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How to Create a Customer Experience Dashboard in Looker

Cody Schneider•

A customer experience (CX) dashboard pulls all your scattered customer data into one clear, centralized view. Instead of patching together insights from your helpdesk, CRM, and survey tools, you get a single source of truth to monitor customer health and make smarter decisions. This guide will walk you through how to properly plan and build a powerful customer experience dashboard using Looker.

Why a CX Dashboard is Non-Negotiable

Your customer data lives everywhere: support tickets in Zendesk, customer details in Salesforce, satisfaction scores in SurveyMonkey, and product usage in your application database. A CX dashboard doesn't just display this data, it connects it. It's the difference between seeing that 100 tickets were created yesterday and discovering that 75 of those tickets came from new customers using a specific feature.

A well-built dashboard helps you:

  • Spot Problems Proactively: A sudden spike in tickets related to a new feature launch becomes immediately visible, allowing your product team to investigate before it escalates.

  • Understand Trends: Is Customer Satisfaction (CSAT) improving month-over-month? Is the First Response Time (FRT) getting shorter? A dashboard visualizes these trends so you can see if your initiatives are working.

  • Align Your Teams: When marketing, sales, support, and product teams all look at the same data, they can work together more effectively. A product manager might notice low adoption of a new feature while the support manager sees an increase in related tickets, prompting a collaborative solution.

  • Prove the Value of Your Efforts: By correlating improved CX metrics (like faster resolution times) with business outcomes (like lower churn), you can clearly demonstrate the ROI of investing in a better customer experience.

Before You Build: Planning Your Dashboard

Jumping directly into Looker without a plan is a recipe for a cluttered, confusing dashboard that nobody uses. The most effective dashboards are designed with a clear purpose before a single chart is created. Start by answering these questions.

1. Who is the Audience and What Do They Need to Know?

A dashboard for a Head of Support will look very different from one for a CEO. Define your audience and their primary goals. Don't try to build one dashboard for everyone, it's better to have a few focused dashboards than one that tries to do everything.

  • For a Support Manager: They need operational metrics. Think real-time ticket volume, agent performance leaderboards, average resolution time, and tickets by priority/status.

  • For a Product Manager: They care about the intersection of product usage and customer feedback. Your dashboard might show NPS scores segmented by feature usage, ticket volume related to a new software release, or feature adoption rates.

  • For an Executive Team: They need a high-level overview of customer health. Focus on KPIs like overall NPS, CSAT trends, churn rate, and Customer Lifetime Value (CLV).

2. Which Metrics and Data Sources Will You Need?

Once you know who you're building for, you can decide what to show them. List the key performance indicators (KPIs) and the data sources where those metrics live. This map will be your guide for connecting your data in Looker.

Here’s a breakdown of common CX metrics and their typical sources:

Key Metrics

  • Satisfaction & Loyalty:

    • Net Promoter Score (NPS): Measures long-term customer loyalty.

    • Customer Satisfaction (CSAT): Measures short-term satisfaction with a specific interaction (e.g., a support ticket).

    • Customer Effort Score (CES): Measures how easy it was for a customer to get their issue resolved.

  • Support Performance:

    • Ticket Volume (Created vs. Solved): Tracks the overall workload of the support team.

    • First Response Time (FRT): How quickly an agent first responds to a customer.

    • Average Resolution Time (ART): The average time it takes to completely resolve a ticket.

  • Business Impact:

    • Churn Rate: The percentage of customers who stop using your service over a given period.

    • Customer Lifetime Value (CLV): The total revenue you can expect from a single customer account.

    • Renewal Rate: For subscription businesses, the percentage of customers who renew their contracts.

Common Data Sources

  • Help Desk Software: Zendesk, Intercom, Freshdesk (for tickets, CSAT, agent performance).

  • CRM: Salesforce, HubSpot (for customer account details, company size, contract value).

  • Survey Tools: SurveyMonkey, Delighted, Typeform (for NPS and CES).

  • Product Analytics Tools: Mixpanel, Amplitude, Pendo (for user engagement and feature adoption).

  • Company Database: Your internal database often holds the master record for users, subscriptions, and revenue data (PostgreSQL, Redshift, BigQuery, etc.).

Connecting Your Data with LookML

Looker's power comes from its modeling layer, LookML. Before you can build charts, you need to teach Looker about your data - what your tables are, how they relate to each other, and how metrics should be calculated. This ensures that when someone on your team includes "Total Revenue" in a report, it's always calculated the same way.

Defining your data model is a more technical step and is typically handled by a developer or data analyst.

  1. Connect to Your Database: In Looker's admin section, you’ll start by creating a connection to the databases where your data resides (e.g., Snowflake, BigQuery, Redshift).

  2. Create a LookML Project: Looker will automatically generate a set of base LookML files for the tables in your database.

  3. Define Views and Joins: A view in LookML corresponds to a data table (like tickets from Zendesk or accounts from Salesforce). You will define the relationships between these views using a join in an Explore file. For a CX dashboard, you might join your tickets data to your users data on a user_id.

  4. Define Dimensions and Measures: In each view file, you define your fields.

    • Dimensions: These are the "group by" fields - the attributes of your data. Examples include Ticket Creation Date, Customer Tier, or Ticket Priority.

    • Measures: These are the aggregations or calculations you perform on your data. Examples include Count of Tickets, Average Resolution Time, or Average CSAT Score.

This upfront modeling work is what makes self-serve analytics in Looker possible. Once configured, a business user can simply select "Average Resolution Time" without ever having to write SQL or know how the calculation is performed behind the scenes.

Building Your CX Dashboard in Looker: A Step-by-Step Guide

With your data models established in LookML, it’s time to start visualizing your data and building the dashboard itself.

Step 1: Create Visualizations from an Explore

An "Explore" is the starting point for any analysis in Looker. It's an environment where a user can select dimensions and measures to ask questions of their data.

Let's create our first component, or "tile": a trend of your CSAT scores.

  1. Navigate to the Explore you or your data team created (e.g., "Customer Support Insights").

  2. From the field picker on the left, select your CSAT measure, such as Average CSAT Score.

  3. Next, select a date dimension, like Ticket Solved Date, and pivot on it.

  4. Apply a filter to the date, for example, "is in the last 90 days."

  5. Click Run. Looker will query your database and return the data.

  6. Under the "Visualization" tab, select the line chart icon.

You now have a line chart showing your CSAT trend over the past 90 days. Next, you need to save it so you can add it to a dashboard.

From the gear icon in the top right, select Save > As a New Look, give it a clear title like "CSAT Trend (Last 90 Days)," and save it to a folder.

Step 2: Build Tiles for Your Other Key Metrics

Repeat the process for the other key metrics on your plan. Here are a few more tile examples:

  • Top-Level KPIs (Single Value tiles):

    • Dimensions: None.

    • Measures: Overall CSAT Score, Total Open Tickets, Average First Response Time.

    • Visualization: Single Value. These are great for displaying headline numbers prominently at the top of your dashboard.

  • Tickets by Priority (Bar Chart tile):

    • Dimension: Ticket Priority (e.g., Urgent, High, Normal, Low).

    • Measure: Count of Tickets.

    • Visualization: Bar Chart or Pie Chart. This helps you quickly see the composition of your open tickets.

  • Avg. Resolution Time by Agent (Table tile):

    • Dimension: Agent Name.

    • Measures: Count of Solved Tickets, Average Resolution Time.

    • Visualization: Table. This builds a simple performance leaderboard.

Step 3: Assemble Your Dashboard Layout

Once you’ve saved all your individual Looks, you’re ready to put them together.

  1. Navigate to the folder where you saved your Looks and click New > Dashboard.

  2. Give your dashboard a name, like "Customer Support Performance."

  3. Click Add Tile and choose "Looks" to select the visuals you just created.

  4. Drag and Drop: Arrange your tiles in a logical order. A common practice is to place high-level KPIs (your single value tiles) across the top, followed by trends and more detailed breakdowns below. Resize tiles as needed for emphasis.

Step 4: Add Filters for Interactivity

A static dashboard provides a snapshot, but an interactive dashboard empowers users to find their own answers. Filters are essential.

  1. In dashboard edit mode, click Filters > Add Filter.

  2. Let's add a date filter: give it a name like "Date Range" and select a time-based field to filter on, like Ticket Created Date.

  3. In the "Tiles to Update" tab for that filter, select every tile you want this filter to control.

  4. Click Save. Now users can change the date range of the entire dashboard on the fly. You can add other useful filters like Support Agent Name, Customer Tier, or Product Line.

Tips for an Actionable Dashboard

  • Keep it Clean: Resist the urge to add every possible metric. A crowded dashboard is an overwhelming one. Stick to the metrics identified in your planning phase that directly relate to your goals.

  • Use Descriptive Titles: Don't leave your charts with default titles. A clear title like "Daily Ticket Volume vs. Solved Rate" gives immediate context to the viewer.

  • Schedule and Share: Looker allows you to schedule a dashboard to be sent out automatically via email or to a Slack channel. Set up a regular delivery to keep key stakeholders informed without them having to remember to check.

  • Review and Iterate: A dashboard is a living document. Check in with its users periodically. What's useful? What's confusing or missing? Use their feedback to refine and improve the dashboard over time.

Final Thoughts

Building a customer experience dashboard in Looker is a process that blends thoughtful planning with technical execution. By defining your audience, selecting purposeful metrics, properly modeling your data, and composing a logical layout, you create a powerful resource that transforms raw data into a clear plan of action for your teams.

The process of connecting sources and modeling data in tools like Looker takes a significant amount of time and technical know-how. To help marketing and sales teams get answers faster, we built Graphed for this very reason. You can connect sources like Salesforce, Zendesk, and Google Analytics in a few clicks, and then just ask questions in plain English - like "show me ticket volume by priority from Zendesk for last month" - and instantly get a real-time dashboard without months of setup or needing a data engineer.