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Oct 13, 2018 · 20m · top-founders

1176 How this Developer Made $250k+ Selling Powertools He Didn't Own Via Amazon Ebay Arbitrage

Alex Kilkka · 10m spoken Nathan Latka · 7m spoken
0:00 / 0:00

gold bands on the timeline = statements, start to end. Hover to read, click to jump. CC turns on captions

Host Nathan Latka interviews developer Alex Kilkka to explore how he built automated software algorithms to generate over $1M in gross sales through Amazon-eBay power tool arbitrage before founding a specialized data consultancy.

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 42.2% of the talking time here. How this is scored →

Nathan as informed peer 5.0 Guest teaching 4.3 Guest disagreement 1.5 Nathan pushing back 3.8
05100:0010:0020:001:22–4:51 · Nathan as informed peer 4/10 Alex Kilkka and Austin Data Solutions Overview Nathan asks about Alex's background and clarifies how the initial eBay-to-Amazon Beats headphone arbitrage idea worked. Alex explains the simple mechanics and how he sought to automate it.4:55–9:40 · Nathan as informed peer 5/10 Developing Arbitrage Algorithms and Finding Winning Products Alex details the unforeseen technical hurdles of programmatic arbitrage, including title similarity scoring, Mechanical Turk validation, and return window discrepancies. He explains why Milwaukee power tools became their most consistent niche.9:43–16:44 · Nathan as informed peer 6/10 Sponsor Segment: AdRoll Marketing Platform Following an ad read, Nathan calculates potential revenue and shares an anecdote about Amazon price wars. Alex explains how an Amazon policy change automating return labels destroyed the model by forcing physical inventory management.16:46–18:26 · Nathan as informed peer 5/10 Financial Metrics and Business Performance Review Nathan repeatedly pushes Alex to state a definitive total unit count and back into overall numbers using gross monthly profit. Alex is unable to recall precise statistics on the spot.1:22–4:51 · Guest teaching 4/10 Alex Kilkka and Austin Data Solutions Overview Nathan asks about Alex's background and clarifies how the initial eBay-to-Amazon Beats headphone arbitrage idea worked. Alex explains the simple mechanics and how he sought to automate it.4:55–9:40 · Guest teaching 6/10 Developing Arbitrage Algorithms and Finding Winning Products Alex details the unforeseen technical hurdles of programmatic arbitrage, including title similarity scoring, Mechanical Turk validation, and return window discrepancies. He explains why Milwaukee power tools became their most consistent niche.9:43–16:44 · Guest teaching 5/10 Sponsor Segment: AdRoll Marketing Platform Following an ad read, Nathan calculates potential revenue and shares an anecdote about Amazon price wars. Alex explains how an Amazon policy change automating return labels destroyed the model by forcing physical inventory management.16:46–18:26 · Guest teaching 2/10 Financial Metrics and Business Performance Review Nathan repeatedly pushes Alex to state a definitive total unit count and back into overall numbers using gross monthly profit. Alex is unable to recall precise statistics on the spot.1:22–4:51 · Guest disagreement 1/10 Alex Kilkka and Austin Data Solutions Overview Nathan asks about Alex's background and clarifies how the initial eBay-to-Amazon Beats headphone arbitrage idea worked. Alex explains the simple mechanics and how he sought to automate it.4:55–9:40 · Guest disagreement 1/10 Developing Arbitrage Algorithms and Finding Winning Products Alex details the unforeseen technical hurdles of programmatic arbitrage, including title similarity scoring, Mechanical Turk validation, and return window discrepancies. He explains why Milwaukee power tools became their most consistent niche.9:43–16:44 · Guest disagreement 2/10 Sponsor Segment: AdRoll Marketing Platform Following an ad read, Nathan calculates potential revenue and shares an anecdote about Amazon price wars. Alex explains how an Amazon policy change automating return labels destroyed the model by forcing physical inventory management.16:46–18:26 · Guest disagreement 2/10 Financial Metrics and Business Performance Review Nathan repeatedly pushes Alex to state a definitive total unit count and back into overall numbers using gross monthly profit. Alex is unable to recall precise statistics on the spot.1:22–4:51 · Nathan pushing back 2/10 Alex Kilkka and Austin Data Solutions Overview Nathan asks about Alex's background and clarifies how the initial eBay-to-Amazon Beats headphone arbitrage idea worked. Alex explains the simple mechanics and how he sought to automate it.4:55–9:40 · Nathan pushing back 3/10 Developing Arbitrage Algorithms and Finding Winning Products Alex details the unforeseen technical hurdles of programmatic arbitrage, including title similarity scoring, Mechanical Turk validation, and return window discrepancies. He explains why Milwaukee power tools became their most consistent niche.9:43–16:44 · Nathan pushing back 4/10 Sponsor Segment: AdRoll Marketing Platform Following an ad read, Nathan calculates potential revenue and shares an anecdote about Amazon price wars. Alex explains how an Amazon policy change automating return labels destroyed the model by forcing physical inventory management.16:46–18:26 · Nathan pushing back 6/10 Financial Metrics and Business Performance Review Nathan repeatedly pushes Alex to state a definitive total unit count and back into overall numbers using gross monthly profit. Alex is unable to recall precise statistics on the spot.

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

0:00 · Nathan 63.9% · guest 36.1%0:00 · Nathan 63.9% · guest 36.1%3:00 · Nathan 22.1% · guest 77.9%3:00 · Nathan 22.1% · guest 77.9%6:00 · Nathan 22.4% · guest 77.6%6:00 · Nathan 22.4% · guest 77.6%9:00 · Nathan 58.5% · guest 41.5%9:00 · Nathan 58.5% · guest 41.5%12:00 · Nathan 34.9% · guest 65.1%12:00 · Nathan 34.9% · guest 65.1%15:00 · Nathan 41.5% · guest 58.5%15:00 · Nathan 41.5% · guest 58.5%18:00 · Nathan 54.5% · guest 45.5%18:00 · Nathan 54.5% · guest 45.5%
Sharpest disagreement ▶ 14:51 Questioning Amazon's pricing reputation

Alex politely counters Nathan's assertion that Amazon always maintains the cheapest prices, explaining differences in platform trust and customer service expectations.

 Hardest push from Nathan ▶ 17:07 Pressing for specific unit sales floor

Nathan refuses to let Alex deflect on total unit sales, repeatedly asking him to provide a baseline floor number he is certain he exceeded.

 Biggest teaching moment ▶ 13:04 Policy shift destroys no-inventory arbitrage

Alex educates Nathan on how automated default return labels forced physical inventory onto sellers, destroying the dropshipping arbitrage model.

 Nathan holds their own ▶ 15:30 Nathan details Amazon diaper price war

Nathan demonstrates industry knowledge by citing David Smith's experience competing with Amazon on diaper pricing in Brazil.
the scores for every segment, with the reasoning behind each
ChapterTopicNathan as informed peerGuest teachingGuest disagreementNathan pushing backWhy
Alex Kilkka and Austin Data Solutions Overview 4412 Nathan asks about Alex's background and clarifies how the initial eBay-to-Amazon Beats headphone arbitrage idea worked. Alex explains the simple mechanics and how he sought to automate it.
Developing Arbitrage Algorithms and Finding Winning Products 5613 Alex details the unforeseen technical hurdles of programmatic arbitrage, including title similarity scoring, Mechanical Turk validation, and return window discrepancies. He explains why Milwaukee power tools became their most consistent niche.
Sponsor Segment: AdRoll Marketing Platform 6524 Following an ad read, Nathan calculates potential revenue and shares an anecdote about Amazon price wars. Alex explains how an Amazon policy change automating return labels destroyed the model by forcing physical inventory management.
Financial Metrics and Business Performance Review 5226 Nathan repeatedly pushes Alex to state a definitive total unit count and back into overall numbers using gross monthly profit. Alex is unable to recall precise statistics on the spot.

Statements from this episode (6)

Opinion
Kilkka: Programmatic cross-marketplace arbitrage opportunities still exist across the board
“But I think there are still opportunities like that across the board if you know how to look for them, especially if you're able to programmatically look for them.”
Alex Kilkka Oct 13, 2018 ▶ 4:45
Disclosure
Kilkka: Arbitrage System Initially Required at Least a $5 Price Spread
“We set certain parameters, like we needed at least a five dollar price difference to make it even worthwhile. And then we increased that over time.”
Alex Kilkka Oct 13, 2018 ▶ 6:58
Insight
Kilkka: Power tools created the best arbitrage opportunity due to model consistency
“So with tower tools, you've got great model numbers. It's very consistent. You have like dozens of very high quality sellers on eBay. You have customers who typically are more like power buyers, you know, they, it's not their first power tool they've ever owne…”
Alex Kilkka Oct 13, 2018 ▶ 9:19
Assertion Not checkable as stated
Kilkka: Power tool arbitrage generated over $1M in sales over three years
“Definitely more than a million in sales. Okay. Dollar sales.”
Alex Kilkka Oct 13, 2018 ▶ 11:39
Insight
Kilkka: Using female customer service personas led to nicer customer emails
“And actually one thing we did is we made all of our customer service reps female in the email correspondence because people tend to be nicer to them.”
Alex Kilkka Oct 13, 2018 ▶ 16:32
Assertion Not checkable as stated
Kilkka: Amazon-eBay arbitrage store generated $4K to $10K monthly gross profit
“On average made four to 6000 in gross profit per month or four to four to 10 depending on holiday season.”
Alex Kilkka Oct 13, 2018 ▶ 17:45
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