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Jan 28, 2018 · 16m · top-founders

918 SaaS: Fraud Prevention Company Raises $54m, Passes 500 Customers

Jason Tan · 8m spoken Nathan Latka · 6m spoken
0:00 / 0:00

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Host Nathan Latka interviews Sift Science founder Jason Tan to explore how the company uses real-time machine learning to combat online fraud, scale past 500 enterprise customers, and raise $54 million in venture capital.

How this conversation actually went

Every chapter scored 0–10 on four independent dynamics. Hover any point for the reasoning behind the score. Nathan holds 43.1% of the talking time here. How this is scored →

Nathan as informed peer 4.4 Guest teaching 2.2 Guest disagreement 2.2 Nathan pushing back 3.8
05100:0010:000:49–4:15 · Nathan as informed peer 5/10 Overview of Sift Science and Pricing Model Nathan probes Jason on how Sift Science generates revenue and drills down on his personal financial outcome from Zillow. Jason gently clarifies that usage-based pricing is still SaaS and tempers expectations regarding startup equity payouts.4:15–7:01 · Nathan as informed peer 3/10 Y Combinator, Early Backing, and Customer Milestones Jason recounts his early days entering Y Combinator and securing seed backing from Max Levchin. Nathan keeps the conversation moving smoothly with straightforward informational questions regarding funding and customer milestones.7:04–9:15 · Nathan as informed peer 6/10 Sponsor Spotlight: Digital Contract Signing with SignEasy Nathan attempts to corner Jason into disclosing financial metrics by multiplying customer count by estimated ARPU to guess MRR and ARR. Jason firmly resists disclosing exact revenue figures and emphasizes the wide variation in contract sizes.9:15–14:11 · Nathan as informed peer 6/10 Team Footprint, Acquisition Channels, and Retention Metrics Nathan delves into unit economics, CAC payback periods, and churn rates. Jason corrects Nathan's assumption about Y Combinator curriculum by explaining that YC focuses strictly on early validation rather than mature SaaS metrics.14:11–16:06 · Nathan as informed peer 2/10 The Famous Five Rapid-Fire Questions Nathan conducts the Famous Five rapid-fire segment, briefly bantering about Jason's choice of Google Docs as his favorite tool before wrapping up with a standard summary.0:49–4:15 · Guest teaching 3/10 Overview of Sift Science and Pricing Model Nathan probes Jason on how Sift Science generates revenue and drills down on his personal financial outcome from Zillow. Jason gently clarifies that usage-based pricing is still SaaS and tempers expectations regarding startup equity payouts.4:15–7:01 · Guest teaching 2/10 Y Combinator, Early Backing, and Customer Milestones Jason recounts his early days entering Y Combinator and securing seed backing from Max Levchin. Nathan keeps the conversation moving smoothly with straightforward informational questions regarding funding and customer milestones.7:04–9:15 · Guest teaching 1/10 Sponsor Spotlight: Digital Contract Signing with SignEasy Nathan attempts to corner Jason into disclosing financial metrics by multiplying customer count by estimated ARPU to guess MRR and ARR. Jason firmly resists disclosing exact revenue figures and emphasizes the wide variation in contract sizes.9:15–14:11 · Guest teaching 5/10 Team Footprint, Acquisition Channels, and Retention Metrics Nathan delves into unit economics, CAC payback periods, and churn rates. Jason corrects Nathan's assumption about Y Combinator curriculum by explaining that YC focuses strictly on early validation rather than mature SaaS metrics.14:11–16:06 · Guest teaching 0/10 The Famous Five Rapid-Fire Questions Nathan conducts the Famous Five rapid-fire segment, briefly bantering about Jason's choice of Google Docs as his favorite tool before wrapping up with a standard summary.0:49–4:15 · Guest disagreement 2/10 Overview of Sift Science and Pricing Model Nathan probes Jason on how Sift Science generates revenue and drills down on his personal financial outcome from Zillow. Jason gently clarifies that usage-based pricing is still SaaS and tempers expectations regarding startup equity payouts.4:15–7:01 · Guest disagreement 1/10 Y Combinator, Early Backing, and Customer Milestones Jason recounts his early days entering Y Combinator and securing seed backing from Max Levchin. Nathan keeps the conversation moving smoothly with straightforward informational questions regarding funding and customer milestones.7:04–9:15 · Guest disagreement 4/10 Sponsor Spotlight: Digital Contract Signing with SignEasy Nathan attempts to corner Jason into disclosing financial metrics by multiplying customer count by estimated ARPU to guess MRR and ARR. Jason firmly resists disclosing exact revenue figures and emphasizes the wide variation in contract sizes.9:15–14:11 · Guest disagreement 3/10 Team Footprint, Acquisition Channels, and Retention Metrics Nathan delves into unit economics, CAC payback periods, and churn rates. Jason corrects Nathan's assumption about Y Combinator curriculum by explaining that YC focuses strictly on early validation rather than mature SaaS metrics.14:11–16:06 · Guest disagreement 1/10 The Famous Five Rapid-Fire Questions Nathan conducts the Famous Five rapid-fire segment, briefly bantering about Jason's choice of Google Docs as his favorite tool before wrapping up with a standard summary.0:49–4:15 · Nathan pushing back 4/10 Overview of Sift Science and Pricing Model Nathan probes Jason on how Sift Science generates revenue and drills down on his personal financial outcome from Zillow. Jason gently clarifies that usage-based pricing is still SaaS and tempers expectations regarding startup equity payouts.4:15–7:01 · Nathan pushing back 2/10 Y Combinator, Early Backing, and Customer Milestones Jason recounts his early days entering Y Combinator and securing seed backing from Max Levchin. Nathan keeps the conversation moving smoothly with straightforward informational questions regarding funding and customer milestones.7:04–9:15 · Nathan pushing back 7/10 Sponsor Spotlight: Digital Contract Signing with SignEasy Nathan attempts to corner Jason into disclosing financial metrics by multiplying customer count by estimated ARPU to guess MRR and ARR. Jason firmly resists disclosing exact revenue figures and emphasizes the wide variation in contract sizes.9:15–14:11 · Nathan pushing back 4/10 Team Footprint, Acquisition Channels, and Retention Metrics Nathan delves into unit economics, CAC payback periods, and churn rates. Jason corrects Nathan's assumption about Y Combinator curriculum by explaining that YC focuses strictly on early validation rather than mature SaaS metrics.14:11–16:06 · Nathan pushing back 2/10 The Famous Five Rapid-Fire Questions Nathan conducts the Famous Five rapid-fire segment, briefly bantering about Jason's choice of Google Docs as his favorite tool before wrapping up with a standard summary.

speaking balance: gold is Nathan, purple is the guest (3 minute bins)

0:00 · Nathan 53.4% · guest 46.6%0:00 · Nathan 53.4% · guest 46.6%3:00 · Nathan 17.2% · guest 82.8%3:00 · Nathan 17.2% · guest 82.8%6:00 · Nathan 43.4% · guest 56.6%6:00 · Nathan 43.4% · guest 56.6%9:00 · Nathan 45.5% · guest 54.5%9:00 · Nathan 45.5% · guest 54.5%12:00 · Nathan 44.6% · guest 55.4%12:00 · Nathan 44.6% · guest 55.4%15:00 · Nathan 72.4% · guest 27.6%15:00 · Nathan 72.4% · guest 27.6%
Sharpest disagreement ▶ 8:35 Jason refusing revenue disclosure

Jason repeatedly stonewalls Nathan's direct attempts to pin down Sift Science's run rate and MRR, refusing to confirm calculated revenue numbers.

 Hardest push from Nathan ▶ 8:27 Nathan forcing the revenue calculation

Nathan refuses to accept a vague answer, directly multiplying Jason's stated ARPU by total customer count to trap him on run-rate figures.

 Biggest teaching moment ▶ 11:28 Debunking Y Combinator's metric curriculum

Jason clearly educates Nathan that Y Combinator teaches nothing about mature SaaS metrics because its focus is purely on early-stage three-month product launches.

 Nathan holds their own ▶ 10:56 Nathan highlighting LTV to CAC traps

Nathan demonstrates domain expertise by explaining how LTV to CAC ratios can create deceptive cash gaps without tight payback periods.
the scores for every segment, with the reasoning behind each
ChapterTopicNathan as informed peerGuest teachingGuest disagreementNathan pushing backWhy
Overview of Sift Science and Pricing Model 5324 Nathan probes Jason on how Sift Science generates revenue and drills down on his personal financial outcome from Zillow. Jason gently clarifies that usage-based pricing is still SaaS and tempers expectations regarding startup equity payouts.
Y Combinator, Early Backing, and Customer Milestones 3212 Jason recounts his early days entering Y Combinator and securing seed backing from Max Levchin. Nathan keeps the conversation moving smoothly with straightforward informational questions regarding funding and customer milestones.
Sponsor Spotlight: Digital Contract Signing with SignEasy 6147 Nathan attempts to corner Jason into disclosing financial metrics by multiplying customer count by estimated ARPU to guess MRR and ARR. Jason firmly resists disclosing exact revenue figures and emphasizes the wide variation in contract sizes.
Team Footprint, Acquisition Channels, and Retention Metrics 6534 Nathan delves into unit economics, CAC payback periods, and churn rates. Jason corrects Nathan's assumption about Y Combinator curriculum by explaining that YC focuses strictly on early validation rather than mature SaaS metrics.
The Famous Five Rapid-Fire Questions 2012 Nathan conducts the Famous Five rapid-fire segment, briefly bantering about Jason's choice of Google Docs as his favorite tool before wrapping up with a standard summary.

Statements from this episode (11)

Disclosure
Tan: Sift Science charges per billable transaction or account creation
“In terms of how we make money, we charge per billable event, and that usually ties to a significant transaction or an account created on our customer's website.”
Jason Tan Jan 28, 2018 ▶ 1:51
Assertion Not checkable as stated
Tan: Sift Science customers pay thousands up to $100,000 monthly
“Oh, it's probably going to be a in the minimum of a few thousand dollars per month. We have some customers paying us a 100,000 dollars per month.”
Jason Tan Jan 28, 2018 ▶ 2:32
Opinion
Tan: Making Over $1M on Startup Equity Typically Requires Facebook-Scale Outcome
“You know, typically to make more than a million dollars at any company, it would require kind of a Facebook size outcome.”
Jason Tan Jan 28, 2018 ▶ 3:59
Assertion Supported
Tan: Max Levchin personally invested $1M to lead Sift Science seed round
“He wanted to lead our seed round. So he put a million dollars of his own money in and we raised a total of 1.6 million dollars back in September of 2001.”
Jason Tan Jan 28, 2018 ▶ 6:35
Assertion Supported
Tan: Sift Science has raised approximately $54 million in total funding
“Oh, we raised about fifty four million dollars.”
Jason Tan Jan 28, 2018 ▶ 6:52
Assertion Not checkable as stated
Tan: Sift Science has approximately 500 paying customers
“We have, we're lucky to have quite a few just about 500”
Jason Tan Jan 28, 2018 ▶ 6:58
Assertion Not checkable as stated
Sift Science has more than doubled revenue annually for past few years
“We've been more than doubling our revenue for the last few years.”
Jason Tan Jan 28, 2018 ▶ 8:36
Assertion Not checkable as stated
Tan: Sift Science maintains an LTV to CAC ratio over 3:1
“I think that our LTVCAC is more than three to one.”
Jason Tan Jan 28, 2018 ▶ 10:51
Assertion Not checkable as stated
Tan: Sift Science achieves CAC payback in under 18 months
“I mean, ours is sub 18 months.”
Jason Tan Jan 28, 2018 ▶ 11:11
Assertion Not checkable as stated
Tan: Sift Science revenue churn is under 1%
“Our churn is, our revenue churn is sub one percent.”
Jason Tan Jan 28, 2018 ▶ 12:34
Assertion Not checkable as stated
Tan: Sift Science annual logo churn is under 3%
“Oh, I think you're sort of sub-three percent.”
Jason Tan Jan 28, 2018 ▶ 13:21
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