect. I call this ‘learning and discovering from each other.’ Imagine this like you’d talk with a friend who you had no intention of selling to. You ask your friend how business is going, and they complain about something related to what your business solves. So, you ask some more questions to understand more, then mention what you do. The equal playing field is your mutual curiosity to hear what the other has to say.

It shouldn’t be any different in a sales conversation. It shouldn’t be you shoving your product pitch down a prospect’s throat. That’s simply not what they want, and a great way to lose a potential sale.

To learn about Miner’s exact NEPQ process, visit his website: 7thlevelhq.com.

Business

AI in Asset Management Explained: How Leading Firms Apply It

Published

2 days ago

April 16, 2026

Matthew Wiles

AI in asset management explained at its most basic level is this: using machine learning, data modeling, and automation to make faster and more accurate investment decisions. The applications vary widely across asset classes, fund strategies, and operational functions. Understanding where AI creates real value separates productive adoption from expensive experimentation.

Asset managers now face a data environment far larger than any human team can process manually. Market signals, company filings, macroeconomic indicators, alternative data sources, and portfolio monitoring all generate information continuously. AI tools process that information at scale. They surface patterns that traditional analysis would miss or find too late.

AI in Asset Management Explained Across Core Investment Functions

AI delivers the most measurable results when applied to specific investment functions rather than deployed as a general capability. The clearest applications sit in portfolio construction, risk management, and credit analysis.

Portfolio Construction and Factor Modeling With AI

Traditional portfolio construction relies on return and correlation assumptions built from historical data. AI-driven portfolio tools go further. They process real-time market data, alternative signals, and macroeconomic inputs simultaneously. This surfaces factor exposures that static models miss.

Machine learning models in portfolio construction can:

Identify non-linear relationships between asset classes that correlation matrices do not capture
Adjust factor weightings dynamically as market conditions shift rather than on a quarterly rebalancing schedule
Flag concentration risks before they appear in standard risk reports
Model tail scenarios using a broader range of historical stress periods than traditional value-at-risk models allow

James Zenni, founder and CEO of ZCG with over 30 years of capital markets experience, has built the platform’s investment approach around the principle that better data and faster analysis produce better outcomes. That view shapes how AI capabilities get deployed across ZCG’s private equity, credit, and direct lending strategies.

Credit Analysis and Private Markets AI Applications

Credit analysis in private markets has historically depended on periodic financial reporting and relationship-based deal intelligence. AI changes that model. Lenders using machine learning tools now monitor borrower health continuously rather than waiting for quarterly covenant tests.

Specific credit applications include:

Cash flow pattern analysis that identifies revenue deterioration weeks before it shows up in reported financials
Supplier and customer relationship mapping that flags single-source dependencies and concentration risks
Covenant monitoring automation that tracks hundreds of credit agreements simultaneously and alerts teams to early warning signs
Loan pricing models that incorporate current market spread data and comparable transaction history

These capabilities compress the time between identifying a problem and taking action. In credit, that time advantage directly affects loss rates and recovery outcomes.

AI in Asset Management Explained Through Risk and Compliance Applications

Risk management and regulatory compliance represent two of the highest-value AI applications in asset management. Both functions involve processing large volumes of structured and unstructured data under time pressure.

How AI Transforms Risk Monitoring in Asset Management

Traditional risk monitoring produces reports at set intervals. AI-powered risk systems run continuously. They flag anomalies in position data and monitor correlated exposures across a portfolio. Alerts fire when market conditions shift beyond defined thresholds.

The practical risk management applications include:

Real-time portfolio stress testing against live market inputs rather than end-of-day snapshots
Liquidity modeling that accounts for position size relative to market depth across multiple scenarios
Counterparty exposure monitoring that aggregates risk across instruments, custodians, and trading relationships
Regulatory reporting automation that reduces manual preparation time and lowers the risk of filing errors

ZCG applies these capabilities across its approximately $8 billion in AUM. The platform was founded 20 years ago. It built its investment infrastructure around systematic data analysis and operational discipline.

AI for Operational Efficiency in Asset Management Firms

Beyond investment decisions, AI delivers significant value in fund operations. Back-office functions like reconciliation, reporting, and compliance documentation consume substantial resources at most asset management firms.

AI tools applied to fund operations include document processing systems. These extract and verify data from offering documents, side letters, and subscription agreements automatically. Reconciliation tools flag breaks between custodian records and internal systems automatically. Investor reporting platforms generate customized materials from structured data inputs, reducing the manual production time significantly.

ZCG Consulting (“ZCGC”) advises operating companies across more than a dozen sectors on operational improvement programs, including technology-driven process redesign. Those operational efficiency principles translate directly to asset management back-office functions.

Applying AI to Asset Management: Limitations Firms Must Address

AI in asset management explained fully must include the limitations. Models trained on historical data perform poorly when market regimes change. Overfitting produces tools that work in backtests but fail in live environments. And AI outputs require experienced interpretation to avoid acting on statistically significant but economically meaningless signals.

The ZCG Team approaches AI adoption with the same discipline it applies to investment underwriting. Every tool requires a defined use case and a measurable success metric. A review process keeps experienced judgment in the decision chain. That framework prevents the common failure mode where AI adoption generates activity without improving outcomes.

Firms that treat AI as a capability layer on top of sound investment processes generate sustainable advantages. Those that treat AI as a replacement for process discipline find the technology amplifies existing weaknesses. It rarely corrects them.