How to Create a Customer Experience Dashboard in Looker with AI
Building a dashboard to track metrics isn’t the goal, the real aim is to understand the story your customers are telling you through their actions. A Customer Experience (CX) dashboard brings that story to life by unifying feedback, behavior, and support data into a single, cohesive view. This guide will walk you through how to plan and build a powerful CX dashboard in Looker, and then show you how to layer in AI for truly next-level insights.
What is a Customer Experience (CX) Dashboard?
A CX dashboard is a central hub that visualizes all the key performance indicators (KPIs) related to how customers interact with and perceive your brand. Instead of jumping between your CRM, helpdesk software, survey results, and web analytics, you see everything in one place. It moves you from isolated data points (like a single bad review or a support ticket) to a holistic understanding of the entire customer journey.
The core benefit is clarity. With a well-designed CX dashboard, you can:
Get a 360-degree customer view: See how marketing efforts impact support tickets, or how product usage influences renewal rates.
Spot problems early: Identify friction points in the customer journey before they lead to widespread churn. Are users getting stuck on a particular feature? Is response time from your support team starting to slip?
Measure what matters: Track the impact of your efforts. Did that new onboarding flow actually improve customer satisfaction scores?
Align your teams: A shared dashboard ensures that marketing, sales, product, and support are all looking at the same data and working toward the same goal: creating happier customers.
Planning Your Looker CX Dashboard
Before you build a single chart, you need a blueprint. A great dashboard answers specific questions. Without this focus, you’ll end up with a collection of charts that look nice but don’t lead to meaningful action. Start by asking what you critically need to know about your customer experience.
Step 1: Define Your Key Business Questions
Get your stakeholders together - from product, marketing, sales, and support - and agree on the most important questions you need to answer. Examples could include:
How satisfied are our new customers compared to long-term ones?
What are the most common reasons customers contact our support team?
Which customer segments are most likely to churn?
How does website engagement correlate with Customer Lifetime Value (CLV)?
Is our customer satisfaction improving over time?
Your answers to these questions will determine which metrics you need to track.
Step 2: Choose the Right CX Metrics
Once you know what you want to answer, you can select the KPIs. Group them into logical categories to give your dashboard a clear structure. Here are a few common categories and the metrics they contain:
Customer Satisfaction & Loyalty
Net Promoter Score (NPS): Measures overall customer loyalty. Usually sourced from surveys. Ask: "How likely are you to recommend our company to a friend or colleague?"
Customer Satisfaction (CSAT): Measures satisfaction with a specific interaction, like after a support ticket is closed. Ask: "How satisfied were you with this interaction?"
Customer Effort Score (CES): Measures how easy it was for a customer to get their issue resolved. Ask: "How much effort did you personally have to put forth to handle your request?"
Service & Support Performance
Ticket Volume: The total number of support requests. Track this over time to understand demand and spot trends.
First Response Time (FRT): How long customers wait for an initial reply. A key indicator of service quality.
Average Resolution Time: The average time it takes to completely resolve a customer issue.
Top Ticket Categories: What are the most common problems? This data is crucial for the product team to identify and fix root causes.
Engagement & Retention
Active Users (Daily, Weekly, Monthly): A fundamental measure of how many people are finding value in your product.
Feature Adoption Rate: Which features are your customers actually using? Spot under-utilized features that may need better promotion or improvement.
Churn Rate: The percentage of customers who stop doing business with you over a given period. The ultimate measure of a poor customer experience.
Customer Lifetime Value (CLV): Predicts the net profit attributed to the entire future relationship with a customer. Better experiences lead to higher CLV.
Building Your CX Dashboard in Looker: A Step-by-Step Guide
With your plan in place, it’s time to start building in Looker. Looker is incredibly powerful but has a notable learning curve, especially its data modeling layer, LookML. Here’s a simplified breakdown of the process.
Step 1: Connect Your Data Sources
First, you need to give Looker access to your data. Your CX data likely lives in several places:
CRM: Salesforce, HubSpot
Helpdesk: Zendesk, Intercom, Jira Service Desk
Customer Feedback: SurveyMonkey, Delighted, Natero
Product/Web Analytics: Google Analytics, Segment, Amplitude
Billing: Stripe, Chargebee
In Looker, you'll set up connections to the databases where this information is stored. This step often requires technical help, as you'll need database credentials and permissions.
Step 2: Model Your Data with LookML
This is what sets Looker apart from many other BI tools. Instead of working directly with raw database tables, you create a semantic layer using Looker's modeling language, LookML. This layer acts as a single source of truth for your business logic.
You’ll define "Dimensions" (the fields you want to group by, like 'Customer Tier' or 'Country') and "Measures" (the calculations you want to perform, like 'Average NPS Score' or 'Total Ticket Count'). This step ensures that when a team member builds a chart for "customer churn," they are using the same standardized definition as everyone else. A well-constructed LookML model prevents data chaos.
For a CX dashboard, you might join data from your Zendesk (support tickets) and Salesforce (customer account data) to be able to analyze tickets by customer size or industry.
Step 3: Create Individual Visualizations ("Looks")
Once your LookML model is set up, business users can start to explore the data without writing any SQL. In Looker's "Explore" interface, you can select dimensions and measures to ask questions of your data.
Let's create a few example visualizations for our CX dashboard:
NPS Score: Select your "NPS Score" measure. Visualize it as a Single Value visualization or a Gauge to show the C-level an instant snapshot of customer loyalty.
Ticket Volume Over Time: Choose your "Ticket Creation Date" dimension and "Ticket Count" measure. Visualize this as a line chart to see if ticket volume is seasonal or has been trending up or down.
CSAT by Support Agent: Pick the "Agent Name" dimension and the "Average CSAT Score" measure. Use a bar chart to see which agents are excelling and who might need more coaching.
Each of these charts saved in Looker is called a "Look."
Step 4: Assemble Your Dashboard
Now, bring all your Looks together. Create a new dashboard and simply add your saved Looks to the canvas. Organize them logically - maybe you have a section for high-level summary metrics at the top, followed by sections for support, engagement, and loyalty.
Add dashboard filters to make it interactive. For example, add a date filter so you can view the dashboard for "Last 30 Days" or "This Quarter." You could also add a filter for "Customer Segment" to compare the experience of your enterprise clients versus your small business clients.
Supercharging Your Dashboard with AI
A static Looker dashboard is a great start, but today’s technology lets you go much further. Integrating AI turns your dashboard from a reactive reporting tool into a proactive insights engine.
Using AI for Sentiment and Topic Analysis
Your richest feedback lives in a sea of unstructured text: survey responses, support ticket comments, and online reviews. Manually reading through thousands of comments is impossible. AI, specifically Natural Language Processing (NLP), can do it for you.
Tools can be integrated with Looker to perform:
Sentiment Analysis: Automatically score customer comments as positive, negative, or neutral. This lets you trend sentiment over time and immediately flag a rise in negative feedback.
Topic Modeling: Identify the key themes and topics people are talking about. You might discover that 20% of your negative support tickets last month all mentioned "slow loading times," an insight your product team can act on immediately.
Anomaly Detection and Forecasting
Instead of just looking at past data, AI can tell you what's unusual right now and what might happen next.
Anomaly Detection: AI models can monitor your KPIs in real-time and alert you to unexpected changes. A dashboard can show you that there was a sudden drop in CSAT, but AI can flag it the moment it happens and help you investigate why.
Forecasting: Use AI to predict future outcomes based on historical data. Looker has forecasting features built in. For example, you could forecast ticket volume to better staff your support team for an upcoming holiday season or predict your churn rate for the next quarter.
The Rise of Conversational AI in BI
Perhaps the most significant change AI brings is removing the barrier between people and their data. The traditional BI process involves finding the right filter, selecting the correct dimensions, and knowing how to build a chart. It requires training and data literacy.
Conversational AI flips this model on its head. Instead of clicking through menus, you can simply ask questions in plain English:
"Show me our NPS score from UK customers on the enterprise plan."
"What was our average first response time last week compared to the week before?"
"Create a bar chart of the top 5 reasons for customer churn this quarter."
This approach democratizes data. An account manager, a marketer, or even a CEO can get the answers they need instantly, without having to learn a complex tool like Looker or wait for a data analyst to become available. It encourages 'drill-down' curiosity, where one question naturally leads to another, allowing for deeper exploration than ever before.
Final Thoughts
Building a customer experience dashboard in Looker is a valuable undertaking that brings together disparate data sources to give you a single cohesive view of your customer journey. By carefully planning your metrics and leveraging Looker's powerful data modeling and visualization capabilities, you can create a central source of truth for your entire organization.
While Looker is powerful, its complexity can be a major hurdle. For many teams, the setup, LookML, and user training required are simply too much. This is where we built Graphed to simplify things. We connect to all your data sources - from Salesforce and Zendesk to Google Analytics and Shopify - and let you build the exact dashboards you need using simple, natural language. Instead of hours spent in LookML, just ask, "Show me a dashboard of ticket volume vs. CSAT score by support agent for the last 90 days," and we build it for you instantly. All your charts are live and interactive, giving you the power of a premier BI tool without the steep learning curve.