Shaped Review 2024: What It Is, How to Use It & Is It Worth It?

Solves personalization use-cases with bespoke models.

Shaped logo

User-friendly design

Cost-effective solution

Handles scalability and reliability

Shaped Description

Shaped is a tool designed for personalization use-cases, allowing users to create bespoke models once their data is connected. It doesn't require a minimum amount of data, only interactions such as clicks, views, and impressions. The tool is designed to be user-friendly, with resources like docs and blogs available for more information. Shaped is also a cost-effective solution, eliminating the need for hiring multiple machine-learning engineers, and the time-consuming process of building and maintaining a custom ML stack. It handles scalability and reliability, allowing users to go from 0 to 1 in just a few days. The tool has been used successfully in various case studies, with users reporting significant increases in engagement.
  • Free plan
  • Paid
  • Free trial

Shaped Detailed Review

So, let's dive a bit deeper into Shaped, shall we? One of the first things you'll notice about this tool is how it simplifies the process of connecting your data. Whether you're using BigQuery, PostgreSQL, or any other database, Shaped has got you covered. It securely connects to your data stack, ingesting your data without the need for setting up complex logging infrastructure. This is a huge time-saver, especially for businesses that are just starting to explore the world of AI and machine learning.

Once your data is connected, Shaped takes over the heavy lifting of training your bespoke recommendation model. It's not just about picking a model and running with it, Shaped actually looks at your data types, volume, and schema to determine the best recommendation models for your specific use-case. It then A/B tests these models to ensure that the one with the highest uplift is the one that gets served to your users. This level of customization is a big plus, as it ensures that your recommendations are as relevant as possible.

Shaped doesn't just stop at deploying your model, it continuously improves your recommendation system over time. It uses the data connections to ingest the most up-to-date user, item, and event data, retraining your recommendation models to pick up on any shifts in your data distribution. This means that your recommendations stay fresh and relevant, even as trends change.

Now, let's talk about use cases. Shaped has been successfully used in a variety of scenarios, from creating a TikTok "For You" feed to categorizing Airbnb listings. This versatility is a testament to the tool's adaptability and effectiveness in different contexts. Whether you're running an e-commerce site or a content platform, Shaped can help you provide personalized recommendations that increase engagement.

But no tool is perfect, and Shaped is no exception. While it does a great job of simplifying the process of creating recommendation models, it may not be the best fit for businesses that require a high level of control over their models. Also, while Shaped is a cost-effective solution compared to hiring a team of machine-learning engineers, it's still an investment. Businesses will need to weigh the potential increase in engagement against the cost of the tool.

In conclusion, Shaped is a powerful tool for businesses looking to leverage AI and machine learning to provide personalized recommendations. Its ability to connect to various data stacks, customize recommendation models, and continuously improve these models over time make it a strong contender in the AI tool market. However, businesses should consider their specific needs and budget before deciding if Shaped is the right tool for them.