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Leads Recommendation Engine, leading the wave for Prescriptive Sales and Marketing 3.0 -- Lean LaunchPad course by Steve Blank Spring 2014

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1

Lean LaunchPadSpring 2014

Recommendation Engine for Lead

GenerationLeading the wave towards Sales & Marketing 3.0

2

Team

Mukesh Ranjan Designer• EW MBA 2015• PhD , CS – U. Cincinnati• Program Manager, Intel

Mohamad Charafeddine Picker• EW MBA 2015• PhD, Elec. Eng. – Stanford

U.• Principal Solutions

Architect, ASSIA Inc

Kartik Shah Hacker• EW MBA 2014• MS, CS – USC• Software Engineer,

Cisco

Swaroop SayeramHacker, Hustler• EW MBA 2015• MS, CS – U. Wisconsin - Madison• Group Product Manager, McAfee

Steve AdelmanManaging DirectorNexus Partners

Peter LeeVP, Bessemer Venture Partners

Mentors

Dheera Prabhakar HackerMIMS Student, UC Berkeley

4

Inno

vatio

n

Time

Sales Automation: SalesforceMarketing Automation: Marketo, Eloqua,

Hubspot, Act-on

2.0 | DescriptiveWhat’s happening?

Vision | Sales & Marketing 3.0

CloudSlate,Infer, Mintigo,Lattice Engine,..

3.0 | PrescriptiveWhat to do?

Big Data Explosion

Cloud Computing

Large Scale Machine Learning

5

• 102 Interviews in 10 weeks

• 62 Unique Companies

• 39 CEOs/Founders, CTOs,

(S)VPs, Directors

• 4 Interested in a paid

high-volume trial

• 1 Pilot

6

Value Prop | Learning & Evolution

Database of companies and

installed technologies

Lead Management

and Scoring of Leads in the

Funnel

New Leads Recommendation

“Lookalikes” based on existing

customer base

Finding Influencers and

Decision Makers

Busines

s

15+ players in Lead Management

7

MVP | Applied to McAfee Security productActual real dataMcAfee’s data is obfuscated

8

Cust. Segments | Learning & Evolution

Sales Ops Dept

Demand Genwithin Marketing

Mid-market & including big companies with

specific product segments (like Cisco)

Sales Dept

Marketing Dept

for B2B focusing on mid-market & excluding big

oligopolies

Week 1

Week 10

Archetypes

in Appendix

9

Channels | Evolution & Learning

Week 1

Own website

Sales partnering with marketing

automation companies

Week 3-10

Salesforce AppExchange

Marketo LaunchPoint

Eloqua AppCloud

Salesforce takes 15-25% Rev share.

Eloqua and Marketo channels are free – in order to attract adopters.

10

Cust. Relationship | Learning

Network

Effect

Algorithm performance improves the more the customer uses it.

Performance also improves as other “similar” customers use CloudSlate –

CloudSlate owns the models and does clustering on its own customers to run parallel learning experiments Vs serial.

11

Revenue & Pricing | Evolution & Learning

Revenue Strategy

Subscription

• Predictable budget forecast

Conversion based: End-to-

End• Unpredictable

budget forecast

• Complex

Conversion based: stage-

wise• Unpredictable

budget forecast

• Complex

Per Lead

• Low quality perception

Pricing Tactic

Con

vers

ion

R

ate

w/ CloudSl

ate

w/o CloudSla

te

CloudSlate Value

Add

30% Commission

to CloudSlateA

vg

. C

.A.C

($

)

w/ CloudSl

ate

w/o CloudSla

te

C.A.C Saving

Computed from backtesting on SalesForce data & Quarterly A/B Testing

Value-based dynamic pricing

12

Partners & Data Suppliers | Evolution

Quality

Quantity

Source: Chiefmartec.com

13

Partners & Data Suppliers | Learning

Data Suppliers (Content)

Algorithms Layer

Negotiation power now

Data Suppliers (Content) Algorithm

s Layer

Negotiation power in the future

Data will get more restricted or more expensive in

the future strategic plan to create our own data

(similar to Netflix’s content strategy).

14

Activities & Resources | Learning

Algorithms

Data Sources

Delivery

Dat

a Sc

ient

ist

Machine Learning Eng

Des

igne

r

Phase I: High-Volume MVP in 3 months

Phase II: Salesforce AppExchange and Marketo LaunchPoint

Metrics that Matter

Annual Revenue per

Customer$50k-$120K

Annual COGS per Customer

$33K

15

CloudSlate

Amplification

Factor

3x Y14x Y3

Annual Churn Rate

20% Initially10% Steady

State

LTV > $250k($50K gross

profit x 5 yrs)

C.A.C for CloudSlate

$5K(Salesforce) to $16K (Marketo)

(less in steady state)

16

What’s Next?

We believe strongly there is real opportunity!

1. Develop high-volume prototype over the next 3 months

working closely with McAfee

2. Then, re-engage with the early adopter pipeline - Cisco,

Adaptive Insights, and Gainsight

17

THANK YOU! Teaching Team

Ryan Jung

Mentors

Awesome Classmates!

18

APPENDIX

Lookalike Modeling

Company: size, location,Vertical, Growth, etc.Professionals: Names, Titles, Dept, Email, Phone #

Company: Technologies used/installed , Products, Solutions, Patents, “About Us”, Full website content

Company: Financial health, Financialtrend, key events/triggersDemographics

Company: Social sentiment, news storiesProfessionals: What they said, liked, read, followed, attended, and their influence Klout

CustomerInterests &

Behavioral SignalsMechanics

Financials

19

Existing Customer Base

Attributes Augmentation

Segmentation/Clustering

Archetypes Extraction

Discoveryof new

Leads w/ similar

ArchetypesAttributes

Bank of augmented leads

(Dynamic, constantly evolving)

Data supplied by our partners and key data

resources

New Look-alike

LeadsMarketing & Sales

Funnel

20

21

High Level Overview

CloudSlate

Customer Archetypes

Account Manager

(B2B company)

SVP Marketing (B2B company)

Sr Dir Sales (B2B company)

Demand Gen Manager

(B2B company)

Pain

Quote

Pain Meter 5 4 3 4

User Buyer

Influencer

Decision Maker

Dave Jill Dan Susan

Cust. Segments | Archetypes

Most leads in the system are

useless

ROI from lead gen campaigns is

weak

Pressure to meet Sales target

Collects leads through laborious

methods, but Sales never seem to convert them

“Only 40% of the leads I close are provided by

Marketing. I have to hunt for the other 60%

myself”

“My marketing budget is just

$250K/quarter, and I can get

very little done with it”

“I don't want my sales reps

wasting their time chasing dead-ends”

“Difficult to satisfy Sales”

23

Finance and Operations TimelineC

ash

Dev.

Channel

&

Cust

om

er

Financi

al

Year 1Q1 Q2 Q3 Q4

Year 2Q1 Q2 Q3 Q4

Year 3Q1 Q2 Q3 Q4

Seed $1M

Series A $3M

Amplification = 3x

Salesforce AppExchan

ge

Demographic, Tech Install Data

Marketo Launchpoi

nt

Behavioral, Business News &

Financial Data

Amplification = 3.5x Amplification = 4x

Jobs postsDevelop Tech Install

Database

Microsoft Dynamics, Oracle

CRM, WebsiteFirst 5

cust., $50k Rev

Next 10 cust., $500k

Rev

Next 25 cust., $1.5M+ Rev

Eloqua, Sugar CRM

Next 40 cust., $5M+ Rev; Profitable

24

Why we chose the Lean LaunchPad

Not because it is easy, but because it is hard, because that goal will serve to organize and measure the best of our energies and skills, because that challenge is one that we are willing to accept, one we are unwilling to postpone, and one which we intend to win…

Borrowed from John F. Kennedy “We Choose to go the Moon” speech in Sept 1962

25

Week 1

26

Week 2

27

Week 3

Week 4

Week 5

Week 6

Week 7

32

Week 8

33

Week 9

34

Week 10

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