Learnings on enterprise sales – SVB and sales force workshop

Sales best practices for founders n SMB sales leaders

Actionable sales tips
– managing leads process
– build out sales stages n codifying it
    – impact 28% increase revenue
– boosting productivity
Sales force is helps users get data from spreadsheet into systems that allows easy sharing within the sales team -USD25/user/month
Partnership with companies to value add for their customers
– sales force partners with SVB to throw event to teach SVB clients how to better do sales. Helps with SVB retention.
Social expectation during a networking session is that you can approach people to talk. Start with a one liner if asked about your project
Sales force comes with Gmail integration
Social proofing: Video where users talk about the benefits they get using a tool

Alessandro Chesser, VP of Sales Carta

Make sure one person can bring in 100K ARR before replicate and scale up sales process

Key regret is not investing early enough in sales operations.

CRM system very important. Cleaning up the mess later is going to be a headache
Dealing with duplicate accounts are pretty painful.
Don’t over engineer sales process up front.
The more data you have the better your decisions. Create baseline and iterate over it over time.
Always iterate on the vision of the company and the pitch to figure out what resonates with your potential buyers
Smooth transition from inbound sales to outbound sales. Create demand from SDR first before hire sales team.
Ensure at least sales development representatives hit at least 60% their quota. If hitting below, it means u have over hired. It’ll create very bad culture like sales leads stealing if not hitting above quota.
Extremely important to make that SDR hire early. Utilize tools like OutReach.IO
Have sales people prioritize closing and not be the jack of all trades.
Marketing organization should have good process.

Mapping the hiring process for the sales leader.

The mistake is hiring really experienced and expensive people who are not willing to roll out his sleeves. You need someone who is really willing to get his hands dirty to go out to close sales. Industry experience is important, will ensure sales leader motivation level. Since he knows what is broken
Early in startup marketing and sales goes hand in hand. How to ensure no stepping on each other’s toes?

Balancing the different functions

 Need to balance load of marketing and sales organization. Make sure invest in marketing to generate more leads than sales can handle to ensure good culture
Utilized VC to generate inbound leads. The more money raised the more inbound network happens.
Hire more SDR generates leads.
Sales people want to focus on closing.
Successful SDR get promoted to becoming sales reps. They will be well positioned for success.
SDR – handle email marketing and phone calls. Uses pitchbook. Scrape Startup names and email addresses. Thousands of emails a day to generate leads.
On Closing Marc Benoff. When join eshare there was no product. Get them excited about the future of the company. The vision and the pitch is so important. Get their feedback
Pricing is important. Need make sure not too cheap. Perceived value is very important. Need to be more expensive than competition and explain the value to customers.
Don’t sell to far ahead and can’t deliver. Need to spend time with engineers to know what can be delivered. 1 to 2 months implementation cycle. Only do it when is early stage company but don’t over promise.
Charge via ACH instead of credit cards. ACH is more scalable, since the latter tends to expire.
When is Sales cycle replicable? Use revenue as signal. Driving 100K ARR per month is a good signal. Gut feeling. When deals are slipping
Organic signup forces u to put your whole pitch online for easy copying. A sales rep can sell the vision to extract higher margins. Deliberate back and forth. Use the demo form to generate leads for sales rep
Drowning in data. Word of mouth. Account based marketing. FireSign based use case.

Insights from dinner with Brian and Jason

Engineering compensation

  • 1% equity max for first 2 engineers.
    • 1% equity and low salary
    • 0.5% equity and medium salary
    • 0.25% equity and full salary
  • 0.5% equity max for second round of engineers
    • 0.5% equity and low salary
    • 0.25% equity and medium salary
    • 0.1% equity and full salary

Advisers compensation

  • Typically 0.5% to 0.25%

On managing the sales team

  • important to clearly defined guiding principles and policies up front
  • This will prevent sales representative from selling out of line and clashing with the Ops team leading to all round frustration.

Fund raising

  • can raise money from angels before there is even a product and distribution
  • Jason Calacanis invest in people who he feels are winners
  • Frame the opportunity and let investors fill in the details with their own models

Investors

  • Really open doors to help with follow on funding rounds
  • Brad slowly stepping away from USV to pursue his investment thesis in the crypto-currency space

Building

  • figure out what your super power is and quadruple down on it
  • always focus your time on the highest leverage activity
  • hire people to fill the positions and eventually replace yourself
  • practice discipline do not step across boundaries and get in the way of specialist. Focus on framing the problem and let the specialist define the solution
  • Use Slack – setup process to ensure information about sales process is disseminated to engineering team as well

On exiting

  • important to grow your personal network so that you can tap on it for key resources
  • seek out other operators within the space seeking to bolt on to their business model
  • company making 20K to pay their staff sufficient wage to continue working on the project
  • eventually find a new home of the team by selling off the company
  • figure out the key strengths of the CEO you hired and structure operations around them.

Related references

  • https://medium.com/@SiliconValleyGC/how-much-is-my-startup-advisor-worth-d97d825a6742
  • JoinMassive.com
  • Begin.com

Book summary AI super-powers, China, silicon valley and the new world order by Kai Fu Lee

The difference waves of AI

  • Internet AI – Facebook, Netflix, Google search
  • Business AI – Palantir
  • Perception AI – Tesla cars
  • Autonomous AI – Tesla self driving, Google self driving

Key locations

  • Silicon Valley
  • Zhong Guan Cun – Beijing

State of the Union

  • We are in the stage of implementation/application as opposed to RnD
    • having access to more data is more important than have expertise to do more RnD
    • having solid AI engineers is more important than AI researchers
  • We are still far from general AI
  • Key ingredients
    • data
    • computing
    • maybe work of strong AI algorithms engineers

Key differences between eco-systems

  • Silicon Valley businesses are mission and core values driven while Chinese businesses are pragmatically focused on profitability.
  • Silicon Valley businesses stay in bits and binaries offloading the brick and mortar to external vendors vendors while Chinese businesses extend their business model into the brick and mortar (online to offline)
  • Silicon valley prefers one size fit all strategy, Chinese businesses utilized localized solutions often investing/acquiring in local startups
  • Americans treat search engines like Yellow Pages (come and leave fast) while Chinese treat search engines like shopping mall (come to linger around long)
  • Silicon Valley is adversed to copying preferring to be unique Chinese business copy the heck out of each other

Chinese Advantage

  • Abundant data – quality and quantity aided by their online to offline initiatives
  • hungry entrepreneurs
  • AI scientist
  • AI friendly policy environment – strong emphasis by Chinese government
  • Hardware manufacturing know how – Shen Zhen
    • unparalleled supply chain flexibility – XiaoMi

Silicon Valley Advantage

  • Microchip manufacturing know-how

Trends within the Chinese eco-system

  • Darwinian eco-system has lead to extreme levels of competition
  • Chinese companies have already moved past the stage of clone Silicon Valley business models
  • Businesses innovate to build a defensive moat around themselves. Local businesses have advantage, with no timezone differences to deal with, decision making is relatively faster.
  • Online to offline
    • an essential ingredient to building strategic moats
    • caused the decline of cash use
  • Chinese government information systems will be able to leap frog US government information systems

Policy approaches

  • Google – impeccable safety
  • Tesla / China – trial by fire
  • key to winning the Autonomous AI race
    • is the bottleneck technology (Silicon Valley) or policy (China)?

Key concerns

  • having cheap labor is no longer going to be a source of advantage in a world heavily powered by automaton.  Developing countries hoping to employ this well tested strategy to progress will not be able to do so anymore
  • Estimated 60% potential job loss worldwide barring policy interventions
  • Job loss probability assessment
    • physical labor
      • environment – unstructured versus structure
      • tasks nature – level of dexterity versus high dexterity
    • cognitive labor
      • social – high versus low
      • cognitive – optimization based versus creativity/strategy based
  • AI replacement approach
    • single tasks approach
    • ground up rethink re-imagination
  • A population of irrelevant (no longer employable) as opposed to unemployed

Tackling Key concerns

  • Silicon valley – reduce, retrain and redistribute
  • Kai Fu Lee – stipends for care, service, education

New promise

  • Humans freed up from repetitive tasks can now focus on becoming more human oriented

Related readings

  • Disruptor, Zhou
  • www.Arvix.org – an online repository of scientific papers
  • Folding Beijing – Hao JingFang

Mark Zuckerberg chats with Yuval Noah Harrai on the Future of AI

Key take aways

  • Spread of inequality where some countries have the ability to harness AI while others don’t
  • AI based recommendation systems moving from being just an oracle to becoming a sovereign
  • AI as a tool is an amplifier
    • concerns that it will benefit totalitarianism more than democracy leading to totalitarianism becoming a more favorable governance model worldwide
    • surveillance
    • psychological manipulation – the inability to know your true self through your thoughts
    • what happens if morality and expediency diverge when it comes to governance
  • Effectiveness of curbing the negative effects of AI by encoding values within policy frameworks governing these AI based systems
    • Companies based in Democratic countries will encode democratic values within their systems vice versa for Totalitarian countries
  • Personalization versus Fragmentation
    • when everyone in a country chooses his own community that is mainly online there is no longer a glue holding the local community together
  • Long term versus short term
    • The long term benefits might come sooner than expected when taking a short term trade off

 

Book summary – Range

  • specialists by instinct will grip tighter to their core tools when the environment becomes more chaotic
  • Repetition to optimize for efficiency is only suitable for a predictable environment
  • in a chaotic environment being able to draw solutions from a wider range of domains will lead to qualitatively better and more innovative break through solutions
  • build culture that encourage dissent rather than blind adherence
  • build teams that are deeply networked rather than hierarchical

Non-purchase of DOMO despite 10% dip

11th June 2019 DOMO 10% share price dip

Reasons for non-purchase

  • Macro economic environment is still uncertain given US/China Trade war where leaders are set to meet during the 28th-29th June 2019 G20 Osaka summit.
  • US Treasury yield curve is currently inverted signaling a forthcoming recession
  • DOMO share price has been on a steady trend for past 3 months.
  • Competition purchases
    • Google has recently purchased Looker
    • SalesForce has recently purchased Tableau
  • Financials
    • Cash and cash equivalent is down by more than 50%
    • Total assets is down by more than 10% while total liabilities is down by only 3%

Related References

  • https://www.fool.com/investing/2019/06/07/why-domo-shares-plunged-today.aspx
  • https://www.globenewswire.com/news-release/2019/06/06/1865628/0/en/Domo-Announces-Fiscal-2020-First-Quarter-Financial-Results.html

Book summary: The essential Drucker

Management: make people capable of working together to respond to change in the environment through:

  • common goals
  • common values
  • the right structure
  • proper training and development

Reasons for failure

  • Not innovating
  • inability to manage innovation

The goal of any business is to create a customer. A business does so by producing generate products and services the community wants in exchange for profits to sustain continued operation.

The goal of marketing is to know understand and know the customer so well the product sells itself

The Purpose of a business is the change it wants to effect in the community

The Mission is what it wants to do to effect the change

The Objectives are key tasks it will execute upon to achieve the mission.

Types of innovation

  • Product innovation
  • Social innovation
  • Management innovation

Waste as little effort as possible on areas of low competence.

First figure out how you learn to figure out how you perform.

Number two person often fails in number one position because top spot requires a decision maker.

Effective people are perpetually working on time management.

The first rule of decision is one does not make a decision unless there is a disagreement.

Determine the right organization size to fit the requirements of the mission.

Insights from Klaren’s birthday

Conversations with Yi (EverString)

The forthcoming trend for engineering

Machine learning is increasingly becoming commoditized. DevOps becomes more important. Demand for specialized service where DevOps is encapsulated will further increase as demand for engineering tasks further outstrips engineering supplies.

On lead generation market

Companies in the lead generation space have need for scalable web crawlers. This helps offset the cost of retaining three in-house engineers.

Lead generation space has consolidated. There were priorly 120k such companies. There is 7k companies in operation. Majority of players are generating leads by scraping LinkedIn.

Consumer space require constant development of new features. Enterprise space requires service heavy. Enterprise space requires not just lead generation but entire channel marketing service suit (physical mail, online advertising, email marketing)

Lead gen hard to retain. The list becomes less valuable once it’s been used. 80% yearly churn is normal. One company reduces yearly churn to just 10% this by reducing second year subscription from USD800/yr to USD200/yr. further discount to USD100/yr if they don’t like. Recurring service is for grabbing fresh leads from same data source.

On Tele conference

Zoom’s product team compared with UberConference has developed a better understanding of the true conference needs of their users in various context. They have worked harder to ensure their product work seamlessly in identified scenarios. A typical example is the ability to join s conference bybthe press of a button on their mobile phone while driving instead of having to type the typical 4 pin digits.

Insights from dinner with Yuan

Conversations with Wang Ji Lei (Google)

China’s 996 practice has allowed China to close the 30 years technology gap between China and the US within the past 5 years. Some segments like cashless commerce have already surpassed US. The norm is some labor regulations are considered formalities and adherence is not necessary.

China’s distribution path starts from Beijing, 1st tier cities, 2nd tier cities, 3rd tier cities and finally remote villages. While double digits growth has been the norm over the past five years, market saturation has been achieved within the Chinese digital space and most apps are having problems growing.

Baidu’s Ads revenue model via search is increasingly being disrupted by Tic Toc. They have lost 50% ofvtheir search revenue last year. Similar trend is being observed with Google’s Ad revenue model which is bring increasingly disrupted by Amazon in the below USD75 consumer goods segment, Facebook between the USD75 and USD2000 consumer goods segment.

Tic Toc and WeChat dominate the bulk of the mobile usage bandwidth in China. Tic Toc with string positions within the short video entertainment segment while WeChat in the social conversations segment. Both companies face significant challenges encroaching into the other’s territory.

Tic Toc is the only Chinese mobile entertainment app that has made significant headway out of China. They have a well defined playbook which specialized tiger teams follow to gain traction in new territories. Maintenance is transferred to local team once penetration threshold is achieved. Due to fickle nature of users, retention remains the number 1 challenge. Position can be easily displaced by new entrants. Thebunderlyimg thesis is that city dwellers have lots of fragmented time spread across a day. Each video is restricted to 45secs to reduce anxiety that user has to stop watching between engagements. This also helps induce the habit of chipping in between engagements.

Typical engineer’s salary in Beijing is USD50,000 per year while a house cost around USD2million. While typical engineering salary in Silicon Valley is at least USD100,000 per year while house cost at least USD1 million. Wealth is mainly retained at the upper levels in China. Given the ratio of salary to house price as well as the comparative working hours, there is significant pull of talents to Silicon Valley.

Average green card waiting time for EB1 is now five since it’s been expended to include executives.

Major housing development is occurring in South Bay given huge influx of population and hiring by google (50,000 people) and other giants. Heavy recruitment is happening as Google continues to build out its cloud team.

Google cloud’s market share continues to lag with Amazon leading, Microsoft Azure in Second and AliCloud 3rd. Google cloud organization is facing an inflection point as upper management faces difficulties with zeroing in on a clear strategy. The symptoms are the dilemma between engineering culture (Google Default) versus customer culture (Amazon) and SMB versu Enterprise. Google’s traditional engineering culture has the effect of causing company to drift towards heavy engineering solutions which might be an overkill for SMB needs coupled with a lot of wastage which no one needs. This opens up a gap for customer focused companies.

Tesla autonomy

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

The Mission

Building and optimizing the entire infrastructure (hardware and software) from ground up with autonomous self driving as the mission

Mission and decision making

Design decisions are made with trade off between functionality and cost to achieve the mission while keeping cost in control

  • Lidar is not useful when cameras are available
  • driving cars with HD mapping makes the entire operation brittle since actual road conditions can change

Operating structure

  • Data Team
  • Hardware Team
  • Software Team

The Data model

  • Cars on roads are constantly collecting new data
  • New data is being utilized to train and improve neural network model
  • New improved model is constantly being deployed back to the car to improve self driving
  • Real world data provides visibility into long tail scenarios that simulated data cannot. Simulating long tail scenario is an intractable problem
  • Balancing between data model and software
    • Neural network is suitable for problems that are hard to solve by defining functions / heuristics
    • Simple heuristics are better handled through coding in software

Future revenue model

Robo-taxi that will disrupt the ride-sharing space.

  • Consumer car – USD0.60 / mile
  • Ride sharing – USD 2-3 / mile
  • Telsa Network – USD 0.18 / mile

Main challenges:

  • Legal – need more data and processing time to get approved
  • Battery capacity
  • Social norms around robo taxi