1. Personalized B2B Selling &
Marketing @ Salesforce
ODSC
Dilip Patel, @dilip817
Senior Manager, Sales & Marketing Data Science
Markus Anderle, @MarkusAnderle
Director, Sales & Marketing Data Science
11/06/2016

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3. Table of Contents
Motivation
Data Science to Personalize B2B Selling & Marketing
Outcome: Integrated Sales and Marketing effectiveness
Q&A

4. Motivation

5. 2013 • 2014 • 2015 2008 • 2009 • 2010
2011 • 2012 • 2013
2014 • 2015
2011 • 2012
2013 • 2014
2015
Most
innovative
companies in
the world
20K
FY16 Employees
Salesforce: 4th Largest Enterprise Software Company in
the World This Year
4th largest software company based on
analyst consensus revenue for FY2017.
Salesforce fiscal 2017 guidance provided
November 18, 2015: "revenue for the
company's full fiscal year 2017 is
projected to be approximately $8.0B to
$8.1B.”
$6.67B
FY16 annual revenue

6. Salesforce Customer Success Platform

7. How do we scale and efficiently run sales and marketing
functions of a hyper growth enterprise SaaS company ?
Accelerate Growth of Sales
• Sell to new and existing customers
• Sell from large portfolio of available clouds
to meet client needs
Improve Account Executive Productivity
• Provide focus, information and tools that
help close a deal and helps teams meet
quota
​Just adding more AEs is not scalable

8. Data Science to
Personalize B2B Selling &
Marketing

9. Can we apply techniques that work for millions of customers
and billions of products in B2C world ?

10. We envisioned to create a personalized Account Intelligence
Application
Product 2
Peter Schmidt
VP of Service
Birds of a Feather, Atlanta 2014.04.03
Service Cloud
*****
90%
$650K
$240K (Service Cloud)
$500K
450 Sales Cloud (2015.07.06)
2016.07.06
Parker’s Tools and Technology
Steel Break Inc.
Product 3
Payers
Shawna Rocha
CIO
CxO Dinner, Atlanta 2015.09.12
Ore and More Inc.
Product 1
Community Cloud
****
72%
App Cloud
***
32%
E-Commerce Wealth Management
Cloud
Segment
Score
Key Persona
Last Contact
Open Pipe
Bookings Pot
Last Purchase
Renewal
$650K
$120K (Community Cloud)
$350K
450 Sales Cloud (2015.07.06)
2016.07.06
$650K
$130K (App Cloud)
$650K
450 Sales Cloud (2015.07.06)
2016.07.06
Similar
Accounts
Peter Schmidt
VP of Service
Birds of a Feather, Atlanta 2014.04.03
Metalworks Support Services
Steel Break Inc.
Industry
Note: All accounts are fictional

11. To begin with, we segmented accounts based on their
Propensity to Buy $X in a defined time frame
HighPTBLowPTB
Medium Term | High Spend Near Term | High Spend
Unknown Term | Unknown Spend Near Term | Some Spend
$X K+ ACV
Next 4 Q’s
$X K+ ACV
Next 6-8 Q’s
<$X K
Unknown
Horizon
<$X K
Next 4 Q’s
High Potential
Low Potential
Short-TermMid-, Long-Term
TLC for Growth Ready for Take-Off
Strangers Run Rate
Notes: “Near Term” defined as Next 4 Q’s; “Medium term” defined as Next 6-8 Q’s; ACV: New New Sales; All numbers are representative

12. However, we found that each customer follow an unique
product journey
First
Purchase
Cross sell Buy
Add-on
Upsell Renewal/
Retention
Sales
Cloud
Service
Cloud
Community
Marketing
Cloud
Apps

13. To offer a right product at right time, we added customer
journey to propensity segments
First
Purchase
Cross sell Buy
Add-on
Upsell Renewal/
Retention
Sales
Cloud
Service
Cloud
Community
Marketing
Cloud
Apps
High Propensity to Buy/Renew
Low Propensity to Buy
Model Provides:

14. And recommended optimal path that maximizes Customer
Success and ACV for Salesforce
First
Purchase
Cross sell Buy
Add-on
Upsell Renewal/
Retention
Sales
Cloud
Service
Cloud
Community
Marketing
Cloud
Apps
Recommended path
Not recommended
path
AE Executes:
High Propensity to Buy/Renew
Low Propensity to Buy
Model Provides:

15. Interactions during Customer Journey provided attributes
for modeling
​Interview with Marketing, Sales (AE/SEs), PMs and PMMs to generate features
“Propensity to Buy” indicator Example metrics
Depth of partnership with Salesforce AOV, License Utilization, Edition
TTM Pipegen, TTM Total Net New Sale
FTM OP, AOV momentum
Login %, # Cases,
# Customer Apps & Objects
Recent Sales Momentum
Product Usage behavior
Industry sector, Industry summary
# Employees, # Total Licenses
Community Product Whitespace &
Firmographics
Friendly CIO, Competition, Purchase triggers,
Past history, Financial performanceField intelligence (Qualitative)
Intent data Web search, Social media, Blog etc.

16. Technical Approaches for Predictive Selling
Approach How
Cloud
Segment
Score
Binary and multi-class
classification using
Random Forests.
Asymmetric cost for false positives
and false negatives, sampling for
unbalanced data sets
Similar
Accounts
Nearest neighbor
similarity
User defined weighted distance
metrics for categorical and
continuous features
No
Revenue
Lost
Wasted
Effort
Oppor-
tunities
Discovered
Missed
Oppor-
tunities
Yes No
NoYes
Actual Purchase
Predicted Purchase

17. Predictive Model Performance – All Products
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
% ACV Captured
% Accounts Tagged

18. 18
Sales
Account Prioritization
Next Best Product
Onboarding
Whitespace Analysis
Territory Carving
Sales Story Creation
Marketing
Bill of Material definition for
Line of Business
Journey Design
Next Best Product and Message
Content and Channel
Personalization
Contact selection for Line of
Business
Lead Scoring
Sales and Marketing Alignment
Use Cases and Benefits for Sales and Marketing

19. Summary & Outlook
• While B2C and B2B share some of the same characteristics (customers, products, …), the
scaling of a sales team is different compared to scaling recommendations to customers
directly
• The customer journey has a strong sequential component, each one requiring a
specialized modeling approach, depending on the current and past states of a customer
• Next steps include:
• Refining of journey modeling additional sequential modeling steps
• Combining predictive modeling with a local similar account identification
• Provide explanation capabilities

20. Q&A

21. thank y u