Revolutionizing Pricing: AI's Impact on Telecoms

Table of Contents

1. Introduction
2. The Limitations of Conjoint Analysis
   • Statistical Limitations
   • Hypothetical Buying Situations
   • Difficulty in Differentiating between Channels
   • Inaccuracy in Replicating Actual Market Share
   • Speed to Insight
3. The Virtual Shopper Model
   • An Extension of Conjoint Analysis
   • How the Model Works
   • Using Real Sales Data
   • Updating the Model with Data Insights
4. Case Study: Optimizing Pricing and Product Portfolio
   • Example of Price Optimization
   • Benefits of Using the Virtual Shopper Model
5. Conclusion
6. Next Steps
7. FAQs

Beyond Conjoint Analysis: How AI Is Revolutionizing Pricing and Product Optimization for Telecoms

In today's rapidly evolving telecom industry, pricing and product optimization have become increasingly important for carriers, particularly in the B2C sector. With inflation and technological advances on the rise, telecom companies are seeking innovative solutions to address pricing challenges and maximize profits. This article explores how AI is transforming pricing and product optimization through the use of the Virtual Shopper Model, going beyond traditional conjoint analysis. By leveraging machine learning and real sales data, telecom companies can gain valuable insights and make data-driven decisions to stay competitive in the market.

Introduction

At the recent MWC conference, pricing was a hot topic of discussion among telecom professionals. With rising inflation and technological advancements, telecom companies are facing numerous challenges in finding the right prices and optimizing their product portfolios. This article aims to shed light on how AI, specifically the Virtual Shopper Model, is revolutionizing pricing and product optimization for telecoms, especially in the B2C sector.

The Limitations of Conjoint Analysis

While conjoint analysis has long been used as a valuable tool for understanding customer preferences, it has its limitations in the telecom industry. These limitations include statistical constraints, hypothetical buying situations, difficulties in differentiating between channels, inaccuracies in replicating actual market share, and the slow speed to insight.

Statistical Limitations

Conjoint analysis is often Based on a small sample size, making it challenging to accurately predict market share fluctuations. With only a thousand or two thousand participants, representing one percent market share may be as little as 10 to 20 people. This impacts the precision of predictions, leading to less accurate results.

Hypothetical Buying Situations

Conjoint analysis presents participants with hypothetical purchasing decisions, which may differ significantly from real-world buying behaviors. Customers' actual choices and responses to price variations in the market are not fully captured in these hypothetical situations, affecting the validity of the results.

Difficulty in Differentiating between Channels

Conjoint analysis struggles to differentiate between different sales channels. Most customers use both online and offline channels, making it challenging to determine their preferences accurately. Prices and competitive sets can vary significantly across different channels, resulting in ambiguous insights from conjoint analysis.

Inaccuracy in Replicating Actual Market Share

The results obtained from conjoint analysis often do not Align with the real market share. The predictions derived from conjoint studies require extensive adjustments to match the actual sales figures, indicating a disparity between the study outcomes and the market reality.

Speed to Insight

Conjoint analysis is a time-consuming process, taking weeks or even months to deliver actionable insights. In today's rapidly changing telecom landscape, businesses require faster and more agile solutions to keep up with the market dynamics.

The Virtual Shopper Model

The Virtual Shopper Model offers a groundbreaking alternative to traditional conjoint analysis, revolutionizing pricing and product optimization for telecoms. Combining the power of machine learning and real sales data, this model provides a more accurate and comprehensive approach to understanding customer behavior and making data-driven decisions.

An Extension of Conjoint Analysis

The Virtual Shopper Model builds upon the strengths of conjoint analysis while addressing its limitations. By creating a large set of virtual shoppers that emulate real customers' behaviors, this model incorporates customers' preferences, price thresholds, and behavioral traits into the analysis. It enables telecom companies to capture the complexity of the actual buying decision process more effectively and gain insights that align with real-world market dynamics.

How the Model Works

The Virtual Shopper Model utilizes extensive sales data from multiple periods to determine realistic customer valuations of different product attributes. By assessing the impact of price changes and promotions on actual sales volumes, the model understands customers' willingness to pay in various scenarios. This allows telecom companies to simulate market responses accurately and optimize their pricing and product portfolios accordingly.

Using Real Sales Data

In addition to using real sales data for parameterizing the model, the Virtual Shopper Model can incorporate insights from other data sources such as conjoint studies and pricing surveys. By continuously updating the model with the latest data, businesses can stay informed about customer preferences, track changes in market dynamics, and make well-informed pricing decisions.

Updating the Model with Data Insights

The Virtual Shopper Model offers flexibility in updating and refining the understanding of customer behavior. Whether it is updating the model with the latest sales data, conducting pricing experiments on new features, or incorporating expert judgment, the model can adapt to changing market conditions and deliver accurate predictions for telecom companies.

Case Study: Optimizing Pricing and Product Portfolio

To better illustrate the effectiveness of the Virtual Shopper Model, let's explore a case study on optimizing pricing and product portfolios. In this example, a telecom company used the model to analyze its pricing strategy for different tariff plans and their features. By considering real sales data and simulating various pricing scenarios, the company identified an optimal pricing strategy that resulted in a significant increase in profitability.

Example of Price Optimization

Using the Virtual Shopper Model, the telecom company analyzed four tariff plans with different features and prices. By applying the model to real sales data and simulating price changes, the company optimized its pricing strategy to maximize profitability. The results showed that adjusting prices for specific plans led to a more profitable portfolio, resulting in a substantial increase in revenue per customer.

Benefits of Using the Virtual Shopper Model

The Virtual Shopper Model offers several advantages over traditional conjoint analysis in pricing and product optimization:

1. Utilizes real sales data: By using actual sales data, telecom companies gain more accurate insights into customer behavior and market dynamics.

2. Captures contextual buying decisions: The model considers real-world buying situations, accounting for the complexity and variability of customers' preferences and behaviors.

3. Enables accurate price predictions: With the combination of machine learning and real sales data, the model accurately predicts customer responses to price changes and promotions.

4. Provides faster insights: Unlike traditional conjoint analysis, the Virtual Shopper Model delivers Timely and actionable insights, allowing businesses to make more agile decisions.

5. Enhances profitability: By optimizing pricing and product portfolios based on accurate demand forecasts, telecom companies can unlock higher profits and gain a competitive edge in the market.

Conclusion

In today's dynamic telecom industry, pricing and product optimization are vital for carriers to thrive in the competitive market. The Virtual Shopper Model offers a revolutionary approach to pricing analysis, leveraging the power of AI and real sales data. By going beyond traditional conjoint analysis, telecom companies can gain valuable insights into customer preferences and behavior, enabling them to optimize pricing strategies, maximize profitability, and stay ahead of the competition.

Next Steps

To unlock the full potential of the Virtual Shopper Model and revolutionize your pricing and product optimization strategies, here are the next steps:

1. Evaluate your Current pricing and product portfolio challenges.
2. Assess the availability and quality of your sales data and other Relevant data sources.
3. Connect with our team to discuss how the Virtual Shopper Model can be tailored to your specific needs.
4. Implement the model and start gaining valuable insights for pricing and product optimization.

Contact us today to embark on the Journey of transforming your pricing strategies and revolutionizing your telecom business.

FAQs

1. How does the Virtual Shopper Model handle B2B contexts?

   At present, the Virtual Shopper Model is primarily tailored for the B2C sector, specifically small and medium-sized businesses (SMBs). However, further development of the model to accommodate B2B contexts is underway.

2. How are utilities calculated from sales data?

   The Virtual Shopper Model leverages machine learning algorithms to analyze the impact of price changes, promotions, and other variables on sales volumes. By assessing customer responses to these changes observed in sales data, the model derives utilities that reflect customer valuations and preferences.

3. Can the Virtual Shopper Model be used for forecast demand and sales?

   Yes, the Virtual Shopper Model can be utilized for forecasting demand and sales based on simulated pricing and product scenarios. By leveraging machine learning and real sales data, telecom companies can gain accurate insights into future market trends and make informed decisions.