00:41 Meet Lena Waters

01:11 AI Transformation as Debt

02:24 Headcount and AI Washing

03:30 From Efficiency to Strategy

04:44 Websites vs Agent Architecture

07:10 Agent Driven Buyer Journey

09:40 Marketers Minimum Playbook

13:05 Behavior and Liability Shifts

16:09 Rebuilding Marketing Org

17:38 Does Brand Still Matter

19:02 Key Takeaways and Wrap

02:32 Snowflake and AI

06:43 Choosing your model

07:44 Snowflake & OS

09:43 Innovations to reduce training data size

10:59 From large to small models

13:14 Snowflake and agentic systems

15:50 AI & data security

17:17 Access control layer

18:14 Embedded applications

19:55 Data sharing

21:37 Snowflake training & inference

23:12 Data reshaping

24:40 Structured versus unstructured model inputs

25:24 Models providing the mean v. exceptions

27:19 Vector databases

30:33 Summary

05:57 BI's Third Form

09:12 Future of BI

12:50 Data Quality in the World of LLMs

18:48 Building Resilient Data Pipelines & ETL

21:01 Evolving Role of BI Analysts

23:18 Data and Decision-Making

28:05 Conclusion

06:41 Ease of Use

08:54 Hybrid Architecture

10:30 Audience Question: LLMs for Onboarding?

12:55 Hybrid Architecture Enables New Software Design

16:58 DuckDB & ETL

19:24 Duck Puns

22:07 Duck Community

24:32 Summary

01:28 From Meta to Mysten

04:54 Why Develop a New Language, Move

11:15 Developer Response to Move

13:21 Zero Knowledge (ZK) Proof of Login

19:00 Kiosk

24:34 On Chain Storage Limitations

29:01 DAGs & Web3

30:01 Mysten Labs Ecosystem & SUI

34:03 SUI Token Launch

38:30 Closing

1. There is a pendulum between governance and self-serve, and that swing is narrowing with developments like Omni
2. There's a dynamic with centralization and decentralization hybrid execution at the edge which allows super-interactive user experiences. 
3. As these experiences improve, the number of people who benefit and can access data in meaningful ways only increases.

00:06 Introduction

01:27 Being Chief Analytics Officer

04:08 Evolution of BI

08:23  Data Organization Structure

10:53  Data Permissioning Philosophy

14:12  Hybrid Execution

17:36  BI Application Architecture

19:36 Mitigating Buyer Fatigue

21:20 BI & AI

25:59 Semantic Layer

27:12 Audience Question: Should AI Suggest Analyses?

28:43 Embedded Analytics

32:48 Will AI Automate BI Users Away?

35:00 Summary

02:10 Arbitrum Statistics 

02:47 Building Your Developer Community 

09:08 Arbitrum One v. Arbitrum Nova 

14:55 L3 & Customization 

19:41 Arbitrum Orbit & Chain Clusters 

24:39 Future Customizations for Developers 

28:30 Accepting Developer Languages: To reduce barriers to entry 

30:11 Accepting Developer Languages: To access legacy code 

32:38 Accepting Developer Languages: To reduce fees 

33:48 Convergence of Web2 and Web3

37:55 Community-Source-Software

42:03 Summary

01:47 Q: How did you decide to structure your team like software engg? 

05:07 Q: Determining the value of data 09:17 Structuring data teams: data PMs 

10:47 Q: Same data team responsible for internal v. external PRDs? 

11:14 Structuring data teams: data engineering 12:02 Q: What is a data product? 

12:47 Structuring data teams: data analysts 13:22 Structuring data teams: data governance 

13:37 Structuring data teams: data platform 

14:18 Q: What distinguishes a DPRD from a PRD?

17:33 Q: Role of the DPRD? 

20:05 Q: DPRD v. TEP? 

20:55 Demystifying data governance 

23:22 Data alert management - internal team and customers 

26:20 Q: Motivating ownership of data assets? 

28:51 Defining value of a data asset 

30:29 Measuring data usage 

31:37 Q: Can tools today handle the stochastic nature of data? 

33:28 Building a data team within the enterprise 

35:42 Q: How to test data products prior to release? 

40:00 Q: How do you use observability to manage diversity of alerts? 

41:51 Summary

02:54 What is Outbound Fury (OBF)? 

03:34 Inspiration for OBF 

05:03 OBF Tactics 

07:44 Determining the Line 

11:22 The Challenger Sale 

14:11 Personas & OBF 

16:05 Product-Led Growth (PLG), ABM & Outbound Fury 

19:35 Setting Up Your Team for Success 

21:54 Managing Internal Stakeholders 

25:28 Measuring Success 

27:16 Brand & OBF Campaigns 

29:25 Pricing in Marketing Considerations 

31:47 Analyst Community (e.g. Gartner) & OBF

33:43 Company Scale & OBF 

36:23 Conclusions 

Materials Mentioned in Today's Session: 

-- Raj Sarkar's Post: https://rajsarkar.substack.com/p/mark... 

-- Marc Benioff, Behind the Cloud https://www.amazon.com/Behind-Cloud-S... 

-- Matthew Dixon, The Challenger Sale https://www.amazon.com/The-Challenger...

03:07 Overview of PLG at Dropbox

05:30 Overview of PLG at Asana

06:07 How to succeed in PLG end user acquisition phase

09:15 Tactics for Generating Awareness

10:17 Customer Expansion Phase

13:15  When Tension Arises Between PLG & Enterprise Security Needs

15:24 Security is an All Consuming Roadmap, not a Feature

18:35 How the Organization Shifts during the Transition from PLG to SLG

20:48 How Pricing Changes from PLG to SLG

26:22 The PLG Trap

28:03 Avoiding the PLG Trap

31:55 Value-Based Selling: Generalizable or Vertical/Use-Case Specific?

35:35 Atlassian v. Asana's Approaches

37:11 Advice for New Startups Pursuing PLG

38:22 Navigating from SLG to PLG

42:19 Resources for Founders

43:06 PLG, SLG & AI

Bill & I exchanged emails about Deliberately Underselling as Sales Strategy. I asked him to share his views on land & expand team structure & quotas. But we covered much more. Here are three highlights from the session.

First, Deliberately Underselling means optimizing the sales process for Net Dollar Retention (NDR). Logo-based quotas focus the team on speed to close. Sometimes, these plans have a minimum contract value plus a bounty.

Another structure establishes land account executives & expand account executives. The company’s leadership should calculate sales efficiency on the combined OTE (on-target earnings) to quota ratio of these teams.

A land AE with a $300k OTE might have a $600k quota. Her land AE counterpart might also have a $300k OTE with a $2.8m quota. If they attain plan, the combined OTE/quota ratio is 0.176. Most startups operate between 0.15-0.25.

This land & expand team construct recognizes the difference in difficulty between landing & expanding accounts; also, the potential difference in ideal AE for each role. Last, the plan compensates those responsible for growing accounts with a quota - in line with Frank Slootman’s philosophy.

Second, Bill offered a bold prediction. Top startups will record 200-300% NDR as PLG becomes a dominant go-to-market strategy. Today, best-in-class tops out at 170% or so. We agree there!

Third, Bill revealed his Mojo Metric, his north-star metric. The Mojo Metric reports the net change in pipeline daily. Here’s how to calculate yours:

Mojo = new pipeline + new_pipeline_expanded + deals_pulled_forward deals_killed - deals_shrunk - deals_pushed

Each day’s Mojo reveals how much incremental pipeline the team has generated & informs the sales leader early on about this quarter’s health.

There’s much more in the session including handling commissions on multi-year deals (TCV vs ACV), criteria for evaluating ramping account executives that echoes insights from the Vista sales playbook, optimal ratios for team construction, how sales has changed in 30 years & how it changes after Covid, amongst other topics.

Throughout the hour, Lars provided insightful perspectives on how to build sales organizations. These the five most memorable takeaways for me.

In early-stage companies, founders sell for the first three to four quarters. Then, many founders opt to hire an AE. Hiring a sales or business-development representative (SDR/BDR) can be the better choice. Incoming account executives will want to see a significant lead volume before joining, especially when selling into the enterprise.

Teams often overlook storytelling as a critical part of effective lead generation. Fear-of-missing-out or the inspiration of a potential future, stories equip champions inside customer organizations to sell the product through the buying process. Founders validate the effectiveness of their stories when hiring SDRs better. SDRs call ten-times as many prospects as AEs do. Much the better to iterate with greater speed and confidence.

As the company grows, building the sales development team becomes the most productive source of pipeline particularly for enterprise-grade technical products. Hire for hunger. Then surround the new SDR/BDR with three pillars: strong training materials, a manager who cares about the employee’s success, and a peer to accelerate learning.

At Snowflake, sales development lives within the marketing team. Lars manages his team through a single metric, meetings. Getting to an account late, a few days or a week after they’ve signed with a competitor accrues to the meeting metric (see why in the video).

Last, exiting unlikely sales processes saves the company’s resources and boosts team morale. Closed - no decision is the worst outcome of an engagement.

We covered much more in the session including the techniques Snowflake uses to align account-based marketing with sales development & sales teams; how to structure career paths within the team; transitioning accounts between SDRs/BDRs to account executives; and the right SDR:AE ratios as companies scale.

Thank you, Lars, for the masterclass on sales development.