How to Create a Quarterly Sales Report in Looker with AI

Creating a quarterly sales report can feel like a mountain of work, requiring you to wrangle data from your CRM, slice it up in a BI tool, and turn it into something your team can actually understand. This guide will walk you through building a powerful quarterly sales report, first covering the traditional manual approach in Looker and then showing how AI is completely reinventing the process.

What Exactly is a Quarterly Sales Report?

A quarterly sales report is a summary of all your sales team's activities and results over a three-month period. It’s more than just a collection of numbers, it's a strategic tool used to gauge performance against goals, spot trends, identify top performers, and make informed decisions for the next quarter. A well-crafted report bridges the gap between raw data from your CRM (like Salesforce or HubSpot) and actionable business insights.

While the specifics will vary based on your business, a rock-solid report should give you clear answers to these questions:

• Are we on track to hit our annual revenue goals?

• Which products or services are selling the most?

• How did this quarter's performance compare to last quarter? To the same quarter last year?

• Who are our top-performing sales reps?

• How long does it take an average deal to close, and what is the typical deal size?

• Where are our best leads coming from, and how well are they converting?

Key Metrics to Include in Your Report

To answer those questions, you'll need to track the right Key Performance Indicators (KPIs). Trying to report on everything is a recipe for confusion. Instead, focus on a core set of metrics that tell a clear story about your sales performance.

• Total Revenue: The most straightforward metric. How much money did you bring in this quarter?

• Revenue by Product/Service: A breakdown that shows which of your offerings are the most popular and profitable.

• Sales Growth: This adds crucial context. Compare your current quarter's revenue to the previous quarter (QoQ growth) and the same quarter last year (YoY growth).

• Average Deal Size: Calculated by dividing total revenue by the number of deals closed. An increasing average deal size is a great sign of health.

• Win Rate: The percentage of deals your team closes out of the total number of opportunities they worked on. (Won Deals / Total Deals).

• Sales Cycle Length: The average time it takes from the first contact with a prospect to closing the deal. A shortening cycle means your team is becoming more efficient.

• Lead-to-Close Conversion Rate: What percentage of leads ends up becoming paying customers? This helps you understand lead quality and sales effectiveness.

• Pipeline Velocity: Measures how quickly deals move through your sales pipeline, helping you forecast future revenue more accurately.

• Sales Rep Performance: Metrics like quota attainment, deals closed, and revenue generated per rep. Essential for coaching and identifying top performers.

The Traditional Way: Building a Sales Report in Looker Manually

Looker (now part of Google Cloud) is an incredibly powerful enterprise-grade business intelligence tool. For teams with dedicated data analysts, it offers deep flexibility. However, building a report from scratch often involves a manual, multi-step process that relies heavily on technical skills.

Step 1: Get Your Data Connected and Modeled in LookML

Before you can build a single chart, Looker needs somewhere to get its data. This means a data analyst or engineer must establish a connection between Looker and your data sources, whether that's your CRM (like Salesforce), a marketing platform, or your company's data warehouse (e.g., BigQuery, Snowflake, Redshift).

Then comes the most complex part: modeling the data using LookML. LookML is Looker’s proprietary language used to describe dimensions (attributes like 'date,' 'sales rep,' 'region') and measures (calculations like 'sum of revenue,' 'count of deals'). This is where the business logic lives. An analyst must manually write LookML code to define every single metric and relationship in your data. It's time-consuming and requires highly specialized knowledge, creating a bottleneck for any team wanting a new report.

Step 2: Create an "Explore" for Reporting

Once the data model is built, the analyst creates an "Explore." Think of an Explore as a curated starting point where business users can begin building their own reports. For our report, a sales analyst would create a "Sales Performance" Explore, making specific dimensions and measures available for the sales team to work with.

Step 3: Build Your Visualizations ("Looks")

Now, a user can go into the Sales Performance Explore to start building the report. To create a chart showing revenue by sales rep, you would manually:

• Navigate to the correct Explore.

• From the field picker on the left, select the "Sales Rep Name" dimension.

• Select the "Total Revenue" measure.

• Set a filter for the "Quarter" dimension, choosing the specific quarter you want to analyze.

• Run the query to generate a data table.

• Select a visualization type, like a bar chart, to display the results.

You’d have to repeat this process for every single metric and visualization you want in your report: win rate, average deal size, sales growth - each one is its own separate build, called a "Look" in Looker.

Step 4: Arrange Everything in a Dashboard

Finally, with all your individual Looks created, you can compile them into a dashboard. You add each Look to a new dashboard, then drag and drop them into a logical layout. You can also add top-level dashboard filters, such as a date range or team filter, that apply to all the charts at once. After this, your dashboard is ready to be shared with stakeholders.

This process is powerful but clearly has its challenges: it's slow, depends entirely on analysts who know LookML, and can't easily adapt to follow-up questions. If a sales manager sees a spike in revenue and asks, "Which marketing campaigns drove that spike last quarter?" they can't just get an answer. It requires another request to the data team to update the model and build a new Look, which could take hours or days.

The Future is Now: Using AI to Accelerate Reporting

The manual, click-heavy process is quickly becoming a thing of the past. AI fundamentally changes the dynamic of data analysis by removing the friction between your question and the answer. Instead of learning a complex BI tool, you can simply have a conversation with your data.

Modern AI-powered analytics tools connect directly to your data sources - like Salesforce, a Google Sheet, or Google Analytics - and let you build reports using simple, natural language.

From Clicks to Conversation

Imagine simply typing or saying what you need instead of clicking through endless menus. Instead of the multi-step Looker process, you could ask:

"Create a quarterly sales report dashboard for Q2 2024. Include total revenue, a bar chart of revenue by rep broken down by month, and a line chart of our win rate over time."

The AI understands your request, queries the underlying data, and instantly generates the entire dashboard for you in seconds. The "building" part is handled for you, letting you jump straight to the insights. This also opens the door to deeper, more fluid analysis. Got a follow-up question? Just ask it.

• "Okay, now filter this dashboard to only show the 'Enterprise' team."

• "What was our average deal size in Q2 compared to Q1?"

• "Which sales rep had the shortest sales cycle last quarter?"

This conversational approach democratizes data analysis. Your newest sales hire can get the same sophisticated insights as a seasoned analyst without needing months of training on a BI tool. It makes your entire organization more data-driven by putting the power of analysis into the hands of the people who need it most.

Best Practices for Any Great Quarterly Sales Report

Whether you're building your report manually in Looker or using AI, these principles will ensure your report is clear, insightful, and drives action.

1. Tell a Story with Your Data: Don’t just present a wall of charts. Structure your dashboard logically. Start with high-level summary KPIs (like total revenue), then dig into contributor metrics (like team breakdowns) to explain the "why" behind the numbers.

2. Focus on What's Actionable: For every chart on your dashboard, ask yourself: "What decision can we make with this information?" If a chart doesn't lead to a potential action, it might just be clutter.

3. Provide Context with Comparisons: A number on its own is meaningless. Always compare performance to previous periods (last quarter, last year) and, most importantly, against the goals or quota you set for the period. Seeing performance in red or green against a goal is immediately impactful.

4. Keep Your Visuals Clean and Simple: Use the right chart for the job. Bar charts are great for comparisons, line charts for trends over time, and scorecards for big-picture KPIs. Avoid 3D charts, unnecessary colors, or anything that detracts from a quick, clear reading of the data.

Final Thoughts

Quarterly sales reports are a critical rhythm for any successful sales organization. While powerful tools like Looker offer limitless customization for technical users, the manual process of building reports has historically been a major bottleneck. The rise of AI-powered analytics completely changes this by allowing anyone to build sophisticated reports simply by asking for what they want to see.

This shift from manual assembly to instant, conversational analysis is what we're obsessed with. We built Graphed to be your team's AI data analyst, eliminating the hours spent wrestling with complex tools. By connecting sources like Salesforce, HubSpot, and Google Analytics, you can ask questions in plain English - "prepare a dashboard showing our sales pipeline health for last quarter" - and get a full, real-time dashboard built in seconds, not days. We believe great decisions shouldn’t have to wait for a data team's help.