AI Driven Cannibalization Risk Assessment Workflow Guide

AI-Enhanced Cannibalization Risk Assessment

1. Define Objectives

1.1 Identify Key Performance Indicators (KPIs)

Establish metrics to evaluate the success of location selection, such as sales forecasts, foot traffic, and demographic alignment.

1.2 Determine Scope

Outline the geographical area and specific retail chains involved in the assessment.

2. Data Collection

2.1 Gather Historical Sales Data

Utilize AI tools like Tableau for data visualization and Python libraries for data manipulation to compile historical sales data from existing locations.

2.2 Collect Market Data

Leverage AI-driven platforms such as Geolytics and Placer.ai to gather insights on market trends, consumer behavior, and competitor analysis.

2.3 Demographic Analysis

Employ tools like ESRI Tapestry and Claritas to analyze demographic data relevant to potential locations.

3. AI Model Development

3.1 Select Machine Learning Algorithms

Choose appropriate algorithms such as regression analysis for sales forecasting and clustering algorithms for identifying potential cannibalization.

3.2 Train AI Models

Utilize platforms like Google Cloud AI or AWS SageMaker to train models on historical data, ensuring they can predict sales performance and cannibalization risk.

4. Risk Assessment

4.1 Analyze Cannibalization Scenarios

Use the trained AI models to simulate various scenarios of new location openings and assess their impact on existing stores.

4.2 Evaluate Results

Generate reports using Power BI to visualize the potential impact of new locations on existing sales and identify high-risk areas.

5. Decision-Making

5.1 Stakeholder Review

Present findings to key stakeholders using interactive dashboards created with Tableau or Power BI for informed decision-making.

5.2 Strategic Recommendations

Provide actionable insights based on AI analysis, recommending optimal locations for new stores while minimizing cannibalization risks.

6. Implementation and Monitoring

6.1 Execute Location Strategy

Implement the location strategy based on AI-driven recommendations, ensuring alignment with corporate objectives.

6.2 Continuous Monitoring

Utilize real-time analytics tools, such as Google Analytics and Shopify Analytics, to monitor sales performance post-implementation and adjust strategies as necessary.

7. Feedback Loop

7.1 Collect Performance Data

Gather ongoing sales and customer feedback to refine AI models and improve future assessments.

7.2 Model Re-Evaluation

Regularly update AI models with new data to enhance predictive accuracy and adapt to changing market conditions.