Timeless Wisdom from Warren Buffett and Charlie Munger

I recently came across a Twitter handle and found these videos featuring Warren Buffett and Charlie Munger.

Pure gold. Essential listening.

https://x.com/i/status/2012503133315068277
https://x.com/i/status/2012113355738411215

My Plan for 2026

2026 Plan

As my New year resolution, I plan to focus on reading, certifications and Developing AI agentic app in year 2026. 

Reading List (Finish 10 from this list below)

I. Mental models and decision discipline; reset cognition

1. The Great Mental Models; Shane Parrish

2. Thinking in Bets; Annie Duke

3. Everything Is Obvious; Duncan J. Watts

II. Economics, incentives, and money behavior

4. The Psychology of Money; Morgan Housel

5. Prediction Machines; Ajay Agrawal

6. Power and Prediction; Ajay Agrawal

III. AI macro forces and geopolitics

7. AI Superpowers; Kai-Fu Lee

8. The Coming Wave; Mustafa Suleyman

IV. AI inside the enterprise; work re-architecture

9. Human + Machine; Paul Daugherty

10. Co-Intelligence; Ethan Mollick

11. Agentic Artificial Intelligence; Pascal Bornet

V. Strategy, disruption, and platforms

12. The Innovator’s Dilemma; Clayton M. Christensen

13. Competing in the Age of AI; Marco Iansiti

14. Platform Revolution; Sangeet Paul Choudary

VI. Building and scaling organizations

15. Innovation and Entrepreneurship; Peter F. Drucker

16. Built to Last; Jim Collins, Jerry I. Porras

17. Zero to Scale; Arindam Paul

VII. Execution, leadership, and culture

18. Measure What Matters; John Doerr

19. A Sense of Urgency; John P. Kotter

20. Trillion Dollar Coach; Eric Schmidt

21. Eleven Rings; Phil Jackson

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Certifications

• Complete three certifications; to be finalized.

Courses

• Complete one Agentic AI course; to be finalized.

Reshuffle : Who Wins when AI Restacks the Knowledge Economy

Finally finished reading the book Reshuffle by Sangeet Paul Chowdary. My summary points below.

The book talks about how AI reshapes power not by replacing workers, but by reorganizing coordination and the advantage goes to those who redesign systems, not those who deploy tools.

​

Summary of my notes: AI's biggest impact isn't about making things smarter or replacing jobs. It's about coordination, how work gets organized, how decisions flow and who controls the system. The winners are the ones who redesign how things work, not just the ones who buy better tools.​

The Real Story: We get distracted by whether AI is 'intelligent' or human-like and that's a wrong lens to judge the AI platform. What matters is whether it performs economically and can it deliver results inside a system? Just like GPS changed how we navigate without 'thinking, AI changes outcomes by restructuring how people and systems coordinate.​

It's Not Automation. It's Coordination: The automation framing misses one critical point. We all focus on task replacement, productivity gains, cost cuts. That leads to marginal improvements and a lot of hype. The real frame of coordination is in getting fragmented teams, vendors, tools and decisions aligned. AI's economic power comes from lowering coordination costs, not just execution costs.

The Container Ship Analogy: Singapore became a hub not by optimizing docks, but by positioning itself at the center of coordination. Shipping containers didn't win because they were faster. They won because they forced standardization across ports, rail, customs and contracts. They made coordination predictable. AI does the same thing for knowledge work that containers did for global trade.​

The Coordination Gap: Modern work is messy with siloed teams, disconnected tools, misaligned incentives. Traditional software only handles structured, rule-based environments. But most real work involves ambiguity, judgment calls, tacit knowledge, and negotiation. That gap is where AI actually matters.

How AI Works (Economically): AI does five things that make it useful for coordination under uncertainty:

• Sense the environment

• Build a working model

• Evaluate trade-offs

• Execute decisions

• Learn from feedback

This isn't about perfect reasoning. It's about making fragmented systems work together.

Jobs Get Unbundled and re-bundled: Jobs are bundles of tasks plus coordination plus judgment. AI strips out the coordination-heavy parts. Value shifts away from doing tasks toward orchestrating the system. That's why reskilling alone doesn't work. The system itself undergoes changed and one needs to reposition where value accumulates after everything gets rebundled.​

Organizations Need to Be Rebuilt: AI isn't a new hire, but a reorganization trigger. Traditional org charts optimize for control and hierarchy. AI enables flatter, more modular structures driven by outcomes. Authority shifts from hierarchy to system design, who sets up the coordination layer matters more than who manages people.

Competitive Advantage Comes from System Design: Buying AI tools doesnt create  advantage for organizations. Everyone buys the same software. Advantage comes only from managing uncertainty, owning the decision context, and designing the interfaces where others depend on your system.​

Control Without Consensus: Traditional coordination requires everyone to agree upfront. AI enables coordination without consensus by translating across fragmented actors. Value attracts participants first; consensus follows later. Power goes to whoever controls the shared representation layer and the system others plug into.

Designing for Indecision: AI increases options and it increases confusion. The real advantage is helping users decide under ambiguity. Companies that reduce cognitive load capture trust, attention and dependency. Decision orchestration becomes the new lock-in.​

AI Strategy: AI isn't a standalone strategy. Before defning AI strategy, the right questions to ask are: 1) Where does coordination break today? 2) Where does uncertainty block value? 3) Who controls the decision flow? 

Strategy shifts from "what tech to adopt" to "where do we re-architect the system".

CXOTalk Podcast : Why AI Works, but your strategy isn't

Came across a solid CXOTalk discussion: “Why AI Works, but Your Strategy Isn’t.”

Host: Michael Krigsman
Guest: Sangeet Paul Choudary
Link: https://www.youtube.com/watch?v=mV6g4uQEUUo

Summary
AI fails when it’s deployed as a tech add-on instead of a system redesign. Efficiency gains trigger new coordination costs that outgrow the benefits if strategy, structure, and governance aren’t rebuilt. Real value appears only when technology, process, people, and decision rights are treated as a single system.

Key Points

• Efficiency paradox: Automation cuts task effort but increases coordination overhead across teams.

• Strategy mismatch: AI treated as a tool, not a strategic shift. Weak change management leads to shallow outcomes.

• System thinking: AI operates inside an ecosystem. Design for “scale without consensus” using guardrails, rules, and clear authority lines.

• Leadership and governance: Senior leadership must own the AI agenda; data, model, and operational governance enforce trust and repeatability.

• Measurement: Look beyond cost. Track decision latency, adaptability, coordination load, and risk shifts.

Key Takeaways

• Stop treating AI as plug-and-play; treat it as organisational redesign.

• Build AI strategy as a unified system: tech + process + people + governance.

• Map human–system interaction changes; identify coordination points and ownership.

• Define decision rights: who acts on outputs, how exceptions route, how escalation works.

• Create feedback loops to catch unintended consequences and correct fast.

Integrating Generative AI Into Business Strategy: Dr. George Westerman

 https://www.youtube.com/watch?v=9RvWcXVaAng&t=68s

Conclusion: Transforming your organization with (generative) AI

1. AI can seem very intelligent, but you need to be intelligent in how you use it

2. Start with the problem, not the technology: many solutions will be combinations of technologies, processes, and people

3. Get started now: pilots, policy, org capability

4. Help your people be ready and willing to participate

5. Continuously iterate and improve: “Small t” transformations address risk and build capability for “Larger T” transformations later

Andrej Karpathy — “We’re summoning ghosts, not building animals"

Recently listened to this podcast. 

'Andrej Karpathy — “We’re summoning ghosts, not building animals"'

https://youtu.be/lXUZvyajciY?si=kDL68fMfVhq3sUGO

Amazing podcast and I would recommend you to patiently go through the 2 Hour 26 minutes podcat at 0.80X Speed.

I am always a fan of Andrej. And I really liked the clarity of his thoughts and the conviction with which he has explained various topics such as AGI, Evolution of intelligence, Future of Education etc. 

Here is the summary of the following topics discussed in detail

00:00:00 – AGI is still a decade away 00:30:33 – LLM cognitive deficits 00:40:53 – RL is terrible 00:50:26 – How do humans learn? 01:07:13 – AGI will blend into 2% GDP growth 01:18:24 – ASI 01:33:38 – Evolution of intelligence & culture 01:43:43 - Why self driving took so long 01:57:08 - Future of education

They discuss a wide range of topics, and it's always a treat to listen to Andrej's point of view. They delve deep into a variety of aspects.

Personally I am waiting for the teaching agent or the Tutor which Andrej is working on and how it revolutionizes the education.

AI Agents for CIOs: Practical Criteria, Alternatives and Best Practices

 

Excerpts from the Gartner Session. 

Link for the Gartner Session: 

https://webinar.gartner.com/742302/agenda/session/1676970?

Insurance 2030: AI Is Changing Everything

We are on the edge of a major shift in insurance, one driven by artificial intelligence, deep data, and connected devices. McKinsey envisions a future where insurance transforms from a reactive “detect & repair” model into a proactive “predict & prevent” system. 

Four Big Trends Reshaping Insurance Explosion of Data from Connected Devices As homes, cars, wearables, and medical devices increasingly talk to the web, insurers gain intimate visibility into risk. That means more personalized pricing and real-time service. Rise of Physical Robotics & Automation From drones to autonomous vehicles to 3D-printed structures, robotics will shift how risk is distributed and how claims are handled. Open Data Ecosystems & Sharing Insurance won’t sit in isolation. Data will move across industries — home sensors, auto telematics, health devices — creating richer profiles for insurers to use. Cognitive Technologies & Deep Learning Models that “learn” will increasingly power underwriting, claims, fraud detection, and customer service. Algorithms get smarter and more autonomous over time. 

What Insurance Might Look Like in 2030 

 1) Distribution: 

Buying a policy becomes almost instant - minutes or seconds. Insurance shifts away from “buy & renew annually” toward continuous, usage-based models. 

2) Underwriting & Pricing: 

The manual underwriting we know now mostly disappears. Machines use live data flows and external sources to decide risk and price policies in real time. 

3) Claims: Automation rules. Smart sensors, cameras, drones, AI routing—these reduce human intervention to only complex, contested cases. Response and repair become faster. 

What Insurers Must Do Now to Get Ready 

1) Learn the tech : not just IT teams but board members and business leads must get fluent in AI, IoT, and related innovations. 

2) Build strategy : map out a multiyear roadmap that strikes balance between pilot projects and hard bets. Decide whether to partner, acquire, or build in-house. 

3) Deploy a data strategy : collect, integrate, license, secure external and internal data. The richer your data, the better your models. 

4) Invest in talent & infrastructure : you’ll need data engineers, AI specialists, cloud architects, creative thinkers. Expect to reskill existing staff. Also, your tech stack must support rapid, adaptive change. 

Source: https://www.mckinsey.com/industries/financial-services/our-insights/insurance-2030-the-impact-of-ai-on-the-future-of-insurance#/

The Forrester Wave™: Cognitive Search Platforms, Q4 2023

While researching more on the GenAI for business Enterprise platforms, I came across the below link which highlights the Forrester Wave™ evaluation of cognitive search platforms (Algolia, Amazon Web Services, Coveo, Elastic, Glean Technologies, IntraFind, Kore.ai, Lucidworks, Microsoft, Mindbreeze, OpenText, Sinequa, Squirro and Yext). 

It assesses leading providers based on 27 criteria, analyzing their strengths, weaknesses, and strategic direction. 

The cognitive search market is undergoing a transformation driven by generative AI, with increasing demand for search-driven applications and the adoption of technologies like large language models (LLMs) and vector databases. 

Vendors are evolving beyond basic search functionalities to offer robust indexing, intent-based search, and extensive data connectivity. The report highlights their capabilities in intent understanding, data integration, and advanced retrieval methods. 

Leaders excel in intent-based search and LLM integrations, while other vendors focus on specific industry needs or customization capabilities. 

As new players enter the market, buyers must prioritize platforms that offer comprehensive indexing, deep intent analysis, and strong data pipelines. 

The report provides a comparative analysis, encouraging buyers to customize evaluations based on their requirements using Forrester’s detailed scorecard.

https://reprint.forrester.com/reprints/the-forrester-wave-tm-cognitive-search-platforms-q4-2023-b947209c

USPs of few leading Congnitive Search Engine Providers.

1. Coveo is a good choice for firms that want powerful automated relevancy tuning 
2. Sinequa is a good fit for large enterprises that have a variety of different data types, especially specific data demands such as pharma and manufacturing, and that want to deliver a highly contextual search experience that brings those data types together in multimodal results. 
3. Lucidworks is a good choice for large enterprises who want to build a highly customizable search solution that can support robust internal- and external-facing search experiences. 
4. Squirro is a good fit for companies who want to build a powerful and flexible search experience for core use cases with focused data sets. 
5. Opentext IDOL is a good fit for customers who want one of the most extensive search toolboxes, but must be prepared to have some good builders on hand to achieve success with this complexity. 
6. Kore.ai is a good fit for companies who want to build cognitive search solutions for knowledge workers or for scenarios where direct answers are needed.
7. Elastic is a good fit for companies who want to build a hybrid search experience on a flexible foundation, either using their own internal resources or working with a partner to customize the platform to their business needs.
8. Glean is a good fit for customers looking for a search capability that can be quickly deployed in their search applications and want to elevate virtual assistants to the same level as the rest of enterprise search. 

The Power of Graph Technology

While exploring Knowledge Graphs, I came across Tony Seale's insightful series on Embracing Complexity - a fascinating read!

"...The Knowledge Graph is not a product that you can buy off the shelf. It is a way of organizing your data and algorithms in a unified network for each organization to bring its genius to this process..."

In a world of increasing complexity, organizations must move beyond traditional tabular data structures and embrace Knowledge Graphs—dynamic, interconnected networks that unify data, cloud, and AI. The articles highlights how graph-based data models unlock hidden insights by capturing relationships, feedback loops, and abstractions that traditional databases overlook. 

Key tools like Graph Adapters, Data Services, and Graph Neural Networks enable seamless integration of diverse data sources while enhancing AI-driven decision-making. 

A well-structured Knowledge Graph provides a holistic view of an organization, fostering systemic thinking, better change management, and more informed decision-making. As businesses accelerate into the digital age, building a Knowledge Graph is no longer optional but it is essential for survival and growth.

The graph-shaped data enables richer context, while a graph-shaped cloud ensures seamless connectivity across distributed data sources. 

Traditional AI struggles due to its reliance on linear, structured data, but Graph Convolutional Networks (GCNs) offer a breakthrough by incorporating networked relationships into AI learning. 

By embedding intelligence directly into the data-cloud network, organizations can unlock context-aware AI, enabling smarter predictions, systemic automation, and deeper insights. This network-based approach represents a paradigm shift, making AI more adaptive, intuitive, and lifelike for organizations navigating complex data ecosystems.

Its quite an informative read. Please read through all parts.

https://medium.com/@Tonyseale/embrace-complexity-conclusion-fb8be6f39deb

Book Summary : 'How to Become a Rainmaker: The Rules for Getting and Keeping Customers and Clients" by Jeffrey J. Fox'

It is is a bestselling guide that offers straightforward, practical advice on how to excel in sales and business development. 

A "Rainmaker" is a term used to describe the seller who consistently brings in new clients, generates revenue, and drives business growth—an invaluable asset to any organization.

Key Lessons from the book:

1. The Rainmaker Mindset:

• Rainmakers think and act differently from average performers.
• They focus on delivering value to clients and building long-term relationships.

2. The Rules of Selling:

• Always sell the benefits of a product or service, not just its features.
• Ask probing questions to understand what the client truly needs.
• Follow up promptly and regularly—timing and persistence are critical.

3. Prioritize the Customer:

• Focus on how you can help clients achieve their goals.
• Make every interaction about them, not you.

4. Mastering Business Etiquette:

• Handwritten thank-you notes go a long way in building trust and goodwill.
• Always be professional, respectful, and courteous in all dealings.

5. Be Results-Oriented:

• Rainmakers don’t just set goals—they surpass them.
• They measure success by the results they deliver, not just the effort they put in.

6. Networking and Referrals:

• Build a network of advocates who can refer you to new opportunities.
• Reward and thank those who provide referrals to maintain strong relationships.

7. Time Management:

• Rainmakers focus on high-value activities that directly contribute to revenue generation.
• Eliminate distractions and prioritize actions that lead to results.

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Key Takeaways:

• Sales Excellence: Sales is about helping people solve problems, not just making a pitch.
• Focus on Results: Rainmakers are relentless in delivering measurable outcomes.
• Personal Branding: Build a reputation for reliability, expertise, and professionalism.

AI Agents Market landscape.

 𝗧𝗵𝗲 𝗨𝗟𝗧𝗜𝗠𝗔𝗧𝗘 𝘄𝗲𝗯𝘀𝗶𝘁𝗲 𝗼𝗻 𝘁𝗵𝗲 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁 𝗺𝗮𝗿𝗸𝗲𝘁 𝗶𝘀 𝗵𝗲𝗿𝗲 — 𝗜 𝘄𝗼𝘂𝗹𝗱𝗻’𝘁 𝗯𝗲 𝘀𝘂𝗿𝗽𝗿𝗶𝘀𝗲𝗱 𝗶𝗳 𝗶𝘁 𝗯𝗲𝗰𝗼𝗺𝗲𝘀 𝘁𝗵𝗲 𝗴𝗼-𝘁𝗼 𝘀𝗼𝘂𝗿𝗰𝗲 — 𝘁𝗵𝗶𝗻𝗸 𝗼𝗳 𝗶𝘁 𝗮𝘀 𝗮 𝗳𝗼𝗿𝗺 𝗼𝗳 𝗪𝗶𝗸𝗶𝗽𝗲𝗱𝗶𝗮, 𝗯𝘂𝘁 𝗳𝗼𝗿 𝗳𝗼𝗿 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀! 

https://aiagentsdirectory.com/landscape 

Source: Andreas Horn

AI agents are more than robots, software, or simple automation. They’re 𝗮𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 𝗱𝗼𝗲𝗿𝘀 that can 𝗹𝗲𝗮𝗿𝗻, 𝗿𝗲𝗮𝘀𝗼𝗻, 𝘁𝗮𝗸𝗲 𝗮𝗰𝘁𝗶𝗼𝗻, 𝗮𝗻𝗱 𝗺𝗮𝗸𝗲 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻𝘀. And currently everyone’s talking about them. Despite the growing buzz, real use cases are taking off already. Whether it is predictive maintenance in manufacturing, customer service, or AI agents disrupting software development, AI agents will be the autonomous specialists of the future.  

The field is very dynamic and there are lots of new approaches, frameworks, and use cases added every week in this field. To keep a good overview the website below is helping a lot. It categorizes most existing AI Agent projects (450+) in the market and breaks them down into the following categories:  

➤ 𝗕𝘂𝗶𝗹𝗱𝗲𝗿𝘀: 142 projects  

➤ 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝘃𝗶𝘁𝘆: 56 projects  

➤ 𝗖𝗼𝗱𝗶𝗻𝗴: 55 projects  

➤ 𝗖𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝗦𝗲𝗿𝘃𝗶𝗰𝗲: 42 projects  

➤ 𝗣𝗲𝗿𝘀𝗼𝗻𝗮𝗹 𝗔𝘀𝘀𝗶𝘀𝘁𝗮𝗻𝘁/𝗗𝗶𝗴𝗶𝘁𝗮𝗹 𝗪𝗼𝗿𝗸𝗲𝗿𝘀: 58 projects  

➤ 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀: 28 projects  

➤ 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄: 20 projects  

➤ 𝗖𝗼𝗻𝘁𝗲𝗻𝘁 𝗖𝗿𝗲𝗮𝘁𝗶𝗼𝗻: 19 projects  

➤ 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵: 12 projects  

𝗧𝗵𝗲 𝗯𝗲𝘀𝘁 𝗽𝗮𝗿𝘁: 𝗧𝗵𝗲 𝘄𝗲𝗯𝘀𝗶𝘁𝗲 𝗶𝘀 𝘂𝗽𝗱𝗮𝘁𝗲𝗱 𝘄𝗲𝗲𝗸𝗹𝘆 𝗮𝗻𝗱 𝗶𝘀 𝗮𝗹𝘄𝗮𝘆𝘀 𝘂𝗽-𝘁𝗼 𝗱𝗮𝘁𝗲! 𝗜 𝗯𝗲𝗹𝗶𝗲𝘃𝗲 𝗶𝗻 𝘁𝗵𝗲 𝘁𝗵𝗲 𝗺𝗼𝗻𝘁𝗵𝘀 𝗮𝗵𝗲𝗮𝗱, 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝗶𝗰 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀 𝘄𝗶𝗹𝗹 𝗹𝗶𝗸𝗲𝗹𝘆 𝗱𝗿𝗶𝘃𝗲 𝗺𝗼𝗿𝗲 𝗽𝗿𝗼𝗴𝗿𝗲𝘀𝘀 𝘁𝗵𝗮𝗻 𝗲𝘃𝗲𝗻 𝘁𝗵𝗲 𝗻𝗲𝘅𝘁-𝗴𝗲𝗻 𝗳𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 𝗺𝗼𝗱𝗲𝗹𝘀. 

𝗧𝗵𝗶𝘀 𝗺𝗶𝗴𝗵𝘁 𝗯𝗲 𝗮 𝗿𝗲𝗮𝗹𝗹𝘆 𝘂𝘀𝗲𝗳𝘂𝗹 𝗿𝗲𝘀𝗼𝘂𝗿𝗰𝗲 𝗳𝗼𝗿 𝘁𝗵𝗼𝘀𝗲 𝗼𝗳 𝘆𝗼𝘂 𝘄𝗵𝗼 𝘄𝗼𝗿𝗸 𝗶𝗻 𝗔𝗜 𝗮𝗻𝗱 𝘄𝗶𝘁𝗵 𝗮𝗴𝗲𝗻𝘁𝘀.

Book Summary: Great Demo!: How to Create and Execute Stunning Software Demonstrations" by Peter Cohan

Demos and presentations are an integral part of every presales and sales professional’s life. Quite often, I have encountered situations where teams are required to deliver demos at short notice with minimal information. Whether it’s showcasing POC work or presenting a POV as part of an RFP evaluation, demos offer a valuable opportunity to highlight key capabilities to stakeholders in a short span of time. Solution demos play a crucial role in sales conversations, and making them impactful can often be challenging. However, if not executed well, they can have significant consequences.

A partner once recommended this book '"Great Demo!: How to Create and Execute Stunning Software Demonstrations" by Peter Cohan' to me, and I found it incredibly insightful. 

I highly recommend the book to my colleagues in Presales and Solution teams, especially those involved in product, solution, and POC demos. 

Gathering the necessary requirements from prospects is often tough, and with limited time, we must make the most of every opportunity. One of the common dilemmas we face is deciding what to showcase and what to leave out.

The book “Great Demo!" is about shifting the focus from a feature-heavy presentation to a problem-solving, customer-focused narrative. By keeping the customer’s needs at the center of the demo, the approach helps sales teams deliver more compelling presentations, shorten the sales cycle, and close deals more effectively.

This book provides a structured approach to software demos, focusing on demonstrating products in a way that captures customer interest, engagement, and buying intent. Below is a detailed summary of the core principles and strategies from the book to help guide effective and compelling software demonstrations.

Core Concepts:

1. The “Last Thing First” Principle

This principle emphasizes delivering the primary value or result the customer seeks right at the beginning. Instead of starting with extensive background information, jump straight to what the customer wants to see. This approach captures the audience's attention quickly and immediately establishes the product's relevance to their needs.

2. Critical Business Issue (CBI)

Peter Cohan stresses the importance of identifying the prospect's Critical Business Issue (CBI) before creating or delivering a demo. Understanding this core problem ensures that every feature or functionality shown in the demo directly addresses the customer’s most pressing needs.

3. Situation, Solution, and Value

A successful demo should clearly present:

• Situation: The current challenge or status quo that the customer faces.
• Solution: How the product directly addresses or improves upon this challenge.
• Value: The tangible benefits or ROI the customer can achieve with the product.

4. The “Do the Last Thing First” Workflow

This structured workflow for software demos consists of four key steps:

• Discovery: Conduct an in-depth discovery process to uncover the customer’s needs, pain points, and specific use cases.
• Introduction: Introduce the product by immediately showing how it addresses the critical problem identified during discovery.
• Demonstration: Dive into the software, keeping the focus on features that provide direct value to the customer.
• Summary: Conclude by reinforcing the key points, emphasizing the benefits, and restating how the solution meets the customer’s problem.

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Key Strategies:

1. Focus on Customer Benefits

An effective demo should not be a technical walkthrough but a focused presentation on how the product provides value to the customer. Rather than overwhelming them with features, concentrate on functionalities that directly solve their problem.

2. The “Inverted Pyramid” Structure

Cohan suggests using the inverted pyramid model, common in journalism, where the most important information is presented first, followed by supporting details. This keeps the audience engaged from the beginning and allows them to leave at any point without missing key takeaways.

3. Minimal Viable Demo (MVD)

Deliver a Minimal Viable Demo, showcasing only the essential parts of the product that directly address the customer’s issue. Avoid spending time on features that do not contribute to solving their critical business problem.

4. Vision Generation Demos

These demos are designed to help the customer envision success. By illustrating how your solution fits their specific needs, you enable the prospect to see the value it can bring to their organization, fostering early buy-in and commitment.

5. Interactive Demos

Encourage interaction by asking questions, seeking feedback, and allowing the prospect to use the software. This makes the demo feel like a collaborative exploration rather than a one-sided presentation, enhancing engagement.

6. Handling Questions and Objections

Cohan recommends addressing customer questions and objections openly during the demo. Rather than avoiding tough questions, tackle them head-on while maintaining control of the demo flow, linking responses back to the product’s key value proposition.

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Techniques for Effective Delivery:

1. Storyboard the Demo

Just like a movie, a software demo needs a storyboard. Plan out what you will show, the sequence of steps, and how you will communicate each point. This ensures a smooth, logical flow that keeps the audience engaged.

2. Demo Flow

Keep the flow of the demo simple, avoiding unnecessary complexities. The demo should be easy for the audience to follow, focusing on how it solves their problem, not just how it functions.

3. Use Scenarios and Use Cases

Present the demo in the context of specific customer scenarios or use cases. By demonstrating how the software solves a real-world problem, the prospect will have a clearer understanding of the product’s value.

4. Repeat Key Points

Throughout the demo, emphasize and repeat the key benefits and outcomes the customer will gain. Reinforcement ensures that these points resonate with the audience and that they leave the demo with a clear understanding of how your solution helps them.

5. Summarize and Reiterate Value

At the end of the demo, summarize the main points and reiterate the value your product brings. Remind the prospect of the Critical Business Issue and how your solution resolves it.

---

Post-Demo Engagement:

1. Follow-Up

After the demo, follow up promptly with materials that reinforce the key takeaways. These could include:

• A summary of what was demonstrated
• Relevant case studies
• Answers to questions raised during the demo

2. Tailored Proposals

Build on the demo by creating a tailored proposal that addresses the specific needs and goals identified during the demonstration. This ensures continuity in the sales process and maintains momentum.

Useful Links

I keep updating this blog with useful links I come across

1) GCN (Graphical Convolution Network): 

https://towardsai.net/p/l/graph-convolutional-networks-gcn-explained-at-high-level 

2) Knowledge Graphs:

https://medium.com/@Tonyseale/embrace-complexity-conclusion-fb8be6f39deb

How to Start a Startup Series @Stanford University

1) Lecture 1 - How to Start a Startup (Sam Altman, Dustin Moskovitz)

https://www.youtube.com/watch?v=ak8G9Ntc_dA

2) Lecture 2 - Team and Execution (Sam Altman)

https://www.youtube.com/watch?v=Gn7GDQeG0vQ

3) Lecture 3 - Before the Startup (Paul Graham)

https://www.youtube.com/watch?v=pQ-9qWRBaCA

4) Lecture 4 - Building Product, Talking to Users, and Growing (Adora Cheung)

https://www.youtube.com/watch?v=ygGys3NZNAo

5) Lecture 5 - Competition is for Losers (Peter Thiel)

https://www.youtube.com/watch?v=-oKjLVECMKA

6) Lecture 6 - Growth

https://www.youtube.com/watch?v=UoEQyj5Wn8s

7) Lecture 7 - How to Build Products Users Love

https://www.youtube.com/watch?v=xlQWogFlvmA

8) Lecture 8 - How to Get Started, Doing Things that Dont Scale, Press

https://www.youtube.com/watch?v=8rAajBg5L_A

9) Lecture 9 - How to Raise Money

https://www.youtube.com/watch?v=CBA01CpVqwI

10) Lecture 10 - Culture

https://www.youtube.com/watch?v=gdkhKuzgd34

My 2024 Indian Election Prediction

 

LLMs: Understanding Tokens and Embeddings

I'm sure that since ChatGPT went mainstream, you've been hearing the term LLM quite frequently. The article below provides a clear and insightful explanation of Large Language Models (LLMs) and the concepts of tokens and embeddings.

The article explores how LLMs process text by converting it into numerical representations. It first explains why text must be transformed into numbers for machine learning systems, emphasizing that tokens—the fundamental units derived from text—are mapped to unique numeric identifiers.

While words might seem like natural token candidates, the article highlights that tokens can also be sub-word units, offering greater flexibility in text representation. This approach helps address challenges such as case sensitivity and the emergence of new words, which can complicate text processing. By breaking text into smaller components, like characters or sub-words, LLMs can handle linguistic variations and nuances more effectively.

The article also delves into embeddings, which are vector representations of tokens that capture their meanings and relationships in a continuous vector space. These embeddings allow LLMs to understand context and semantics, enhancing their ability to perform tasks like language generation and comprehension.

Overall, the piece underscores the crucial role of tokenization and embeddings in improving LLMs' capabilities in natural language processing (NLP).

https://msync.org/notes/llm-understanding-tokens-embeddings/

Offshore Software Development Rates by Country: Detailed Guide

With companies increasingly focused on cost reduction, near-shoring and offshoring have become mainstream strategies. Customers often ask us to compare offshore pricing or to estimate costs for delivering work from locations such as Latin America, India, or Europe.

Are you looking to optimize your software development budget?

I recently came across a blog by Kateryna that provides an in-depth analysis of offshore software development rates across different regions, key pricing factors, and insights on choosing the right outsourcing destination for your business.

https://fulcrum.rocks/blog/software-offshore-development-rates

Kateryna Khalim Marketing Specialist at Fulcrum Rocks

 

Methodologies/Frameworks/Tools Used during the Discovery Phase of the Sales Process

The discovery phase of the sales process is a critical stage where you gather information about a potential customer's needs, challenges, goals, and pain points. To facilitate this phase effectively, sales professionals often use a variety of tools and frameworks. Here are some of the tools and frameworks commonly leveraged during the discovery phase:

Customer Relationship Management (CRM) Software:

Tools like Salesforce, HubSpot, or Microsoft Dynamics provide a centralized database for managing customer information. Sales teams can track interactions, log notes, and set reminders for follow-up.

Buyer Persona Framework:

Developing buyer personas helps create a detailed profile of the ideal customer. It includes demographics, job roles, pain points, and goals. HubSpot and Xtensio are examples of platforms that assist in creating buyer personas.

SWOT Analysis:

Conducting a SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis helps assess a potential customer's internal and external factors. It's a simple yet effective framework for understanding their current situation.

SPIN Selling:

The SPIN (Situation, Problem, Implication, Need-payoff) framework, popularized by Neil Rackham, helps salespeople ask the right questions to uncover a customer's pain points and needs.

BANT Framework:

BANT stands for Budget, Authority, Need, and Timeline. This framework helps determine if a lead is qualified and worth pursuing.

Value Stream Mapping:

Value stream mapping is a lean methodology used to visualize and analyze the steps involved in delivering a product or service. It helps identify areas where improvements can be made to meet customer needs more effectively.

Customer Surveys and Questionnaires:

Tools like SurveyMonkey or Google Forms allow you to create and distribute surveys to gather feedback from potential customers. This can help uncover valuable insights.

Competitive Analysis Tools:

Tools like SEMrush, Ahrefs, or SpyFu can be used to analyze a potential customer's competitors. This helps understand the competitive landscape and identify areas where your solution can offer a competitive advantage.

Social Media Listening Tools:

Tools like Hootsuite or Brandwatch enable you to monitor social media conversations related to your potential customer or their industry. This can provide valuable insights into their challenges and preferences.

Data Analytics and Business Intelligence Tools:

Platforms like Tableau, Power BI, or Google Data Studio can help you analyze data related to a potential customer's industry trends, market share, and performance metrics.

Consultative Selling Framework:

Consultative selling is an approach that emphasizes listening to the customer's needs and providing customized solutions. It involves open-ended questions and active listening techniques.

Discovery Call Frameworks:

During discovery calls, sales professionals often use structured frameworks, such as the Medic Framework (Metrics, Economic buyer, Decision criteria, Identify pain, Champion), to guide the conversation and gather essential information.

Document Management and Collaboration Tools:

Tools like Google Workspace or Microsoft Office 365 facilitate collaboration and document sharing during the discovery phase.

Sales Playbooks:

Sales playbooks are comprehensive documents that provide sales teams with guidance, scripts, and best practices for different stages of the sales process, including discovery.

AI and Machine Learning Tools:

Some advanced tools use AI and machine learning algorithms to analyze data and predict customer behavior or needs, helping sales teams make data-driven decisions.

These tools and frameworks are valuable resources for sales professionals to systematically uncover customer insights, tailor their approach, and ultimately deliver solutions that address customer needs effectively. The specific tools and frameworks used may vary depending on the industry, company, and sales approach.

Data Scientist vs. Data Engineer: Understanding the Difference

Are you curious about the difference between a Data Scientist and a Data Engineer? While both roles work with data, their responsibilities differ significantly.

At a high level:

• Data Engineers source, transform, and secure data, making it accessible for analysis.
• Data Scientists prepare and analyze this data to build models and extract insights.

---

What Does a Data Scientist Do?

A Data Scientist specializes in extracting valuable insights from data, using machine learning, statistical analysis, and visualization techniques. Their role involves:

• Data Collection & Preprocessing: Gathering data from multiple sources (databases, APIs, etc.), cleaning, and preparing it for analysis.
• Exploratory Data Analysis (EDA): Identifying trends, patterns, and anomalies in the data using statistical methods and visualization.
• Feature Engineering: Creating and selecting relevant variables to improve model performance.
• Model Development & Training: Building predictive models using machine learning and deep learning techniques.
• Model Evaluation & Deployment: Assessing model performance and integrating successful models into production.
• Data Visualization & Communication: Presenting insights through reports, dashboards, and visualizations.
• A/B Testing & Business Intelligence: Running experiments to support data-driven decision-making.
• Ethical Considerations: Ensuring data privacy, fairness, and ethical use of AI.

In short, Data Scientists turn data into actionable insights that drive business decisions.

---

What Does a Data Engineer Do?

A Data Engineer focuses on designing and maintaining the data infrastructure that enables efficient storage, processing, and access to data. Their key responsibilities include:

• Data Ingestion & Storage: Developing pipelines to collect and store data from various sources (SQL/NoSQL databases, data lakes, etc.).
• Data Transformation & ETL Pipelines: Cleaning, structuring, and transforming raw data into a usable format.
• Data Modeling & Integration: Defining schemas, optimizing database structures, and integrating data from different sources.
• Data Quality & Security: Implementing validation checks, access controls, and encryption to ensure data integrity.
• Scalability & Performance Optimization: Using cloud services and distributed frameworks (Hadoop, Spark) to handle large-scale data processing.
• Automation & Monitoring: Automating workflows and monitoring data pipelines to maintain system efficiency.
• Collaboration & Documentation: Working closely with data scientists and analysts to provide the right data infrastructure.

Essentially, Data Engineers build the foundation that enables data scientists and analysts to perform their work efficiently.

---

Key Takeaways

• Data Engineers create and manage the data infrastructure.
• Data Scientists analyze and interpret data to generate insights.
• Both roles work together to maximize the value of data in an organization.

By understanding these roles, businesses can better allocate resources and optimize their data-driven strategies.

Book Summary: The Challenger Sale Summary

I read 'The Challenger Sale. book by authors 'Brent Adamson' & 'Mathew Dixson.' Its a great read and I recommend any B2B technology seller to read and finetune your selling techniques.

My draft summary notes from my read.

The Sales Personas can be categorized under 5 types namely

1.     The Hardworking

2.     Lone Wolf

3.     Relationship Guy

4.     The Reactive Seller

5.     The Challenger Seller

 

 Key Attributes of the Challenger Seller

1.     Rep offers a unique perspective.

2.     Two-way communication 

3.     Knows Customer value drivers.

4.     Know Customer Economic Drivers

5.     Rep is comfortable discussing money.

6.     Cas push the customer.

 

 The Challenger Seller does the following things.

1.     Teach

2.     Tailor

3.     Take Control

 

In a complex Sale situation, almost 40% of the Challenger Sellers are star performers.

 

Principles of the challenger sale model:

 

1.     Challengers are made and not born.

Right tools, training, and rewards – Sales reps can be trained.

 2.     Its combination of skills matter the most.

a.     Use in combination Teach, Tailor and Take control.

 

3.     Challenging is about the Organization's capabilities and not the individual reps.

a.     Teaching material, frameworks

b.     Tailor – The organization can leverage business intelligence, which teaching will resonate will which customer.

c.     Taking Control – This is the rep’s skill. 

 4.     It’s a journey to build a challenger sales force 

 

Does the challenger selling mode work?

Teaching for Differentiation

1.     Teach customer something new about how to compete in the market.

2.     New perspectives

3.     Getting ahead of RFP. Reshape RFP. Happy to construct the RFP. 

 

Tailoring for resonance:

1.     Tailor the message to individual customers, and personal economic drivers, craft customized messages

 

Taking Control of the Sale:

1. Being assertive doesn’t mean being aggressive. Doesn’t give to customer’s demand of discounts. Brings back discussion to value. (Example: If the customer is asking for price discounts, relook at all your features, and ask customers to rank these features in order of priority)
2. You can’t be an effective teacher if you can’t push your customers.
3. Take control of customer discussions with a specific end in mind.
4. In situations where the customer is risk-averse and prefers the status quo, the ability to take control can be a game changer.  

 Sales Innovation is specific to individual deals. 

Teaching for Differentiation

 

Why do insights Matter? 

1.     Discover the customer’s pain (Asking questions, and probing do not work perfectly as thought).

a.     Tell Customers what they need. Challengers better understand the customer’s world and teach customers what they don’t know.

 

Loyalty: 

The product can be great, customer service is world-class, and the brand is great, but the competition provides the same level.

 

Customers focus on general similarities between the competition.

1. 38% attribute loyalty to the brand, customer service, and product features.
2. Only 9% of loyalty is attributed to lower prices. Today’s discounts won’t get you business for tomorrow.
3. Customers value sellers who provide incredibly valuable information and conversations with customers. Outperform in the sell over competitors.

Power of Insight

 

Attributes Customers rate higher than other

1. Rep offers unique and valuable perspectives on the market
2. Rep helps navigate alternatives
3. Rep provides ongoing advice/consultation 
4. Avoid potential landmines
5. Rep educates the customer on new issues and outcomes
6. Supplier is easy to buy from
7. The supplier has widespread support across the organization

The sales professional should 

• Align this to customers’ needs to reduce costs, increase revenue, penetrate new markets, and mitigate risks which customers didn’t even know.
• Need for consensus among the customers before the purchase. Network advocacy along the way to gain support. 
• Sale has to be simple and not complicated. Don’t make your customer work hard. 
• Customers value the rep’s teaching skills (great insights) more than the discovery skills.  

Commercial Teaching:

1.     Lead to your unique strengths

• Share unique insights
• Tie back to your capability, where you outperform your competitor. <Seller should always ask. Why should customers buy from us?>

2.     Challenge customers' assumptions

• Find the connection between Insight and the customer. Your insights should challenge their assumptions, which they haven’t considered before.
• Reframe the data in front of customers, how they operate, and help them navigate. Thoughtful reflection from customers validates the reframing of customer issues.  
• Help customers see things differently.

3.     Catalyze action.

• Get the customer to act. Customers easily lose focus. Make customers realize why action matters. 
• Convince customers how your solution provides incremental value.
• Best ROI -> Cost incurring by failing to act on opportunities we just talked which customers overlooked. What they are losing against what they will gain. 

4.     Scale across customers

• Think about customer segmentation.
• Identify customers by needs.

Six Steps of building a Teaching pitch

1.     The Warmer: (Build credibility, shared experience, common challenges). Hypothesis bases selling.

• a.     Assessment of customers’ key challenges at similar companies, benchmarking data. Demonstrate that they are not alone.
• b.     Ask for their reactions.
• c.     Cuts the chase. It shows you have done the homework 

2.     The Reframe: (Tell customer New insights)

• a.     Introduce a new perspective and connect with a bigger problem, bigger opty
• b.     Insight itself. Headline. (Customer reaction: Huh, I haven’t thought about this). If you fail to provide unique insight, you fail to provide unique value.
• Surprise customer. Make them curious. 
• Alternate view

3.     Rational Ground: (Build Business Case, why that matters)

• Data/Graphs/charts to justify the hidden cost and size of the opty they have lost. 
• Make them feel like they are drowning. Create Fear. 

4.     Emotional Impact: (Make it personal. How nearly every company acts)

• The customer should be able to apply. 
• How do we get out of we are diff. 
• Make an emotional connect. Customers should see it as their story
• It’s about the narrative. How other customers went through a similar path.

5.     A New Way (Paint the picture. How Ranger can help.)

• Convince the customer of the solution. 
• It’s about the solution. 
• Behaving diff will change life.

6.     Your Solution:

• How is your solution better
• How you can lay out the solution. 

When does the supplier enter the conversation?

How do you build a commercial Teaching Organization?

 

Create a logical path. What’s currently costing customers more money than they realize which we can help them fix.

 

Inputs from organizations to Sellers

1.     Provide customers with game-changing insights

2.     Specifying and personalizing the impact

3.     Introducing the capabilities as the best means to solve the problems

 

Commercial Teaching:

 

Team sport

·      Sales & Marketing Alignment

·      Marketing has tools to generate insights. Marketing must serve as insight generating machine to equip reps.

·      Sales must ensure reps know to use that insight.  

 

1.     Identify unique benefits.

2.     Develop commercial insights that challenge customer thinking.

3.     Package commercial thinking into compelling messaging 

4.     Equip reps to challenge customers.

 

 

The single biggest opportunity is not the solutions and the services you sell, but the quality of insights you deliver as part of the sale.

 

 Tailoring for Resonance

 

Helps with the broader consensus among the organization.

 

53% of B2B of Loyalty – How do you sell than what you sell.

 

Decision Makers:

Senior Executives and the Procurement.

What matters to decision makers. Aspects of the sale process. They buy from organizations.

 

Widespread support across my organization to my decision-makers. Easy to buy from, accessible. Work with most of the stakeholders in the customer organization. Decision Maker values the team’s inputs.

 

Senior Execs place a higher value on Reps Knowledge, and Procurement place a higher value on reps not overcharging. 

 

Identify, and nurture key stakeholders.

 

Consensus Sale: 

Understand what drives loyalty to the team and not only the senior executives.

 

The biggest influencer on end-user loyalty is on Rep’s professionalism. Underpromise and over-deliver. 

 

The ability of the rep to provide a unique value prop. Educate customer.

 

Educating the end users, and influencers, providing insights and helping them reduce cost and increase revenue makes them advocate your solution. 

 

The link between the influencer/Stakeholder and the decision-maker is significantly stronger. 

 

One of the conventional ways to build loyalty is to elevate the conversation to the C-suite. Suppliers should have widespread support across the organization. 

 

The best way to sell is not to approach the decision maker directly, but by approaching indirectly through the established stakeholders to ensure widespread support. The stakeholder has greater leverage over influencing the decision-maker than the supplier rep.

 

Tailoring the Message:

 

·      Talk to more people/stakeholders in consensus-making buying to achieve maximum resonance. 

·      Start at the Customer’s industry, company, and role and then down to the person. Marketing can add value. Any merger, customer gaining share, regulatory, press releases and earning reports

 

Reducing variability:

·       

 

Tailor: Talk to more people to get the buy-in. How exactly tailor the message?

 

Start with the product, the person’s industry/company and to the personal level (personal goal’s and objectives).

 

Rep needs to know multiple things to tailor the message effectively. Personality, role, region

 

Knowledge of individual stakeholder’s value drivers

And economic drivers of end user’s business  

 

Focus not on what the end user is selling, but on what he is trying to achieve. Focus on the individual’s most pressing needs.

 

Tailoring Tools. Marketing and Sales can provide the same to reps.

 

Tailoring Cheat Sheets.

Capture end user’s (specific role) specific objectives, goals, and regular questions asked. 

Provide tailored product bundles. 

 

Template capturing the end user-specific outcomes.

 

Taking Control of the Sale:

 

Hold value and maintain the momentum of the sales process. 

Challengers always think of the next steps. The goal is to sell deals, moving ahead. Create urgency.

 

Three Misconceptions:

1.     Taking control is synonymous with negotiation.

• It’s not about negotiation but about the entire sales process.
• Many times optys are veiled optys and buyer gets into discussions with buyers. The customer doesn’t have any intention of buying. Challenger sellers can sense this, if they are not granted access to the senior exec. They move away. Time is better spent anywhere.
• Its important to gauge is they have access to the senior folks initially.
• Challengers understand the buyer’s goals and objectives and biases. They map out what the stakeholders care about and why they care about it.   

2.     Reps take control only in matters of money.

• Reps take control and educate the customer on the challenges customer faces and solutions to the challenges. They reframe the customer’s world. 
• On Buyer’s skepticism and push back stating their company is diff, the challenger pushes back on the customer acknowledging buyers company is diff just like all organizations seller works with. Challenger brings new ideas to the table. 

3.     Reps will become aggressive.

• Negotiate, stay your ground, and explore avenues to discuss and convince customer on the rationale. 

 

Taking Control:

 

Challengers thrive in ambiguity. They like tension. We look for excuses to avoid them. 

 

Dupont Negotiation tactics:

 

Purposeful planning: Do in advance of the sales.

 

Situation Sales Negotiation. (SSN) Template

 

Planning

1.     List all We have strengths and areas where we have weaknesses.

2.     All information needed from the customer.

3.     Ask all questions to be asked.

4.     Information customers may be likely to ask.

5.     Difficult questions which may be asked and responses.

6.     List of possible concessions to be given. List of customer demands to be made. 

 

Winning such conversations is what differentiates the challengers. They have the scorecard wired into their brain.

 

High-performing sales reps spend a significant amount of time planning. Not on the current move but think steps ahead of the buyer.

 

Sales reps must learn to challenge customers and push back.

 

Anatomy of successful negotiations

 

4-Step Framework (by Dupont)

1.     Acknowledge and differ

• E.g. Price discounts without threatening the deal (I understand price is what needs to reduce, but before we do that we completely understand all your needs to be done so that we can do all we can)
• Rep has bought some time. He has also promised the closure.
• Don’t come across as aggressive. 

2.     Deepen and broaden

• Reps are given tactics to understand customers’ underlying additional needs.
• Expand customer’s view important to them
• Customers to restate things which customer likes
• What are you trying to achieve with the desired discount? 

3.     Explore and compare

• Reps are given tactics to explore additional needs
• The primary idea is to expand customers' view of the things important to them
• What else besides price matters? Warranty, expedited shipping, and installation. 
• What are you trying to achieve by 10% reduction?
• Often the requirement can be achieved by offering something else. 
• Supplier creates value and provides additional value. 

4.     Concede according to the plan. 

• Reps are taught to trade carefully with low-value solution elements before getting to the price.
• What is the seller willing to concede?
• How and when?
• Avoid certain concessions (start with small and then get too big as the concessions progress) or take or leave it. The customer feels cheated.
• Start with meaningful concessions. Give concession patterns. 

 

Taking control happens throughout the discussion. Making powerful requests shows seriousness. 

 

Overcome passivity: Teach reps about clarity of direction over quick closure. Create real value in the sales process.

 

 

Manager and Challenger selling Model:

 

Front line sales manager needs to be onboarded else the initiative will fail. The manager is the fundamental link between strategy and execution. 

 

World-class Sales Manager:

Analyzed data on the following four parameters.

A>

1.     Management Fundamentals (Integrity, reliability, recognition, team building, listening skills)

a.     Accounts for 25% of Sales manager success

b.     Great reps don’t necessarily make a great manager.

c.     Find a new position that meets these abilities and screen up front.

B>

 

Sales (Account planning, Territory management, Level of innovation manager shows in positions offering). 

Account for 75% of sales m manager's success

a.      

b.     Selling

c.     Coaching

d.     Owning: Running their territory as if it was their own business 

 

2.     Attributes related to actual selling Ability (Negotiation skills, and unique perspective).

a.     Commander’s intent. Stop giving step-by-step instructions.

b.     Be a rep when needed.

c.     These attributes fall under three categories (Selling, coaching, and owning)

3.     Coaching skills (Follow through on development commitments).

a.     Accounts for 28% success.

b.     Leadership, Providing guidance. Demonstrate ownership of the business.

 

Sales Leadership is about how innovative sales managers are. Sales Innovation: What’s holding up the deal, and then finding creative ways to move forward. When and why the deal is running into trouble.

 

29% attributed to the manager’s success.

 

Best Manager comes from the Challenger category.

 

Sales Coaching:

 

Formalized Sales coaching improves sales rep performance in complex sales processes.

 

Design, diagnose and reinforce behaviour specific to the individual.

Coaching is ongoing; training is one-off. 

Coaching is customized to the rep’s individual needs.

Effective coaching is formal, structured and regularly scheduled.

 

Sales Innovation:

 

Three key Sales Innovation activities:

1.     Investigate: 

a.     to determine what’s blocking the sales, who’s involved, What decision criteria they will consider, what kind of financial concerns may get into our way

b.     Map customer’s decision-making process for any given deal which are stalled somewhere along the line

c.     Many times customers aren’t sure about the decision-making process

2.     Create Sales Solutions (innovative sales solutions). It’s not deal inspection Position Rep’s capabilities to better connect with supplier’s challenges.

a.     Shipping risks from customer to supplier for long terms contracts for long term contracts

b.     Cross sale optys

c.     Co-creation, thought partnership, working collaboratively. 

d.     Focus on deals where the stakes are high. 

3.     Share

a.     Replicate the innovation somewhere. Get scale from the efforts

b.     Develop and sustain strong relationships inside the organization

c.     Passing new ideas to the rest of the team 

 

Roles and Conflict

 

Efficiency focus (Territory management, resource allocation, right reps, right customer) vs effectiveness Focus 

 

Effectiveness focus is twice impactful as compared to efficiency focus. 

Innovation is the ability to thrive in the unknown world. 

 

Helping managers to understand their biases

 

Opening thinking, alternative thinking. 

1.     Practicability bias: unrealistic ideas should be eliminated

2.     Confirmation Bias: Unexplainable customer behaviours can be ignored

3.     Exportability Bias: If it didn’t work here, it wouldn't work anywhere 

4.     Legacy Bias: The way we have always done it. It must be best

5.     First Conclusion Bias: First offered solution must be the best.  

6.     Personal bias: I won't buy it, and the customer won’t buy it either.

  

Prompting Questions: (THE SCAMPER Framework. Brainstorming) 

            Expand ideas. What to do next? 

 

What else can be going on

 

What might be a substitute?

What ideas worked elsewhere can be considered?

What’s CFO, marketing must be thinking?

Questions set aside practicality: what would you do diff if you had more budget? 

 

Holding biases. 

 

Implementation Lessons from the early adapters: