Algorithmic Targeting: Why Amazon Knows What You Want
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If you’re a business student today, you’re standing at a critical moment in time. The world of commerce has fundamentally changed. It’s no longer enough to just have a great product, you have to predict what your customer needs before they even know they need it. And no company has pioneered this shift more than Amazon.
If I were standing in front of you today, I would tell you that the principles Amazon uses aren’t a secret. This is a modern strategy that will transform your business. Every principle is a lesson you can learn and a tool you can use. The future looks bright if you can master these principles.
-Let’s do this.
Your Browsing History is a Map of Your Desires
This is the most fundamental principle. Every click, every page view, and every search query you make is a data point. The Amazon algorithm doesn't just see a list of actions; it sees a map of your interests, your style, and your intentions. Your desires aren't a secret, they're right there in your data.
How it Works: The algorithm uses your browsing history to build a dynamic profile of your interests. It maps your actions to broad categories (e.g., searching for "running shoes" and "workout clothes" maps to a "fitness enthusiast" profile) and then cross-references that with thousands of other users to start making predictions.
Resources to Implement: You can duplicate this process with simple, free tools. Google Analytics or the built-in analytics on platforms like Shopify and Squarespace track every customer's journey. They show you which products are viewed most often, where they linger, and where they abandon their cart.
Example from a Different Industry: Imagine you run a small online art supply store. A customer visits your site but doesn't buy anything. You can see they clicked on five different types of "watercolors" and then viewed a "how-to" video on watercolor techniques. This tells you they have a strong interest in a specific medium and its application.
Action Step to Implement: Go to your own website analytics. Find the "User Flow" or "Behavior Flow" report. Trace the journey of just one visitor. What did they click on? What did they view? Use that information to identify their interest and send them a follow-up email with a product or blog post related to that specific topic.
People Like You Buy Things Like This
This is the concept of Collaborative Filtering. Amazon doesn't have to know you personally. It just has to find millions of other humans who behave like YOU. When they all buy a specific item, the algorithm uses the wisdom of the crowd to predict you'll want it too. It’s the engine behind the "Customers Who Bought This Also Bought" section.
How it Works: The algorithm looks at massive customer datasets. It identifies clusters of users with similar buying patterns and behaviors. Once a new user enters a cluster, the algorithm automatically recommends products popular within that group. The more data, the smarter the prediction.
Resources to Implement: Most modern e-commerce platforms have built-in "Related Products" features that use basic collaborative filtering. To do it yourself, you can use a CRM or email marketing tool. By manually creating customer segments, you can identify patterns.
Example from a Different Industry: Let's say you have a specialty coffee subscription service. You notice that 75% of your customers who bought the "Sampler Pack" also went on to buy the single origin coffee from Puerto Rico. This is your pattern.
Action Step to Implement: Look at your own sales data. Find a common product pairing. Then, send a targeted email to everyone who has purchased the first product, recommending the second one. This is a simple, manual version of Amazon's collaborative filtering.
Timing is Everything
This is a crucial component of the algorithm's predictions. The most valuable recommendation is the one that arrives at the perfect moment. Amazon tracks not just what you buy, but when you buy it, anticipating when your supply might be running low or when a seasonal interest will return.
How it Works: The algorithm analyzes a customer's purchase frequency. For a product like coffee beans or skincare, it can establish a "purchase cycle." It then uses that data to schedule a reminder or promotion just before the customer is likely to run out. It's a predictive, rather than reactive, approach.
Resources to Implement: This is where email marketing automation platforms like Klaviyo and Mailchimp are incredibly powerful. You can set up automated email flows that are triggered by a specific date, like 30 days after a customer's last purchase.
Example from a Different Industry: You own an online store selling protein powder. You see that your average customer re-orders every 45 days.
Action Step to Implement: Set up an automated email that sends a personalized reminder to reorder your protein powder to every customer 25 days after their last purchase. This simple trigger-based email acts like a small, highly effective algorithm.
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The Power of Micro-Segmentation
Back in the day, marketers would group people into broad, ineffective buckets like "moms" or "teenagers." Amazon's algorithm can create thousands of ultra-specific micro-segments, like “college students who love ramen and gaming keyboards.” The smaller and more specific the segment, the sharper the targeting and the more personal the message feels.
How it Works: The algorithm uses a vast number of data points, from browsing to purchase history and beyond, to identify incredibly niche groups. It then serves each group content and products that are highly relevant, making every interaction feel like a one-on-one conversation.
Resources to Implement: Most modern email marketing and CRM (Customer Relationship Management) platforms have advanced segmentation features. You can create custom segments based on specific purchases, location, or even engagement with past emails.
Example from a Different Industry: Imagine you run a local bakery. You can create a segment for "Customers who have purchased a birthday cake in the past 12 months" and another for "Customers who only buy pastries on Saturdays."
Action Step to Implement: Go to your customer list. Create a single, specific segment based on a past action, like "Customers who bought Product A in the last 90 days but have never bought Product B." This group is a goldmine for targeted marketing.
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Feedback Loops Make It Smarter Every Day
The algorithm's greatest strength is its ability to learn and evolve. It never assumes its predictions are perfect. It constantly tests and refines its recommendations based on real-world behavior. If you ignore a recommendation, the algorithm learns. If you click on one, it learns even more.
How it Works: Every customer action is a data point in a continuous feedback loop. A recommendation is made. The customer's response (click, ignore, buy) is recorded. The algorithm then adjusts its model to make a better prediction next time. It’s a relentless process of testing, learning, and improving.
Resources to Implement: The "A/B testing" feature in most email marketing and e-commerce platforms is your most valuable tool for creating feedback loops. It allows you to test two different versions of an email or webpage to see which one performs better.
Example from a Different Industry: A local music streaming service recommends a playlist to a user. If the user skips the first three songs, the algorithm learns that the playlist is a bad match and adjusts its next recommendation immediately.
Action Step to Implement: For your next marketing email, create a simple A/B test. Test two different subject lines. For example, "Coffee is running low" vs. "Time for a refill?" Whichever subject line gets more opens is the one you should use in the future. You have just completed your first feedback loop.
Get With The Program.
Amazon algorithm isn't magical. It’s systematic. It’s about gathering the right data, spotting patterns, and predicting behavior with precision. You don’t need to build the next Amazon to use this mindset. You just need to be a student of your customer.
The most successful businesses are those that never stop learning. By applying these simple principles and using your available resources, you will be well on your way to building a powerful, intuitive business that not only understands what its customers want, but will out-strategize the competition.
Let's get to work. 💯
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3 Simple Steps To Apply Concepts
Start Gathering Customer Data
You can't make predictions without data. The first step is to set up a system to track customer behavior. This doesn't require a massive investment—just an obsession with understanding what your customers are doing.
Your Action Step: Open your website analytics (Google Analytics or Shopify/Squarespace Analytics) and find the "User Flow" report. Trace the journey of just one visitor. What did they click on? What did they view? Use this information to identify a single product or topic they are interested in.
2. Automate a Simple Prediction
Once you have some data, you can start making a prediction. Take the patterns you've identified and automate a response. This is your first small-scale algorithm.
Your Action Step: Go to your customer list or CRM. Find a common product pairing from your sales data. Set up an automated email using a tool like Mailchimp or Klaviyo to recommend the second product to everyone who has purchased the first.
3. Create a Feedback Loop
The most powerful algorithms never stop learning. Your final step is to start a feedback loop to improve your predictions over time.
Your Action Step: For your next marketing email, create a simple A/B test. For example, test two different subject lines to see which one gets more opens. Use the result to inform your next campaign. You have just completed your first learning loop, just like Amazon.