Kick-off Kumo in Snowflake with Smarter Product Recommendations via Snowflake Solution Center

Improve average order size with targeted product recommendations

by Zach Drach

In today’s digital landscape, where information overload and choice paralysis are the norm, recommendation systems are pivotal in guiding users toward the most relevant content. These systems provide a more personalized customer experience, improving consumer happiness and your bottom line. 

Used by retailers, media, entertainment, advertisers, and more – Kumo powered recommendation systems are relied upon by leading organizations to drive key KPIs like average order size, sales, and even monthly active users. Kumo now offers its recommendation solution to interested customers directly via the Snowflake Solution Center and our Snowflake Native App, bringing the powerful predictive platform directly into the Snowflake environment.

Why Kumo in Snowflake

Kumo saves time during predictive model development because it eliminates the complex process of rebuilding the end-to-end pipeline to suit each new query. Kumo helps data scientists make highly accurate predictions about user and customer behavior by combining graph learning over structured data and generative AI models trained for unstructured data – all entirely within Snowflake. 

Named Forbes AI 50 in 2024, Kumo’s predictive AI learns across multiple Snowflake tables, to build accurate predictive models in hours – not months. The out of the box machine learning models can be refined for maximum performance and predictions can be delivered in batches or as embeddings for use downstream.  Use a low-code interface to simplify MLOps, combine graph learning and LLMs, and use Kumo to accurately predict the right products, actions, and offers for each person to drive improvements to KPIs like conversion, engagement, and retention.

⭐ If you want to try this yourself, download the Kumo app here and dig into the solution here.  ⭐

What you’ll build: 

Using Kumo as a Snowflake Native App, you will build predictive models for determining customer lifetime value (LTV) and delivering top ten product recommendations to those customers, all within your Snowflake environment. You can easily access those predictions from within a Snowflake worksheet, and deploy these predictions to your production website or email notification system.

Kumo’s solution includes a step-by-step guide that users can use to follow along: 

1. Setting Up Data in Snowflake
2. Registering Your Schema in Kumo and Create Your Graph
3. Building Your Predictive Models
4. Evaluating Model Performance
5. Productionizing Your Model

Related Resources: 

Recommendations quickstart guide
Kumo Documentation
Snowflake Native Apps
Snowflake Snowpark Container Services
Predictive Query Tutorial
Installing Kumo on SPCS