🎅🏼 N*ked Truths, Aggregator Moves, A9 News

🎅 3 of 2023’s MOST TALKED ABOUT STORIES in BDSN 🦌
  • 🪆 Naked girl on balcony - most loved it, a few not so much

  • 💰 Amazon aggregator concept isn’t dead

  • 🥇 Most shared story - changes afoot in the A9 algorithm

Curriculum Vitae:

This newsletter began on August 14, 2023. Issue #1 went to 1,728 subscribers.

Today’s edition is the 39th newsletter. It is being sent to 6,477 subscribers.

You can read the back issues you missed here.

Almost all growth is organic and from current subscriber referrals.

Another 1,991 subscribers have been removed because they didn’t open and click at least once in 30 days.

Below are the current engagement stats with lifetime percentages.

Open rates >40% and click rates >4% are considered excellent by experts.

Want to learn how to do this for your product or service business?

Watch this free newsletter secrets webinar from me and/or sign-up for 10 live interactive hands-on trainings that just started (replays available).

🗣️ READER COMMENTS

“I just wanted to say thank you for the great newsletters. Your newsletter is the only one I regularly open.”

“I am the first collector of BDSS Newsletters. I don’t delete them. I feel it is such a waste to delete any of them.”

🔥  I was just wondering how long you can keep the unbelievable quality of these newsletters - I'm hoping for a long time. ☺️

Please feel free to share your thoughts and comments here

In case you missed them, here are some of the stories published in BDSN that got the most buzz this fall. These make for great reading when you want to escape from your in-laws today, or on the plane or car ride home or to your vacation.

🥇 MOST CONTROVERSIAL STORY

In some newsletter editions I add a personal story and then tie it to a lesson.

When a story is really good, or on the flip side if some folks are a bit put off, comments pour in.

Here’s one from August 28 that was love it (most readers) or hate it (a few):

  • “NAKED GIRL BALCONY. Really?”

  • “Love it Kevin!  Here’s to Balcony Barbie!”

  • “I was smiling at the gym reading the naked girl story 😂

  • “Interested in earning a living, not in your appetite for inappropriateness.“

  • “Dude, your newsletter is the absolute best in the industry. I’ve shared it many times.”

  • “Please unsubscribe me from this rubbish.”

  • “Ha ha ha! Too good!”

  • “I love how you intertwine edginess, personal stories, humor, travel and Amazon news with awesome tools and actionable tips. I can’t believe this is free. I look forward to every Monday and Thursday.”

🪆 The Naked Barbie Chronicles

I work from home on the 32nd floor of a high-rise on Rainey St in Austin.

A week ago Wednesday, I was crafting the next issue of this newsletter.

I felt the urge to stretch and grab a Coke Zero refill.

As I was moseying to the kitchen, I made a pit stop at my balcony to play clean up crew for Zoe’s little “gifts” (shoutout to Fresh Patch real grass - it’s the Rolls Royce for high-rise canine living).

As I'm on poop patrol, my spidey-senses tingled. I glanced over the railing and to my surprise saw Balcony Barbie!

One floor down and one building over, there she was, in all her naked glory, casually chatting with a guy who apparently missed the 'clothes-optional' memo.

I kid you not, this is a 100% organic, non-GMO, gluten-free true story.

Now, was she feeling the 108 degree Austin heat, or maybe she just thinks “Hey, it’s a free world, let’s feel the breeze?” Who knows?

It got me pondering about us, the mighty Amazon sellers. Sometimes, we too feel exposed, trying to decode the enigmatic dance of the A9 algorithm.

We do want to stand out, in all our glory, for the world to see.

We’ll cover that today (no pun intended).

( to see the photo, check out the bottom of the original August 28 edition )

🥇 MOST COMMENTED STORY

This story from the November 20th edition got a lot of reader comments and appreciation, including these:

💰 AMZ AGGREGATOR CONCEPT ISN’T DEAD

The concept of Amazon aggregators, especially as Thrasio prepares to file for bankruptcy, isn't dead.

Thrasio was founded with $500,000 from friends and family in 2018 by two guys with multi-decade track records of building & scaling businesses. They focused on acquiring under-optimized but profitable Amazon FBA businesses, mostly with top line revenue under $5 million. 

They grew rapidly, building proprietary software and acquiring over 500 Amazon businesses, hitting $500 million in revenue with 1,000 employees.

They also spent like billionaires, including $75,000 sponsoring events with as few as 25-30 sellers attending, and hundreds of thousands on a lavish party at the Mayfair Supper Club in the Bellagio during the July 2021 Prosper Show.

Thrasio had access to as much as $3.4 billion (the spigot was probably cut off before it was all used). In 2021, Thrasio considered going public via a SPAC, but faced financial audit complications, rising interest rates, and a post-pandemic decrease. A change in management was made, but it was too late.

Down markets reveal strong operators - which Thrasio is not. Thrasio's bankruptcy filing can be attributed to several factors. The declining COVID bump, the creation of knockoffs of highly-rated products, and Amazon becoming a direct competitor impacted Thrasio.

Thrasio also overpayed on multiples due to high competition. I remember one executive from Thrasio claiming on Clubhouse in 2021 there was an endless supply of good Amazon businesses to buy (I strongly disagreed).

In their approach, the company's lack of focus on both customer and seller needs ultimately led to their downfall. Decision-making solely driven primarily by the interests of shareholders and board members, with an eye towards an eventual strategic sale or "flip" of the business, proved to be ineffective.

The McKinsey-style management consultancy approach with inexperienced Harvard and MIT MBA’s know-it-alls also doesn’t work in this business.

Additionally, Amazon’s total fees increased by 30%, slashing margins and making unit economics unviable. Thrasio's lack of a distinctive brand and the drying up of funding sources further compounded their troubles. Plus, they added little value to most of the brands they acquired.

Amazon entrepreneurs excel in their operations, tailoring their business strategies to maximize rapidity and income generation. In contrast, aggregators have been structured to function similarly to conventional consumer packaged goods (CPG) or retail businesses.

Implementing the necessary transformations to achieve this, while preserving the products’s ranking on Amazon, is not a swift transition. Numerous aggregators have recognized this reality and have been evolving.

Thrasio, like many of the 100+ aggregators that have emerged, has no choice but to to concede defeat. Establishing a strong defensive barrier is essential. Brand building must be prioritized over commoditization.

remembering the glory days

Thrasio's portfolio includes several underutilized brands, presenting attractive acquisition opportunities. One seller in the private BDSS Whatsapp Group (a highly active group only for those who have attended a live BDSS event) says he is considering repurchasing his brand to revitalize it.

Another member who sold to Thrasio recommends prioritizing cash at closing, even though buyers often view high immediate cash payments as risky. Seller financing tends to be more stable than relying on performance-based payouts.

What’s worse, many sellers who sold to aggregators have never received promised stability payments or additional earn outs. Lawsuits have been filed.

Despite Thrasio's struggles, the aggregator model still has potential. Good aggregators (which possess more funding than generally known) are quietly rebuilding and adapting. In a few weeks they are getting together in New York.

Amazon is taking measures to regulate the diverse and unbridled marketplace that developed between 2019 and 2021 by altering fee structures, processes, and standards to enhance their network. While these changes are likely to benefit larger, more sophisticated sellers, they could be detrimental to smaller, offshore, and part-time sellers.

Additionally, Amazon is making efforts to become more accommodating to sellers. This shift is particularly advantageous for aggregators, as their size and influence are likely to gain Amazon's attention and support.

Such support from Amazon could address some of the systemic inefficiencies faced by aggregators, such as managing multiple seller central accounts and handling separate inbounding processes for a portfolio of brands.

Lessons from Thrasio's experience suggest the importance of experienced Amazon operators, a focused acquisition strategy, prioritizing brand-building, strengthening product development, and robust supply chain management. 

There are aggregators out there who are expected to successfully navigate the current challenges and re-emerge stronger in the next 18 to 24 months. They might be the one buying your business - so start preparing now. 🤑

🥇 MOST SHARED LINKEDIN STORY

If you don’t follow me on LinkedIn, click here to do it now. I share different content as well as occasionally something from the newsletter abbreviated.

I just joined LinkedIn in August, so it is a new platform for me. But the story below had 30,405 organic impressions (pretty good for a new account), 337 likes and loves, 58 reposts and 91 comments.

It’s long, but worth reading (or re-reading), as it will affect your bank account.

Keyword stuffing in your Amazon listing may soon go the way of the dinosaurs and be irrelevant for ranking on Amazon. 

A single keyword, phrase or proper flat file set up with browse nodes and optimized category attributes, along with some sales and conversions, may soon be all you need to rank on anything related to your product.

CURRENT A9 ALGORITHM

A recent deep dive published by the guys at Seller Sessions talks about how the mysterious A9 ranking algorithm at Amazon works. While generally accurate, its conclusions are based mostly on the “2016 Sorokina Paper” and testing.

The article goes in depth about some key components of the A9:  Your ol’ buddy Kevin read all 10,000+ words, and here are the highlights:

Key Components of the A9:

  • Product Search Scores: How well products match search terms.

  • Query Category Score: Gauges the relevance of a search query to a product category based on clicks, purchases, and word combinations.

  • Hunger Score: Eagerness of a category to be selected after a search.

  • In-Category Relevance Score: Assesses how closely a product matches a search query within its category.

Notable Insights from the Seller Sessions deep dive:

  • The "Honeymoon Period" post-product launch is random.

  • Cold Start Override allows for manual adjustments to rankings.

  • Amazon uses data purchases, add-to-carts, and clicks for training.

  • Major challenge: Showing results from multiple categories.

  • A9's solution: Combine search results based on historic data and predict searcher's intent using a language model.

  • Power Law Distribution affects how products are ranked based on the popularity of search terms.

  • Query Ranking Module structures and understands user queries, breaking them down for the algorithm.

  • Behavioral features, like sales and user context, play a significant role in product rankings.

  • Brands should prioritize keyword relevance, especially when launching.

COMING SOON TO THE A9 ALGORITHM

Seller Sessions’ breakdown is good, but a very recent paper gives sellers better insight into what’s probably about to happen with Amazon search results.

In a nutshell, Amazon is working on both physical GPU based technology and its own unique e-commerce LLM. It can process massive AI-related search analysis to serve up results in the blink of an eye.

This could take away the need to be indexed for everything and pretty much eliminate keyword stuffing and how we try to optimize listings to rank today.

Let me introduce you to my friend ‘BERT’ (buy him a beer so he talks more).

BERT was explained by Amazon this past May in Osaka Japan at the 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining.

You need to have a PhD in data science to deeply understand what they are talking about.  But let me break it down for you in simple language.

Traditional methods of matching products with search queries just look for exact words or phrases in the product data & descriptions. This is fast and simple but has some problems. For example, if a customer makes a small spelling mistake or uses a synonym, they might not get the results they want.

To fix this, newer methods use something called "semantic matching." This is like understanding the meaning behind words rather than just the words themselves. It's like if someone searched for "sneakers," the system would also know to show "running shoes."

Now Amazon will soon be launching a new method to improve search results using a language model called BERT.

There is a four-step method to train an e-commerce LLM model that can efficiently match queries to products to buy:

  • Domain-Specific Pretraining: Train it on e-commerce data. E-commerce language is different, so a general model won't work well.

    • They created a special vocabulary from around 1 billion product titles and descriptions from 14 languages, fine-tuning with 330 million query-product pairs using AWS data, taken from multiple countries in at least 4 languages.

    • Tools like Deepspeed and PyTorch were used, and training was done on AWS P3DN instances.

  • Query-Product Interaction Pre-finetuning: They improve the model by making it understand how search queries relate to products. They use a dataset where the queries and products have a strong relation, and they mask parts to train the model to predict the missing parts.

  • Finetuning for Matching: This stage trains the model to match queries to products. They employ a bi-encoder system which is efficient for large-scale data. The training uses a scoring system that labels pairs as positive matches, hard negatives, or random negatives.

  • Knowledge Distillation to a Smaller Model: The final step is to transfer the knowledge from the large, well-trained model to a smaller one that can work quickly in real-time for product search.

AMAZON’S PROJECT NILE TO REVOLUTIONIZE SEARCH

Amazon is developing a revolutionary AI initiative named Project Nile, which is set to transform its online search experience.

Joseph Sirosh, a former Microsoft AI executive now serving as an Amazon VP, envisions Project Nile as embedding the expertise of a knowledgeable in-store salesperson into Amazon's search function, tailoring it to each shopper's distinct preferences.

The objective of Project Nile is to enhance Amazon's search bar with advanced AI, offering real-time product comparisons, detailed information, reviews, and suggestions based on search context and user shopping history.

It has been called a “conversational search agent for customers.” It appears that it may act like a SGE (search generative experience). It could display a full list of product related results in one quick moment individually tailored using Amazon’s immense database of historical customer orders and preferences.

What is Project Nile?

  • Objective: Project Nile aims to equip Amazon's search bar with advanced AI, providing instant product comparisons, detailed information, reviews, and recommendations based on search context and user shopping data.

  • Functionality: The AI-powered search will proactively present diverse options and details when a customer searches, eliminating the need to click on each product.

  • Implementation: Originally planned for a September release, it has been delayed. Internal tests are in progress, with a potential January 2024 launch focusing on the US.

  • Mobile First: Amazon identifies a significant opportunity to boost sales via mobile devices. Currently, about 80% of Amazon searches are mobile, but its conversion rate is lower than desktop.

  • Comprehensive AI Strategy: Amazon's AI efforts aren't limited to Project Nile. They've recently enhanced Alexa with improved AI and have made significant investments in generative AI, indicating a long-term commitment to AI innovation.

  • Challenges: Ensuring accurate search results is crucial. Amazon intends to address AI inaccuracies by employing human AI trainers to review AI-generated responses and using moderation tools for sensitive queries.

A single sentence in a product listing could soon match hundreds of search phrases without keywords needed explicitly mentioning them all.

Tools like Helium 10, Jungle Scout and Data Dive will need to pivot with their keyword tools, or they could become irrelevant.

Implications for Sellers:

The new search approach seems to emulate how users research products on Google rather than Amazon. It's challenging to predict the exact changes to A9, but the semantic context of a product listing will likely become even more crucial for Amazon to extract specific product details based on customer searches.

✌🏼 It’s the heart of College Football bowl season this week. Gig ‘em!

See you again on Thursday …