Jul 12, 2018 · 23m · top-founders
1083 How she 2x ARR yoy in mobile testing space to $6m in ARR
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gold bands on the timeline = statements, start to end. Hover to read, click to jump. CC turns on captions
Apptimize CEO Nancy Hua joins Nathan Latka to break down how her mobile A/B testing platform scaled to $6 million in ARR, achieved sub-$5,000 CAC with a two-month payback, and reduced enterprise churn below 10%.
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 32.2% of the talking time here. How this is scored →
Nathan as informed peer 6.0 Guest teaching 1.1 Guest disagreement 1.1 Nathan pushing back 3.4
speaking balance: gold is Nathan, purple is the guest (3 minute bins)
Sharpest disagreement ▶ 5:53 Nancy directly corrects growth expectations
Nancy firmly corrects Nathan's assumption that missing their two closing deals would leave them 'a little below' their target, asserting they will be 'a lot below'.
Hardest push from Nathan ▶ 11:43 Nathan challenges quarterly churn figureNathan refuses to let Nancy's 10% quarterly churn figure pass without pointing out that it equates to a devastating 40% annual revenue loss.
Biggest teaching moment ▶ 11:50 Nancy concedes on SaaS churn calculationsNancy acknowledges Nathan's superior grasp of SaaS reporting math after he points out the annualized implication of quarterly churn.
Nathan holds their own ▶ 11:43 Nathan recalculates churn on the flyNathan instantly demonstrates deep SaaS domain knowledge by multiplying Nancy's quarterly churn metric to reveal an annualized inconsistency.
the scores for every segment, with the reasoning behind each
| Chapter | Topic | Nathan as informed peer | Guest teaching | Guest disagreement | Nathan pushing back | Why |
|---|---|---|---|---|---|---|
| Introducing Nancy Hua and Apptimize | 5 | 1 | 1 | 2 | Nathan guides the introduction into Apptimize's business model, ACV ($35k), and monetization tiers. Nancy comfortably details enterprise pricing along monthly active user volume. | |
| Customer Evolution from Startups to Enterprise Giants | 6 | 2 | 2 | 5 | Nathan presses Nancy on exact ARR and monthly revenue figures. Nancy reveals their 2x YoY growth hinges on two massive end-of-quarter deals closing, prompting Nathan to narrow down their monthly run rate to ~$500k MRR. | |
| Sales Team Structure, Onboarding, and Quotas | 6 | 1 | 1 | 3 | Nathan dissects the sales team structure, asking about rep ramps and annual quota targets ($800k). He rapidly calculates deal volume requirements based on their ACV. | |
| Combating Churn Through Adoption and Usage Tracking | 8 | 1 | 1 | 6 | When Nancy states quarterly revenue churn is sub-10%, Nathan immediately catches the implied ~40% annualized churn rate, prompting Nancy to admit Nathan knows the SaaS math better and clarify it is an annual figure. | |
| Customer Acquisition Costs, Payback Periods, and LTV | 7 | 1 | 1 | 3 | Nathan walks through CAC and payback periods, noting that a sub-$5k CAC on a $35k ACV yields an exceptionally fast 2-month payback period. Nancy discusses their conservative 3-year LTV baseline. | |
| Fundraising History, Series B Valuation, and Cash Flow Goals | 6 | 2 | 2 | 4 | Nathan inquires about their $20M total raise and Series B valuation multiple. Nancy describes raising $15M during the 2016 SaaS downturn and explains her goal to reach cash flow positivity to avoid fundraising again. | |
| The Famous Five Rapid-Fire Questions | 4 | 0 | 0 | 1 | Nathan runs through his Famous Five questions in a friendly, conversational tone, and Nancy shares candid personal reflections before Nathan delivers the final summary. |
Statements from this episode (17)
Hua: Apptimize's average contract value exceeds $35,000 per year
“It's our ACB has gone up to a little more than 35 K now per year.”
Hua: Apptimize's largest customers pay over $300,000 annually
“And, but our biggest customers are paying more than 300 K per year.”
Hua: Apptimize offers non-linear tier pricing as active users increase
“Yeah, we definitely don't just scale linearly. So the more they the more they add on monthly active users, the like more economical to get per monthly active user.”
Hua: Apptimize has no single customer over 10% of revenue
“No, we're still pretty balanced in terms of like still having a bunch of like diversity in the customer set.”
Hua: Apptimize aims to close its first $1M deal next year
“Like for the next year, we do want to close a million dollar deal and stuff like that. And that'll, that's pretty aspirational for us right now. Cause it's like it's already in the pipeline basically. Right. So we know we'll have to close one of these deals fo…”
Hua: Apptimize generates right around $500k monthly recurring revenue
“No, we're very close to like, we're right around there right now.”
Hua: Apptimize has six salespeople and six SDRs
“There's, oh, actually now there's six salespeople. There's also six SDRs.”
Hua: Apptimize ramps sales reps for one quarter before full quota
“They're off their, they're on their ramp quota for the first quarter. And then after that, they're on a full quota.”
Hua: Apptimize sets salesperson quota around $800K with 50/50 comp split
“I think right now most of them are on around an 800 K per year quota. And you know, it's like 50 50 basin bonus.”
Hua: Apptimize annual revenue churn is under 10%
“And so our turn is sub
10% in terms of, like, not counting growth, and then our goal is.”
Hua: Apptimize's annual revenue churn was over 20% last year
“I think it was, like, 20%
A little more than 20%, even, I'm remembering.”
Hua: Churn is a lagging metric that creates false security
“And so we started tracking usage and different adoption metrics and that gave us way more leading indicators because turn is the ultimate lagging indicator where you're like, you feel safe just ignoring it for a long time because it doesn't come up until final…”
Hua: Apptimize assumes 3-year LTV despite 9-year calculated retention
“Based on what we were measuring it, like we haven't had enough churn that it looked like it was lower than, you know, like nine years or something like it was on average. It was really weird. So we just assume three because that's what is standard. And we want…”
Hua: SaaS valuation multiples are random, ranging from 5x to 40x
“I think that these multiples are just, like, really random. Cause they're just kind of like in SAS, I've seen anything from like a five X to like a 40 X, you know?”