The document discusses how sales development representatives (SDRs) face challenges with low conversion rates, which leads them to increase lead volume at the cost of quality. It suggests using data and automation to more accurately determine lead fit and intent in order to take the right actions like outreach at optimal times. This helps improve conversion rates over relying solely on human sales reps. Machine learning models can predict lead value and costs to optimize marketing spend across channels like email, advertising, and gifts based on intent signals. The goal is to apply successful consumer marketing strategies to business-to-business sales automation.

2. What does a Good SDR do?

3. Data collection & entry
Lead Qualification
Book meeting

4. SDRs are facing a world of low conversion rates
So they increase volume.

5. One things humans aren’t good at?
Maintain quality work while volume increases

6. SalesVelocityin$/month
Company size
5 50 200 1,000 10,000
lead value
Cost of sales
leadsProfitable
CAC > COS

7. SalesVelocityin$/month
Company size
5 50 200 1,000 10,000
lead value
Cost of sales
leadsUnprofitable
CAC < COS

8. But you don’t know which is which before engaging

9. SalesVelocityin$/month
Company size
5 50 200 1,000 10,000
Sales Velocity
Cost of sales

10. SalesVelocityin$/month
Company size
5 50 200 1,000 10,000
Sales Velocity
Cost of sales
leadsProfitable

11. We marketers need to own this.
We’ve created this world where
lead quantity > lead quality

12. Q: Who is a SaaS buyer?
@guillaumecabane #SYNAPSE

13. Q: Who enjoys buying SaaS?
@guillaumecabane #SYNAPSE

14. 2015 Forrester survey:
53% of B2B buyers prefer researching data on
their own vs talking to a sales rep

15. In 2017? 68%
They called that research “the death of the B2B
Salesmen”

16. 70% of B2B buyers start with a generic search query
rather than a brand
2015 HBR study

17. Truth:
The last place people go for a purchasing
decision : your sales reps

18. What’s good at handling large volumes of low frequency events?
Code
What’s good at creating a consistent experience?
Code

19. data + engineering is eating sales.
And I like it
Let me show you how we do it

20. 0%
19%
38%
56%
75%
Low Medium Good Very Good Low Medium Good Very Good
Won Opportunities
MadKudu Customer Fit
Percentage of Lead Types
79% of won opportunities
16% of leads make up

21. SalesVelocityin$/month
Company size
5 50 200 1,000 10,000
Sales Velocity
Cost of sales

22. Company size
5 50 200 1,000 10,000
Enterprise
ABM
Self-Service
Marketing
Automation

23. Likelihood to buy
Competitors budget per lead
Our budget per lead

24. Because we are bidding the right amount ...
we can spend more on the right people when our
competitors can’t.
We won’t spam with emails or ads people who
aren’t likely to buy our products.

25. There is no insane innovation going on here.
All of this what derived from B2C bid optimization /
retargeting

26. Look at your funnel. Where does it start?

27. Acquisition
Engagement
Signup
Activate
Conversion
@guillaumecabane #SYNAPSE

28. Code is moving up the food chain

29. Acquisition
Engagement
Signup
Activate
Conversion
What happens before?

30. B2B Buyers do
12 searches before engaging with a brand
2015 Google Insights team study

31. User fit
User
Behavior
time
1
0.2
Conversion event
Likelihood to
Convert (p)
Visit my
website
Visit 3rd
party websites
Assisted discovery (nurture campaigns)
Register for
trial
Self discovery of
product
Discovery of
Market
Fit & Behavior modeling
h/t to @dariusmc

32. time
1
0.2
User belief
User
motivation
Likelihood to
Convert (p) Marketing campaign goal
Actual campaign effect
Marketing Campaigns = Usually Late to the Game

33. time
1
0.2
User belief
User
motivation
Likelihood to
Convert (p)
injection insufficient / late
“Come Back and Get 20% OFF”
=> Cost incurred but no revenue
injection too high
“Get a Free Tesla”
=> Cost > ARRPU = negative Margin
optimal time
of campaign (t)

34. fit prediction
behavior
analysis
optimal time to
inject motivation
Fit & Behavior modeling

35. time
1
0.2
User belief
User
motivation
Likelihood to
Convert (p)
Early detection & appropriate injection

36. So….
How did Drift’s Growth team solve this problem?
By leveraging intent data

37. User fit
User
Behavior
time
1
0.2
Conversion event
Intent to
Convert (p)
Visit my
website
Visit 3rd
party websites
Assisted discovery (nurture campaigns)
Register for
trial
Self discovery of
product
Discovery of
Market

38. time
1
0.2
Conversion event
Intent to
Convert (p)
Visit my
website
Visit 3rd
party websites
Discovery of Market

39. Installed
Web Tech
Reading About
“Livechat”
Hiring Sales
Director
TIME
Working backwards from the visit
Visited
Website
Upvoted
Post
Visited
Category Page
=> We detect intent before it’s expressed to us

40. Web technographics
Alert on competitor install
- 1 month after, chance of POC failure ->
“how’s the POC going with x?”
- 10 month after, warn about contract
renewal
- Multiple competitor install? “Can we be
part of your RFP”?

41. Corp traffic to G2Crowd pages

42. Get all upvotes on a post.

43. Scrape Post upvoters &
commenters with
PhantomBuster APIs

44. Discover companies surging on specific topics

46. Job postings

47. Job change within champions

48. How can we store the data

49. A company can trigger
multiple signals
in a short period of time

54. So what do we do
with that data?

55. Send emailFirmographic ScoreGet IP Address Returns Domain
COMPANY OBJECT
Hundred of high fit
companies per day
IF SCORE > 0.9
Prospection

56. About using the data in the outreach…

58. Good email G ….
We’re missing an illustration

59. Human - sped up 3x

61. Automation - slowed down 5x

64. Email
Account, User and
Campaign Record
VISITED WEBSITE
SCORE PROSPECTING
COLLECTION ACTION
ACTION
ENRICHMENT & FIT SCORING

65. Account, User and
Campaign Record
VISITED WEBSITE
SCORE PROSPECTING
COLLECTION ACTION
ACTION
ENRICHMENT & FIT SCORING
Chat
Email
Web content
Advertising
Physical goods

66. Display LivechatFirmographic ScoreGet IP Address Returns Domain
COMPANY OBJECT IF SCORE > 0.9

67. Deliver coffeeBot data collection Create order

68. Change
HomePage
Firmographic ScoreGet IP Address Returns Domain
COMPANY OBJECT

70. About cost and margins…

71. Visited
Website
Installed
Web Tech
Reading About
“Livechat”
Hiring Sales
Director
TIME
Demo
Request!
Predicting the lead value based on intent
SCORE: 0.12
Intent
SCORE: 0.33
Intent
SCORE: 0.61
Intent
SCORE: 0.88
Intent
VALUE: $130
Prediction
VALUE: $200
Prediction
VALUE: $350
Prediction
VALUE: $600
Prediction

72. Send conversionFirmographic ScoreGet IP Address Returns Domain
COMPANY OBJECT
Predictive $ value
Send to facebook a “conversion” with a predictive $ value
before any engagement happened.
=> Leverage Facebook’s Ecommerce Bid optimizer
…for SaaS

73. VISION
Visited
Website
Installed
Web Tech
Reading About
“Livechat”
Hiring Sales
Director
TIME
Demo
Request!
The Right Action at the Right Time
VALUE: $130
Prediction
VALUE: $200
Prediction
VALUE: $350
Prediction
VALUE: $600
Prediction
VALUE: $1200
Prediction
COST: $0.12
ADVERTISING
COST: $0.01
EMAIL
COST: $20
BRANDED GIFT
COST: $4
LIVECHAT
Predicted next best
Action

74. We are simply applying winning
B2C bid optimization strategies
to B2B sales automation

75. WEB TECHNOGRAHICS
TOPIC SURGING
CUSTOM SITE SCRAPING
Chat
Email
Web content
Advertising
Account, User and
Campaign Record
Physical goods
Output Channels
VISITED WEBSITE
SCORE PROSPECTING
COLLECTION ACTION
Intent capture
Enrich & Fit scoring
SaaS reviews

80. Let’s see it in action