Setting Up Local Falcon with Oracle AI: A Complete No-Code Guide

What if we told you that you could have AI-powered conversations about your Local Falcon data without writing a single line of code? Well, Oracle's AI tools are one option you have to make this possible through a visual, point-and-click approach.

This guide will walk you through connecting Local Falcon to Oracle Analytics Cloud (OAC) and Digital Assistant, creating an intelligent system that can analyze and discuss your local search performance.

Prerequisites

Before starting, ensure you have:

• Oracle Analytics Cloud subscription
• Oracle Digital Assistant access
• Local Falcon Data Retrieval API key
• Oracle Cloud Infrastructure (OCI) account
• Administrator access to your Oracle environment

Part 1: Setting Up a Data Connection in Oracle Analytics Cloud

The first step towards having natural language conversations about your local search performance is to set up a data connection between Local Falcon and Oracle Analytics Cloud.

Creating Your Initial Connection

1. Log in to Oracle Analytics Cloud
2. Click "Create" in the top menu
3. Select "Connection"
4. Choose "REST API Data Source"
5. Fill in the connection details:
1. Name: Local Falcon Data
2. Description: Local Falcon API Connection
3. Base URL: https://api.localfalcon.com/v1
4. Authentication Type: API Key
5. API Key Header: api_key
6. API Key Value: [Your Local Falcon API Key]
6. Click "Test Connection" to verify
7. Save your connection

Creating Datasets

You'll need separate datasets for each type of Local Falcon data:

Connected Locations Dataset

1. Click "Create Dataset"
2. Select your Local Falcon connection
3. Choose "/connected-locations" endpoint
4. Configure the data preview:
1. Select relevant fields
2. Rename columns for clarity
3. Set data types appropriately
5. Save as "LF_Locations"

Scan Reports Dataset

1. Create another dataset
2. Choose "/scan-reports" endpoint
3. Add parameters:
1. start_date: Current_Date - 30
2. end_date: Current_Date
3. limit: 100
4. Save as "LF_Rankings"

Trend Reports Dataset

1. Create a third dataset
2. Select "/trend-reports" endpoint
3. Configure parameters:
1. place_id: [Optional]
2. keyword: [Optional]
3. limit: 100
4. Save as "LF_Trends"

Part 2: Configuring Oracle Digital Assistant

After you've set up the data connection between Local Falcon and Oracle, you can start configuring Oracle's Digital Assistant to get it working just how you want.

Creating Your Assistant

1. Open Oracle Digital Assistant
2. Click "Create Assistant"
3. Fill in details:
1. Name: Local Falcon Analyzer
2. Description: AI Assistant for Local Falcon data
3. Version: 1.0
4. Click "Create"

Setting Up Skills

You'll need to set up three key skills in order for Digital Assistant to analyze your locations, rankings, and trends:

Location Analysis Skill

1. In your assistant, click "Add Skill"
2. Select "Create New"
3. Choose "Business Analytics" template
4. Configure intents:
1. Show locations
2. Compare locations
3. Location performance
5. Add training phrases:
1. "Show me all locations in [city]"
2. "How many locations do we have in [state]?"
3. "Compare performance between [location1] and [location2]"
4. "Which locations need attention?"

Rankings Analysis Skill

1. Create another skill
2. Connect to rankings dataset
3. Add intents:
1. Check rankings
2. Analyze trends
3. Compare keywords
4. Add training phrases:
1. "How are our rankings in [location]?"
2. "Show me ranking trends for [keyword]"
3. "Compare rankings between [date1] and [date2]"
4. "Which keywords are underperforming?"

Trend Analysis Skill

1. Create a third skill
2. Link to trends dataset
3. Configure intents:
1. Historical analysis
2. Pattern detection
3. Seasonal trends
4. Add training phrases:
1. "Show historical performance for [location]"
2. "What patterns do you see in our data?"
3. "Analyze seasonal trends for [keyword]"

Part 3: Building Interactive Dashboards

Once you've configured Digital Assistant, you can create interactive dashboards to help you visualize data and get the most out of future analyses.

Creating the Main Dashboard

1. In Oracle Analytics Cloud
2. Click "Create Dashboard"
3. Start with a blank canvas
4. Add title and description

Adding Key Visualizations

Ranking Overview

1. Add line chart
2. Configure:
1. X-Axis: Scan Date
2. Y-Axis: Average Ranking
3. Color: Location

Location Performance Map

1. Add map visualization
2. Configure:
1. Location: Address
2. Color: Average Ranking
3. Size: Number of Keywords

Keyword Performance Table

1. Add pivot table
2. Configure:
1. Rows: Keyword
2. Columns: Location
3. Values: Current Ranking, Ranking Change

Part 4: Setting Up Automated Insights

Lastly, you should automate notifications and alerts for certain critical insights to ensure you don't miss any major local search performance updates.

Configuring Natural Language Insights

1. Navigate to Insights settings
2. Create new insight type:
1. Name: Ranking Changes
2. Data: LF_Rankings
3. Threshold: 3 position change
4. Frequency: Daily

Setting Up Alerts

Create alert rules:

• Ranking Drops: > 5 positions
• Grid Coverage:
• Competitive Changes: New competitors

Configure notification settings:

• Email alerts
• Dashboard notifications
• Digital Assistant messages

Using Your AI-Powered System

After completing the above setup steps, you should be all set to start using your new Local Falcon-Oracle AI integration to gain real-time, conversational insights into your local search performance. Here are a few examples of how those conversations might look:

Example Conversations

Basic Performance Check:

You: "How are our Chicago locations performing?"

Assistant: "Based on the last 30 days of data:

• 12 locations in Chicago metro area
• Average ranking: 4.2 (up 0.8 positions)
• Best performing: Michigan Ave location (avg rank 2.1)
• Areas needing attention: South Loop (dropped 3 positions)"

Trend Analysis:

You: "Show me ranking trends for 'emergency dentist' across all locations"

Assistant: "Here's what I found:

• Overall upward trend in last 90 days
• Weekday rankings better than weekends
• Top performer: Downtown location (consistently top 3)
• Notable pattern: Rankings improve after hours updates
• Recommendation: Update hours for remaining 3 locations"

Competitive Analysis:

You: "What competitive changes have you noticed in the Dallas market?"

Assistant: "Analysis of Dallas market shows:

• 2 new competitors appeared in top 5 this month
• Main competitor dropped from grid in 3 locations
• Your average position improved by 1.2 spots
• Opportunity: Target 'emergency services' keywords where competition is weakening"

Action Recommendations:

You: "What actions should we prioritize this week?"

Assistant: "Based on current data, here are priority actions:

Houston Memorial location:

• Update business hours (outdated by 30 days)
• Respond to 3 new reviews

Phoenix Downtown:

• Rankings dropped for key terms
• Add missing service categories

Miami Beach:

• Competitive pressure increasing
• Need photos in 2 categories"

Best Practices for Interactions

Be Specific:

• Instead of: "How are we doing?"
• Better: "Show ranking trends for dental implants in Chicago for the past 30 days"

Follow Up Questions:

• Ask for clarification on patterns
• Request specific recommendations
• Compare with historical data

Action Planning:

• Ask for prioritized tasks
• Request impact estimates
• Get specific implementation steps

Regular Monitoring Suggestions

Daily Checks:

• Ranking changes > 3 positions
• New competitor appearances
• Review response needs

Weekly Analysis:

• Overall trend direction
• Competitive landscape changes
• Content update needs

Monthly Review:

• Performance by market
• Seasonal pattern analysis
• Strategy effectiveness

Remember that the AI assistant can help identify patterns and suggest actions, but final decisions should always involve human judgment and local market knowledge!

Wrapping Up

In this guide, you've seen how to set up an AI-powered, no-code integration between Local Falcon and Oracle AI, enabling natural language interactions with your local search data. With Oracle's tools, analyzing and discussing local performance metrics is just a few clicks away. By creating datasets, configuring Oracle Digital Assistant skills, and setting up dashboards and automated insights, you've transformed Local Falcon data into actionable intelligence!

But Local Falcon users aren't limited to Oracle's AI tools for conversational data analysis; you can explore similar conversational insights within Local Falcon itself! Through Falcon Assist, Local Falcon's built-in AI copilot, you can ask questions about your data using natural language queries and receive real-time insights, with no technical setup required.