A practical guide to predictive recommendations in 2025

We’ve all seen it happen. You’re browsing a website, and a product recommendation pops up that’s so on-point, it’s almost creepy. Or maybe a support chatbot gives you the exact help article you need before you even finish typing your question. It might feel like a bit of magic, but it’s really just the power of predictive recommendations at work.

This AI-driven approach is a big deal for improving the customer experience and, let's be honest, for bringing in more revenue. But how does it actually work under the hood? This guide is here to pull back the curtain on predictive recommendations. We’ll walk through the old-school methods, talk about their real-world problems, and show you a more modern, accessible way to get these results without the enterprise-level headaches.

What are predictive recommendations?

At its heart, using predictive recommendations is about using past data and AI to guess what a customer will need or want next, and then offering it up before they have to ask.

It’s a huge step up from basic personalization rules, like "if a customer buys a coffee maker, then show them coffee filters." Instead of sticking to rigid, pre-programmed logic, predictive models sift through thousands of data points like clicks, purchases, support tickets, and viewing history to find connections you wouldn't spot on your own.

The goal is pretty simple: stay one step ahead of the customer. By anticipating their needs, you can keep them engaged, make them happier, and point them toward a solution, whether that’s a product they’ll end up loving or an answer that solves their problem right away.

How traditional predictive recommendations work

If you’ve ever found yourself wondering how Netflix just knows what show you’ll want to binge next, you’ve witnessed a massive recommendation engine in action. These systems are powered by some seriously complex machine learning models that process staggering amounts of data.

While the engineering behind them is incredibly advanced, the basic ideas are fairly easy to grasp. Most traditional platforms use a combination of a few model types:

• Collaborative filtering: This is the classic "people who liked this also liked that" idea. It works by finding users who have similar tastes and then recommending items that one person has liked but the other hasn't seen yet.

• Content-based filtering: This approach looks at the features of the items themselves. If you’ve watched a bunch of sci-fi movies starring a certain actor, it will probably suggest other movies with that same actor or in that same genre.

• Hybrid models: The most effective systems, like the ones at Netflix and Amazon, mix both of these methods together to get the sharpest results.

Trying to build something like this yourself is a huge undertaking. It requires a few things that most companies don't just have on hand: massive amounts of clean, historical data; a full team of data scientists and engineers; and a pretty big budget for servers and infrastructure.

Common predictive recommendations platforms and the reality of their pricing

For businesses that don’t have the same resources as a tech giant, the usual solution is to use a managed service from one of the big cloud providers. These platforms give you the power of predictive recommendations without needing a full-blown AI department. But that power comes with a price tag, and it's often a lot more complicated than it seems.

Let's take a look at the big three and what you can really expect.

Amazon Personalize

Amazon Personalize is AWS's managed machine learning service for creating personalized recommendations. It’s the same tech that Amazon.com uses, so you know it’s the real deal.

• Pricing: Amazon uses a consumption-based model. You get charged for the data you feed it (per gigabyte), the time it takes to train your model (per hour), and the real-time recommendations it serves up (per 1,000 transactions).

• The reality: While this pay-as-you-go model sounds flexible, it makes it nearly impossible to predict your costs. A busy holiday season or a marketing campaign that takes off could make your bill shoot through the roof without warning. Budgeting turns into a constant guessing game.

Azure AI Personalizer

Microsoft's Azure AI Personalizer is a cloud-based tool that uses reinforcement learning to help you create personalized user experiences.

• Pricing: Just like AWS, it’s mainly transaction-based. You pay for the number of recommendation calls your app makes. It's also important to know that Microsoft announced the service will be retired in October 2026, which is a major risk for anyone building on it today.

• The reality: You’re stuck with the same unpredictable costs you'd have with AWS. On top of that, the looming retirement date means any work you put in now will have to be redone in a couple of years, forcing you into a costly and disruptive switch to a different platform.

Google Cloud Vertex AI Search for commerce

As part of the wider Google Cloud family, Vertex AI Search for commerce gives you tools for personalized search and recommendations, all built on Google’s powerful AI.

• Pricing: Google’s model is also usage-based, billing you per 1,000 requests. But it gets tricky because they have different rates for search versus recommendation queries, and you also have to pay separate costs for model training and tuning.

• The reality: The complicated pricing structure is a headache. Trying to track different rates for different services makes it tough to get a clear handle on your total spend, and it’s way too easy for costs to get out of hand.

Platform	Pricing Model	Key Challenge
Amazon Personalize	Per GB, per training hour, per 1k transactions	Highly unpredictable and variable costs.
Azure AI Personalizer	Per 1k transactions	Unpredictable costs and the platform is being retired.
Google Vertex AI	Per 1k requests + training fees	Complex, multi-variable pricing is hard to forecast.

The limitations of traditional predictive recommendations

The unpredictable pricing is really just the beginning of the problems. For most businesses, especially customer-facing teams that need to be nimble, these traditional platforms come with a lot of practical drawbacks.

• They take months to go live: Getting one of these systems up and running is a marathon. It involves long implementation cycles with developers, data prep, model training, and lots of tweaking before you ever see a single recommendation. You often won't see any real value for months.

• They are a "black box": You get very little say in why the model recommends one thing over another. Trying to customize the logic or add your own business rules is often difficult, if not impossible. You're basically at the mercy of the algorithm.

• They are risky to deploy: What if the recommendations are just… bad? Off-base suggestions can frustrate your customers and damage your brand. Most of these platforms don't offer a good way to test how they’ll perform in the real world before you flip the switch.

• They only use part of your knowledge: These systems are usually trained on product catalogs and user clicks. They completely miss the goldmine of information hidden in your support tickets, help center articles, and internal team documents.

A smarter way to deliver predictive recommendations with eesel AI

The truth is, you don’t need a Netflix-sized recommendation engine to give your customers a predictive, personalized experience. Modern tools like eesel AI are built to give you the benefits of predictive recommendations without all the enterprise-level cost and complexity.

Instead of trying to build complicated models from the ground up, eesel AI connects all of your company's existing knowledge to give customers and support agents the right answers and automations, right when they need them.

A workflow diagram showing how eesel AI automates customer support, providing a modern alternative for predictive recommendations.

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Go live in minutes, not months

You can skip the long sales calls and mandatory demos. eesel AI is completely self-serve. You can connect your helpdesk, like Zendesk or Freshdesk, with a single click and have a working AI agent running in minutes. You can get started all on your own and see results on the very first day.

Unify all your knowledge to power predictive recommendations

What really sets eesel AI apart is its ability to learn from all your different knowledge sources. It trains on your past support tickets, help center articles, and internal wikis in places like Confluence or Google Docs. This gives it a complete picture of customer problems and their solutions, which leads to incredibly accurate and helpful responses that feel truly predictive.

This infographic illustrates how eesel AI unifies knowledge from various sources to power its predictive recommendations.

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Test with confidence using powerful simulations

Worried about letting an AI loose on your customers? eesel AI lets you run a simulation to see how your AI agent would perform on thousands of your past tickets in a safe environment. You can see exactly how it would have responded, predict its resolution rate, and tweak its behavior before it ever talks to a live customer. It takes all the guesswork out of deployment.

eesel AI's simulation feature allows you to test the effectiveness of your predictive recommendations in a safe environment before going live.

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Get total control and transparent pricing

With eesel AI, you’re always in control. Our customizable workflow engine lets you decide exactly which tickets the AI should handle and when it should loop in a human agent. And our pricing is simple and predictable. We don't charge you per resolution, so you'll never get penalized for having a busy month. It’s a flat, transparent fee you can actually plan for.

eesel AI offers transparent and predictable pricing, a key advantage for businesses using predictive recommendations.

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Stop guessing, start using predictive recommendations

Predictive recommendations are a seriously powerful tool for any business. But for a long time, the technology was locked up in enterprise solutions made for companies with huge data science teams and endless budgets.

For most businesses, especially support and success teams, a more practical and affordable solution is needed. It’s time to step away from the complexity and start delivering personalized, predictive experiences today. By using the knowledge you already have, you can get ahead of customer needs and offer proactive support that builds loyalty and helps you grow.

Ready to see how easy it can be to automate support with an AI that learns from your business? Try eesel AI for free and go live in minutes.