Calculating Marketing Strategy Decisions with AHP Optimization

Choosing the right marketing strategy can be a challenging task for any business. It involves balancing investments in product features, pricing, promotions, and distribution to maximize market share and profitability.

New research by Bernardo Santos Aflalo and Takashi Yoneyama presents a systematic methodology to help companies make calculated marketing decisions by combining customer insights, competitor analysis, and optimization techniques.

Here’s how it works:

Introduction

This paper describes a methodology to support the decision making process related to the marketing strategies of a company. Information about how customers segments value a product or the provided services, as well as the potential impacts of the decision process are assessed and used in a systematic way.

The competitors’ positioning in the market are taken into account to simulate the sector’s behavior using the multicriteria decision process AHP (Analytic Hierarchy Process) in an event based context.

The simulations provide results in terms of the point elasticity of the market share according to the customers’ perceived values. These results are used as inputs

Key Steps in the Methodology

1. Market Assessment:
   
   • Understand what drives customer decisions by identifying the key product or service features they value most.
     
   • Segment customers into groups (or “personas”) based on shared preferences using tools like conjoint analysis and clustering techniques.
     
   • Evaluate how competitors perform in meeting these customer values.
     
     
2. Market Share Estimation:
   
   • Use the Analytic Hierarchy Process (AHP) to simulate customer decision-making based on their priorities.
     
   • Incorporate factors like customer loyalty (inertia) and randomness (irrationality) to estimate how different segments might choose between competitors.
     
   • Run simulations to predict how changes in product features or marketing efforts impact market share.
     
     
3. Optimizing Marketing Actions:
   
   • Analyze the elasticity of market share—how sensitive it is to changes in product attributes or marketing actions.
     
   • Use an optimization algorithm (e.g., a genetic algorithm) to identify the best combination of marketing actions that maximize gross profit.

How It Works in Practice

The methodology leverages both quantitative data (e.g., customer surveys, competitor analysis) and advanced modeling techniques:

• Customer Segmentation: Customers are grouped into personas based on their preferences, such as price sensitivity or product quality expectations.
  
• Competitor Benchmarking: Competitors’ strengths and weaknesses are rated across key attributes like advertising, delivery speed, price competitiveness, and product quality.
  
• Simulation: By modeling customer decision-making using AHP, businesses can predict how changes in their offerings or marketing strategies will influence market share.

For example, if a company improves its product quality or invests in targeted advertising, the model calculates how much these actions will increase its market share and profitability.

Benefits of the Approach

• Data-Driven Decisions: The methodology provides a clear framework for evaluating how different marketing actions impact market outcomes.
• Proactive Strategy: By simulating customer behavior and competitor responses, companies can anticipate threats and seize opportunities.
• Profit Maximization: The optimization model ensures that marketing resources are allocated to actions with the highest return on investment.

Real-World Example

Imagine a company deciding whether to invest in better advertising or improve delivery times. By inputting these options into the model along with their costs and expected impacts, the simulation reveals which action will yield the greatest increase in market share and profit. For instance:

Action	Cost	Impact on Market Share
Invest in specialized media	$10k	+0.1
Improve delivery logistics	$5k	+1.0

The optimization tool might suggest prioritizing delivery improvements if they offer a higher return relative to cost.

AHP Methodology in Marketing Strategy

The Analytic Hierarchy Process (AHP) is a structured decision-making tool designed to help organizations tackle complex problems involving multiple criteria.

Developed by Thomas Saaty, AHP breaks down decision-making into a hierarchy of goals, criteria, sub-criteria, and alternatives. It allows decision-makers to compare these elements systematically and prioritize them based on their relative importance.

AHP works through pairwise comparisons, where each criterion or alternative is evaluated against another using a scale of importance (e.g., 1 for equal importance and 9 for extreme importance).

These comparisons are then processed mathematically to calculate weights that represent the significance of each factor in achieving the overall goal. To ensure logical consistency, AHP includes a consistency ratio; if inconsistencies are detected, the comparisons can be revised.

The process is particularly useful for integrating both qualitative judgments and quantitative data in decision-making. It is widely applied across industries for tasks such as resource allocation, supplier selection, project prioritization, and marketing strategy optimization. By structuring decisions hierarchically and providing a clear framework for analysis, AHP simplifies complex problems and ensures transparency in the decision-making process.

Conclusion

This methodology combines customer insights, competitor analysis, and advanced modeling techniques to help businesses craft effective marketing strategies. By simulating market dynamics and optimizing resource allocation, companies can make smarter decisions that boost profitability while staying ahead of competitors.