8 Figure Amazon Seller Mels Terlouw Goes Deep On Data

Introduction

Danny McMillan welcomes Mels Terlouw, an Amazon seller from the Netherlands, to the podcast for his first time on Seller Sessions.

About Mels

Mels started selling on Amazon in 2014 after discovering people selling $100k per month.
He set a goal to hit $10k in sales by end of 2014. Hit $100k that first year.
Grew to $2.4 million in sales in 2015, then continued rapid growth each year.
Hit 9 figure mark in 2020 but had a setback afterwards. Still doing great.
In 2016, started building own software tools and put them into a mastermind group.
Opened the tools up in 2022 as Intellivy.

Obsessed With Listing Optimisation

Focus is on listing creation based on consumer insights.
This can be the difference between 15 units vs 250 units sold per day.

The Evolution of Amazon

In the "golden age" of 2014-2016, selling was easy on Amazon. Fake reviews worked.
Now Amazon algorithm and competition has evolved. Old tricks don't work as well.
Need to go where "the puck is going" instead of where it's been. Get back to basics of selling.

3 Types of Amazon Sellers

Ignorance Sellers: Try stuff without much strategy, hope for the best.
Arrogant Sellers: Copy competitors thinking they know better.
Vigilant Sellers: Build on consumer insights and real selling fundamentals.

Understanding the Amazon Algorithm

BERT algorithm understands search intent, customer persona/journey better.
Search results will be tailored more to individual searcher.
Recommendations are already customised to user. Search will follow.
Amazon wants to match real world experience better.

Adapting to the Changes

Can still succeed in saturated market with the right approach: know your customer + selling fundamentals.

The Iceberg Analogy

Most fighting happening "above water" on Amazon right now.
By going deeper below surface there is calm and less competition.
This represents focusing on consumer insights and the lost art of selling.

Video Lesson from Jordan Belfort on Selling

Key points:

Understand your consumer thoroughly
Know their persona, journey stage, needs
Ask pointed questions
Craft sales pitch
Develop products to match
Optimise listings for buyer-focused messaging
Start selling in the right way

The Scientific Method for Amazon Listings

Ask questions
Research to find answers
Form hypotheses
Test with experiments
Analyse results
Repeat until find winning formula
Implement in listing

Key Questions to Ask

What problem are you solving?
What are alternate solutions/products?
What are the customer objections, drivers, preferences?

Brainstorm Extensively

Get multiple people involved.
Spend hours diving into data.
Come up with creative hypotheses.

Develop Listing Theme

Focus on winning the click, converting the customer, countering objections.
Weave together into cohesive theme for listing.

Test and Validate

Try different creative variations.
Tools like Intellivy can help with robust testing.
Make sure hypothesis proves true before investing in new listing.

Case Study: Dollar Shave Club

Upstart disrupted market by simplifying down to core job of razors - conveniently replace dull blades monthly.
Didn't get caught in feature arms race. Addressed root consumer frustration.

Types of Amazon Shoppers

Different groups have different needs. Most sellers focus only on "need based" shoppers.
But many other opportunities exist. Be creative.

Connect on an Emotional Level

Surface features don't create enough certainty and trust.
Tap into beliefs, objections, and emotions.
Storytelling and visual patterns are powerful.

Focus on the Buyer, Not You or the Product

Avoid self-focused messaging.
Put yourself in the customer's shoes. Talk to them directly.

Execute the Lost Art of Selling

Go below the surface noise on Amazon.
Dig deep into consumer insights.
Master stories, emotions, and relationships.
This creates real connection and converts browsers into loyal buyers.

8 Figure Amazon Seller Mels Terlouw Goes Deep On Data

Seller Sessions Amazon FBA and Private Label

8 Figure Amazon Seller Mels Terlouw Goes Deep On Data