What is Looker ML?

Bringing machine learning into your everyday business reports can feel like a far-off goal, but Looker and Google's BigQuery make it surprisingly achievable. The feature that bridges this gap is often referred to as Looker ML, which lets you tap into the power of predictive analytics directly within the dashboards you already know and use. This article will show you exactly what Looker ML is, how it works, and how you can use it to predict future outcomes for your business.

What is Looker ML, Really?

In simple terms, Looker ML isn't a separate product but an integration that connects Looker to Google's BigQuery ML (BQML). Think of it as a gateway allowing you to run and view the results of machine learning models from BigQuery directly inside your Looker dashboards, reports, and Explores. Instead of seeing charts about what happened last month, you can start seeing predictions about what's likely to happen next month.

Traditionally, using an ML model meant a complex process:

1. Export data from your warehouse.
2. Load it into a separate environment where a data scientist builds a model using Python or R.
3. Run the model and get predictions.
4. Export those predictions back into your warehouse or a spreadsheet.
5. Manually join those predictions with your existing business data to build a report.

Looker's integration with BQML streamlines this completely. Your data team can build models right inside BigQuery using familiar SQL commands. Then, through Looker's data modeling layer (LookML), they can expose those model predictions as if they were just another column in your database. Suddenly, "Predicted Customer Lifetime Value" or "Churn Risk Score" becomes a field you can drag and drop into any report, just like "Revenue" or "User Signups." This puts the power of machine learning into the hands of a marketing manager or sales lead, not just the data science team.

How Does It Actually Work?

Getting machine learning into your dashboards involves a few connected steps within the Google Cloud ecosystem. The beauty is that the business end-user only interacts with the final, simple step, while the technical setup happens behind the scenes.

Step 1: The Foundation - Data in Google BigQuery

Everything starts with your data warehouse. For Looker ML to work, your data must reside in Google BigQuery. This is where all your company’s information - from marketing campaigns, website traffic, sales activities, and product usage - is stored, cleaned, and organized.

Step 2: The Brains - Building Models in BigQuery ML

This is where the actual "machine learning" part happens. Data analysts or scientists can use standard SQL syntax to train, evaluate, and deploy ML models directly inside BigQuery. They don’t need to move the data or use external tools. They just write a SQL query.

For example, a command as simple as this can train a model to predict if an e-commerce customer will make another purchase:

CREATE OR REPLACE MODEL ecommerce_project.churn_model OPTIONS(model_type='LOGISTIC_REG') AS SELECT has_made_another_purchase, days_since_last_order, total_orders, average_order_value FROM ecommerce_project.customer_history

BigQuery handles the heavy lifting of training the model. Popular models you can build include:

• Linear Regression: For predicting numerical values, like forecasting next month's sales based on ad spend.
• Logistic Regression: For predicting a binary outcome (yes/no), like whether a customer will churn or a lead will convert.
• Clustering (K-means): For automatically grouping customers into different segments based on behavior.
• Time Series Forecasting (ARIMA): For predicting future values based on historical time-based data, like website traffic or inventory needs.

Step 3: The Bridge - Modeling in LookML

Once a model is trained in BigQuery, you need a way to bring its predictions into Looker. This is handled by LookML, Looker’s modeling layer. A developer on your data team defines the connection to the BQML model and makes its output available as new fields (called dimensions or measures).

They can add a few lines to your LookML project to call the prediction function from your BQML model. The end result is that a new field, let’s call it "Churn Probability," appears in Looker's UI. This field contains the live risk score - from 0 to 1 - for every customer.

Step 4: The Payoff - Interactive Dashboards in Looker

This is where everything comes together for business users. Armed with new predictive fields, you can build powerful, future-looking reports without writing a single line of code. You can filter, sort, and create charts using these predictions alongside your other business metrics.

For example, a marketing manager can now build a Looker dashboard that shows a list of all active customers, sorted by their "Churn Probability." They can immediately see who is at risk and download that list to power a targeted email retention campaign.

Real-World Examples of Looker ML in Action

Theory is great, but seeing how this technology solves real business problems is where its value truly shines. Here are a few practical use cases for different teams.

1. Marketing Teams: Smarter Spend and Higher ROI

• Predict Customer Lifetime Value (CLV): Build a model that predicts the total amount a new customer will likely spend over their entire relationship with your business. In Looker, you can then compare the predicted CLV of customers from different acquisition channels (e.g., Google Ads vs. Facebook Ads). This helps you invest your marketing budget in the channels that bring in the most valuable users, not just the most signups.
• Propensity Modeling: Create a model to predict a user's likelihood to perform a certain action, like upgrading to a premium plan or making their first purchase. You can build dashboards categorizing users into "High Propensity," "Medium Propensity," and "Low Propensity" segments to tailor your marketing campaigns accordingly.

2. E-commerce and Retail: Reducing Churn and Forecasting Demand

• Churn Prediction: This is a classic use case. Train a model on past customer behavior to identify the signals that precede a customer canceling a subscription or stopping purchases. In Looker, you can create an "At-Risk Customers" dashboard that automatically refreshes daily, giving your retention team a constantly updated list of users to proactively engage with special offers or support.
• Demand Forecasting: Use time series forecasting models in BQML to predict future sales of specific products. Your operations team can then use a Looker dashboard to visualize predicted demand and ensure inventory levels are optimized, avoiding both stockouts and overstocking.

3. Sales Teams: Prioritizing the Best Leads

• Predictive Lead Scoring: Instead of relying on manual lead scoring rules, you can build a model that analyzes the attributes and behaviors of past leads that successfully converted into customers. The model assigns a score (e.g., 1-100) to every new lead based on their conversion likelihood. Sales reps can start their day with a Looker dashboard of all new leads sorted by this score, allowing them to focus their energy on the leads most likely to close.
• Sales Forecast Accuracy: Enhance your quarterly forecasting by having BQML predict deal close probabilities based on factors like deal stage, rep activity, and company size. Presenting this alongside the sales team's manual forecast in Looker can lead to more accurate revenue predictions for leadership.

The Big Benefits of This Approach

Adopting Looker ML isn't just about creating fancy new charts, it fundamentally changes how your business operates by making data more forward-looking.

• It Makes ML Accessible: The most significant benefit is taking machine learning out of the exclusive domain of data scientists and making it a tool that marketers, sales ops, and product managers can use daily. You don't need to understand the underlying algorithm, you just need to understand what "predicted churn" means for your business.
• Insights in Real-Time: Predictions aren't served from a stale, month-old spreadsheet. Because Looker is connected live to BigQuery, the model predictions update as your data does. When a user's behavior changes, their churn risk score might change, and you'll see that updated score in your dashboard almost instantly.
• A Single Source of Truth: The entire workflow - from raw data to model training to visualization - happens within the data warehouse and your BI tool. There's no shuffling of CSV files, which minimizes errors and ensures everyone in the company is looking at the same trusted information.
• Puts ML to Work: It’s common for ML models to be built as one-off projects that never actually make it into routine decision-making. This bridge Looker provides is the perfect way to "operationalize" machine learning so its insights are used to drive actions every single business day.

Final Thoughts

Looker's native integration with BigQuery ML successfully closes the gap between historical reporting and predictive analytics. It gives businesses a practical, powerful way to see not just what has happened, but what is likely to happen next - all within the familiar environment of a BI dashboard. By streamlining this workflow, it empowers teams to make proactive, data-informed decisions that can directly impact revenue and growth.

Moving toward a predictive analytics setup like this can feel like a big leap, especially without dedicated data engineering resources. At Graphed, we’re focused on making sophisticated analytics accessible to everyone, regardless of their technical skills. We built Graphed for teams who want answers without the complexity of setting up a data warehouse or learning a BI tool. You connect your marketing and sales tools, and then simply ask questions or describe the dashboard you need in plain English. Our AI analyst builds interactive, real-time reports instantly, helping you unlock insights that were once buried in disconnected platforms.