Analyst's Quick Start Guide

Transform your analytical workflow with BB as your intelligent analysis partner. This guide will help you get started with BB for data integration, analysis, visualization, and reporting.

BB for Analysis: Key Benefits

Seamless Data Integration

Efficiently combine and normalize data from multiple sources without getting lost in technical details. Transform raw data into structured, analysis-ready formats.

Powerful Analysis Capabilities

Leverage advanced analytical methods without specialized expertise. Uncover patterns, relationships, and insights that drive business decisions.

Dynamic Visualization

Create compelling, interactive visualizations that communicate insights effectively. Build dashboards that help stakeholders understand complex data quickly.

Setting Up Your Analysis Project

1. Project Organization

BB works best when your data and analysis materials are organized. Here's a recommended structure:

analysis-project/
├── raw-data/ # Original data sources
├── processed-data/ # Cleaned and integrated datasets
├── analysis/ # Analysis scripts and results
├── visualizations/ # Charts, graphs, and dashboards
└── reports/ # Final reports and presentations

While BB can work with any organization scheme, this structure makes it easier to track your data through the entire analysis lifecycle.

2. Adding Your Project to BB

  1. Open BB and click "Create New Project"
  2. Select your analysis project directory
  3. Choose a meaningful name for your project
  4. Configure any project-specific settings

Pro Tip: When setting up an analysis project, include documentation about your data sources and their structure to help BB understand the context.

Analysis Workflows with BB

1

Data Integration Workflow

Define Your Analysis Objectives

Begin by clearly articulating what insights you're seeking and what business questions you need to answer. This provides the context needed to guide the integration process.

Example:

"I want to understand how customer purchasing behavior relates to their demographic profiles and support interactions to identify opportunities for improving customer satisfaction and lifetime value."

Analyze Data Sources

Share your data sources with BB and discuss their structure, content, and relationships.

# Example conversation
You: I need to integrate data from our CRM (customer data), transaction system (purchases), and support ticketing system to understand relationships between customer characteristics, purchases, and support issues.

BB: I'll help with that integration. Could you share these datasets and tell me about their structure and key fields?

You: [Share datasets] The CRM data has customer profiles with demographic information. The transaction data contains purchase history with products, dates, and amounts. The support data has ticket information with type, resolution time, and satisfaction scores.

BB: Thanks for sharing these datasets. Let me analyze their structure to identify connection points.

Design Integration Approach

Work with BB to develop a plan for integrating your data sources effectively.

You: Now that you've reviewed the data, how should we integrate these sources to best understand the relationships between customer profiles, purchasing behavior, and support interactions?

BB: Based on my analysis, I recommend integrating these datasets using customer_id as the primary key. Here's my proposed integration approach...

Execute Integration

Have BB implement the integration plan, creating a unified dataset for analysis.

You: That approach sounds good. Please proceed with integrating the datasets according to this plan.

BB: I'll integrate the datasets now. This may take a few moments...

[After processing]

BB: I've successfully integrated the datasets. Here's information about the resulting integrated dataset...
2

Analysis Workflow

Exploratory Analysis

Work with BB to explore your data, identify patterns, and generate initial insights.

Example:

"I want to explore the relationships between customer demographics, purchase frequency, average order value, and support ticket frequency. Let's look for interesting patterns and potential segments."

Apply Analytical Methods

Use BB to implement appropriate analytical techniques based on your objectives.

# Example conversation
You: Based on our exploratory analysis, I want to segment our customers based on their purchasing behavior and support interactions. Then I want to analyze how these segments differ in terms of lifetime value and satisfaction.

BB: For customer segmentation, I recommend using clustering analysis based on purchase frequency, average order value, and support ticket frequency. Would you like me to proceed with this approach?

You: Yes, that sounds good. Please implement that clustering approach.

BB: I'll perform the clustering analysis now. This may take a moment...

[After analysis]

BB: I've completed the clustering analysis and identified 4 distinct customer segments. Here are the characteristics of each segment...

Interpret Results

Work with BB to make sense of analytical results and extract actionable insights.

You: Can you help me interpret these segments in business terms? What are the key characteristics and opportunities for each segment?

BB: Based on the analysis, here's how I would interpret these segments in business terms...

[Detailed interpretation with business implications]
3

Visualization and Reporting Workflow

Design Visualizations

Work with BB to create visualizations that effectively communicate your insights.

Example:

"I need visualizations that show the characteristics of our customer segments, their relative value to the business, and key factors affecting their satisfaction levels. These need to be clear enough for our executive team to understand quickly."

Create Interactive Dashboards

Develop dynamic dashboards that allow stakeholders to explore the data themselves.

# Example conversation
You: I'd like to create an interactive dashboard that allows our team to explore the customer segments, filtering by various demographic and behavioral characteristics.

BB: I can help design that dashboard. Here's my recommended approach for an interactive dashboard with these filtering capabilities...

[Dashboard design details]

Prepare Executive Summary

Create concise, insight-focused reports for decision-makers.

You: I need to prepare an executive summary of our findings with clear recommendations for action. The audience is our executive team who doesn't have time for technical details.

BB: Here's a draft executive summary that highlights the key insights and provides clear, actionable recommendations...

Complete Example: Customer Insight Analysis

This example shows a complete workflow for analyzing customer data to improve satisfaction and lifetime value, from initial objective to executive recommendations.

Initial Objective Statement

"I want to analyze our customer data to understand what factors most influence customer satisfaction and lifetime value. We need actionable insights that can help us improve our customer experience and increase revenue."

Conversation with BB

You:

I want to analyze our customer data to understand what factors most influence customer satisfaction and lifetime value. We need actionable insights that can help us improve our customer experience and increase revenue.

BB:

I'll help you analyze your customer data to identify these factors. To get started, I need to understand:

1. What customer data do you have available?
2. How do you measure customer satisfaction currently?
3. How do you calculate or define customer lifetime value?
4. Are there specific aspects of the customer experience you're particularly interested in?

You:

We have customer demographic data, purchase history, support ticket history, and satisfaction survey results. We measure satisfaction through NPS scores and support ticket resolution ratings. Lifetime value is calculated based on total purchases minus returns and support costs. I'm particularly interested in understanding the relationship between support experiences and future purchasing behavior.

BB:

Thanks for that context. Could you share the datasets so I can analyze them?

You:

[Shares customer data files]

BB:

I've analyzed the datasets and identified their structure. Here's my suggested approach for this analysis:

1. First, I'll integrate these datasets using customer_id as the common identifier
2. Then I'll perform exploratory analysis to identify initial patterns
3. Next, I'll use statistical analysis to quantify the relationships between support experiences and purchasing behavior
4. Finally, I'll segment customers and identify the key drivers of satisfaction and lifetime value for each segment

Does this approach sound right for your objectives?

You:

Yes, that approach sounds perfect. Please proceed.

BB:

[After analysis]

I've completed the analysis. Here are the key findings:

1. Support ticket resolution time is the strongest predictor of future purchasing behavior. Customers whose issues are resolved within 24 hours spend 35% more in the following quarter compared to those whose issues take longer to resolve.
2. I've identified four distinct customer segments with different value drivers...
3. For high-value customers, proactive support interactions increase lifetime value by 28%...

Based on these findings, here are my recommended actions...

You:

These insights are very helpful. Could you create visualizations that illustrate these key relationships for our executive presentation?

BB:

I'd be happy to create those visualizations. Here are the key visualizations I recommend...

[Visualization recommendations with descriptions]

Outcome

In this example, BB helped integrate multiple customer data sources, perform complex analysis, and identify actionable insights about the relationship between support experiences and customer value. The analysis provided clear recommendations that could be implemented to improve customer satisfaction and increase lifetime value.

Conversation Starters for Analysts

Data Integration

I need to integrate data from [sources] to analyze [business question]. The key connection points between these sources are [fields], and I'm particularly interested in understanding [specific relationships].

Exploratory Analysis

I want to explore the relationships between [variables] in my dataset to identify patterns and potential insights related to [business objective].

Advanced Analysis

I need to perform [analysis type] to understand [specific question]. The goal is to [business objective], and I have data on [available variables].

Visualization & Reporting

I need to create visualizations/dashboards that show [key relationships] for an audience of [stakeholders]. The key insights I want to communicate are [insights], and the desired actions are [actions].

Next Steps

Analyst's Deep Dive

Explore our comprehensive guide to using BB for data integration, analysis, and visualization.

Analysis Use Cases

See examples of how other analysts are using BB to transform their data workflows.

Thinking in Objectives

Learn how to maximize BB's effectiveness by focusing on analytical objectives rather than implementation details.

Ready to Transform Your Analysis?

Start your first analysis conversation with BB today. Remember to focus on your business questions rather than technical implementation details, and let BB guide you through the process.

Start Analyzing with BB

Last updated: March 25, 2025