Learnings on enterprise sales – SVB and SalesForce workshop

General observation on networking

  • There were a total of 40 attendees and we received a total of 2 name cards – net conversion rate 5%
  • Social expectation during a networking session is that you can approach people to talk
  • Start with a clearly one liner if asked about your project
  • actively direct the conversation to them and spend more time listening
  • make sure to bring name cards

Compare and contrast small businesses and medium sized businesses

  • Small businesses
    • acquiring new customers
    • accessing to investment capital
    • not enough time
  • Medium sized business
    • acquiring new customers
    • achieving work life balance
    • not enough time

Sales best practices for founders n SMB sales leaders

managing leads process

  • Develop a concrete definition of a lead and make sure all employees understand it.
  • Install an effective Customer Relationship Management (CRM) Tool.
  • Track the source.
  • Distribute your leads quickly.
  • Nurture your leads and get your Sales team excited about every prospect.
  • Treat your prospects like customers.
  • Measure everything you do.
  • Hold regular meetings with your sales staff and anyone else involved in the sales process.

build out sales stages n codifying it

standardized sales process see up to 28% increase revenue

  • A consistent schedule: You should know when and how often you are going to be
    performing your sales activities.
  • A strong message. You should know what you are going to say and at what point in the
    process you are going to say it.
  • Mixed media plan. Use multiple channels to convey your message and mix it up – emails and phone calls are the most common, but perhaps it’s appropriate to reach your potential customers on a favorite social channel.

focus on boosting sales rep productivity

  • Make ongoing sales coaching a priority.
  • Advance prospects faster with Value.
  • Evaluate & re-evaluate sales processes.
  • Embrace Automation and technology.
  • Use Analytics to always be improving.

Observations on Sales force

  • UVP helps users get data from spreadsheet into systems that allows easy sharing within the sales team
  • Sales force comes with Gmail integration
    • disrupted CollaSpot’s business model
  • Social proofing: Video where users talk about the benefits they get using a tool
  • Extending existing business lines
    • current base – market segment with higher margins using enterprise sales acquisition strategy
    • new business line market segment with lower margins using self service model
      • USD25/user/mth package self service tier
      • acquisition strategy is not well defined yet
  • 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.
    • tap into Silicon Valley Bank’s extensive distribution network

Alessandro Chesser, VP of Sales Carta

Key regrets

  • 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.

Enterprise sales versus organic adoption

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

Getting the enterprise sales engine started

  • The more data you have the better your decisions. Create baseline and iterate over it over time.
  • Marc Benoff on Closing. When he joined eshare there was no product. His job was to get potential customers excited about the future of their own company. The vision and the pitch is so important. Get their feedbacks.
  • Don’t sell to far ahead and can’t deliver. Need to spend time with engineers to know what can be delivered. Always keep in mind a 1 to 2 months implementation cycle. Only sell ahead when is early stage company but don’t over promise. If failed to deliver, will create bad PR
  • Always iterate on the vision of the company and the pitch to figure out what resonates with your potential buyers
  • Early stage startups can utilized VC to generate inbound leads. The more money raised the more inbound happens with PR that follow each fund raising events

Scaling the sales organization

  • Make sure one person can bring in 100K ARR before replicate and scale up sales process
  • When is Sales cycle replicable? Use revenue as signal. Driving 100K ARR per month is a good signal. Gut feeling. When deals leads are starting to slip through the cracks
  • Charge via ACH instead of credit cards. ACH is more scalable, since the latter tends to expire.

Structuring and managing the sales team

  • For smooth transition from inbound sales to outbound sales first create demand from SDR first before hire sales team.
    • Not all sales people are comfortable with generating sales leads
  • Extremely important to make that SDR hire early. Utilize tools like OutReach.IO
  • SDR – handle email marketing and phone calls. Uses pitchbook. Scrape Startup names and email addresses. Thousands of emails a day to generate leads.
  • 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.
  • Have sales people prioritize and focus on closing and not be the jack of all trades.
  • Promote Successful SDR  to become sales reps. They will be well positioned for success.

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

Balancing the functions of marketing and sales

  • Early in startup marketing and sales goes hand in hand. How to ensure no stepping on each other’s toes?
  • Marketing organization should have good process too
  • Need to balance load of marketing and sales organization. Make sure invest more heavily in marketing to generate more leads than sales can handle to ensure good culture.

On Pricing

Pricing is important. Need to make sure not too cheap. Perceived value is very important. Need to be more expensive than competition and explain the value clearly to targeted subset of customers.

Learnings from Loominance

 UVP to attract customers via word of mouth: They need us when they are drowning in data.
Account based marketing
  • Scrap company websites, identify and recommend similar companies as sales leads to clients

Related readings

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
  • SiliconValley Banks has established fund for investment purposes

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

Insights from Josh

Product

  • Starts with search
  • once query and corresponding is good visualize using graph which users can easily zoom in and out

Key APIs

Key data pipeline technology

  • Apache Spark
  • Apache Kafka
  • Elastic Search
  • GPL visualization

Acquisition strategy

  • Enterprise sales
  • Hire users from customers to act as salesman to other users who are in their network

Key challenge

  • Business is stuck at USD30 million ceiling per year right now
  • Trying to build more granular apps to target verticals to generate more profits

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

How banks and the Federal reserve / central bank works

On US banks

  • they pay interest on deposits from customers and either borrow out the money to lenses or purchase short term US treasury . The spread between deposit interest rate paid to customers and US treasury yield/loan interest rate charged to lender is their profit
  • they charge lenders interest above long term treasury yield rate and finance the loan through either their own deposits or from borrowing
  • US Banks with deposits above USD122.3million needs to meet minimum reserve requirements of 10% imposed by the Federal reserve as of 2018

Meeting minimum reserve requirements

  • Borrow from Federal Fund Rate based on central bank interest rates
    • Used within US economy
    • rates are higher
    • hassle free
    • The federal funds rate is set in U.S. dollars and
    • charged on overnight loans.
    • The fed funds rate is the interest rate at which commercial banks in the US lend reserves to one another on an overnight basis
  • London Interbank Offered Rate (LIBOR) –
    • Used internationally
    • Borrow from other banks
    • rates are lower based on global supply and demand equilibrium
    • based on USD, EURO, Sterling, Swiss Franc, Yen
    • Quotations:
      • overnight, one week, and
      • one, two, three, six, and 12 months.

Federal reserve debt structure

  • US treasury bills:
    • short term maturity at one year or less.
    • Sold at discount
    • paid fully at maturity
  • US treasury notes:
    • 1 year to 9 years maturity
    • Sold at face value
    • pays fixed interest rates every six months.
    • Sold auction style.
  • US treasury bonds:
    • 10 years to 30 years maturity.
    • Sold at face value and
    • pays fixed interest rates every six months .
    • The original vehicle.
    • Registered to single owner and cannot be resold.

Federal Interest rate hike

  • Long term interest rate tend to react faster to hikes then short term interest rates
  • Long term Federal interest rates are used as benchmarks by banks to determine interest to charge lenders.
  • To prevent hyper inflation (price stability) after all employable people within the country have been employed into the economy.

Understanding the yield curve

  • Interest rates are considered the cost of money
  • The Federal reserve only manipulates the overnight interest rate
  • Longer term interest rates are determine by demand and supply of money
  • The Federal reserve increases the overnight interest rate by reducing the supply of money in circulation. This is achieved by supplying more short term securities in the open market, .
  • The Federal reserve decreases the overnight interest rate by increasing the supply of money in circulation. This is achieved by buying up short term securities in the open market.
  • Long term interest rates are higher than short term interest rates because long term interest rates require you to endure greater interest-rate uncertainty as well as greater likelihood of government default

Related readings

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

 

ETFs disrupts hedge funds and democratizes playing field for individuals while driving demand for alternate data

Overview

We observe the following impact to be true for ETFs

  • democratizing access for individuals to positions traditionally accessible only to  hedge fund managers
  • disrupting hedge fund business models
  • increasing population of investors with passively managed portfolios
  • giving tech savvy individuals with finance background an edge by reducing active professional competition

Related references

At the surface level we observe hedge fund managers flocking to alternative data to seek alpha

A deeper dive shows this trend to be driven by the deterioration of the sector over the past few years as more investors shift into ETFs.

Survivors are struggling with transition to increased data intensity.

Even Goldman Sachs embraces the trend by going on a hiring spree to recruit coders as opposed to traditional traders.

As this trend of disruption forces more hedge funds to shutdown, a unexpected gap opens up in the field.

It more than levels the playing field for individuals that are savvy in both tech and finance

On the flip side, it can be interpreted that a bubble is forming within ETFs sector similar to the subprime CDO bubble that popped back in 2008

 

Trends associated with GetData.IO

Alternative Data

Robotic Process Automation

Analysis of the Facebook Libra Token

High level

  • The launching of Libra Token will allow large swath of people access to banking
  • It will also allow corporations with a huge stock pile of cash the access to alternate forms of investment

Libra currency liquidity

Every Libra token that gets created is backed by a reserve of real assets. Close examination of partner balance sheet figures shows approximately USD148 billion dollars of cash and equivalent available for deployment right out the gates.

Libra social impact

One of Libra’s goals is to provide banking access to segments of the world’s population that don’t. Close examination of partners’ reach to this segment of the world shows 7.9million people. This is not including the 204 million African Internet Users on Facebook.

Related References

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