Introduction: Bringing Order to Client Communication

In a midsize service company, consistent and timely client contact is essential for maintaining strong relationships and driving growth. Initially, customer contacts were made in an ad hoc manner, without any fixed schedule or planning. As part of our business consulting services, we partnered with the client to analyze their processes and implement a more structured approach.

The Problem: Inefficient and Unpredictable Client Interactions

The lack of a fixed schedule led to:

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  Uncoordinated outreach causing missed opportunities.
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  Inconsistent customer experiences due to random timing.
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  Suboptimal use of the company’s resources and reduced effectiveness.

The Solution: Defining Contact Days and Times by Customer Group

We introduced a well-defined schedule that allocates specific days and times for contacting each customer segment, based on data about customer preferences and past engagement.

Key improvements included:

• Tailored contact windows aligned with customer availability.
• Elimination of random outreach in favor of planned, strategic communication.
• Improved coordination within teams ensuring consistency.

Introduction

For financial institutions, additional sales to existing customers are a critical revenue driver. Initially, our client relied on a simple, department-level statistical model, which only used limited, monthly data—restricting their ability to spot real-time sales opportunities across the whole customer base.

The Problem: Limited Insight and Low Conversion

This basic approach resulted in:

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  Offers that missed the right customers at the right moment.
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  Conversion rates that varied widely and rarely reached their full potential.
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  Lack of integration between departments, limiting the scope of actionable data.

The Solution: Automated, Real-Time Propensity Modeling

We implemented an automated process that identifies which customers are most likely to respond to additional offers at any given moment—maximizing every conversion opportunity.

Key improvements included:

• Integration of data from across all departments for a 360-degree customer view.
• Real-time modeling to dynamically target the right customer, with the right offer, at the right time.
• Automated execution freeing up teams from routine analysis and guesswork.

Introduction: Enhancing Pricing Strategy with Customer Insight

Pricing plays a critical role in profitability, yet many companies rely on simple decision models rooted in expert rules and only basic customer data. Such approaches are functional, but they rarely provide the nuance needed to fully unlock profit potential across diverse product groups.

Our client faced exactly this situation: a straightforward, rule-based decision process that did not adequately capture how different customers react to price changes.

Before: Simple Rule-Based Decision Model

The initial setup was built around a basic decision model using expert rules and limited client information. The process focused on a small set of core variables and ignored richer behavioral or contextual data, which led to:

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  Reduced ability to tailor prices to individual customer price sensitivity.
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  Missed opportunities to optimize margin for specific products and segments.
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  An inflexible strategy that struggled to keep up with changing market conditions.

Our Approach: Advanced, Explainable Price Sensitivity Modeling

To move beyond this plateau, we designed an automatic price sensitivity process that recommends an optimal price for each product, using advanced machine learning methods and explainable AI techniques.

The enhanced model incorporates:

• Advanced machine learning algorithms (including gradient boosting methods such as XGBoost) to capture non‑linear relationships between price, product attributes and customer characteristics.
• Explainable AI (XAI) tools to make complex models transparent and trustworthy for business stakeholders, including:

Thanks to these methods, the pricing team could not only see what price the model recommends, but also why a given recommendation is optimal in a specific context.

Introduction: Navigating Unseen Challenges 

The arrival of new competitors in close proximity can shake the foundations of any retail chain. For our client, the opening of rival stores translated into a sudden and significant drop in sales. But the real challenge lay deeper: Which products were being affected the most? Did the actual distance to a competitor's shop matter? Without precise answers, effective action was impossible.

Sales figures were declining week by week, yet the client lacked the clarity to determine which areas required protection or where the greatest risks existed. Without this understanding, it was impossible to prioritize actions or develop an effective response plan.

The Problem: Blind Spots in Sales Performance

Prior to our analysis, our client’s retail locations struggled with critical knowledge gaps:

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  Lack of clarity on affected product categories.
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  No data on the influence of competitor proximity.
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  Limited ability to correlate market changes with internal sales drops.

This uncertainty made it difficult to respond proactively and left each store exposed to preventable losses.

The Solution: Precise, Segmented Competition Analysis

Our team stepped in with a targeted, data-driven approach. We performed a comprehensive comparison of competitive impact by store category, mapping the sales drops across locations and linking them to specific competitors’ openings.

Key implementation steps:

• Segmentation of stores by type and market demographics.
• Statistical analysis correlating competitor proximity and sales figures.
• Identification of product categories most vulnerable to competition.
• Modeling expected decreases for each group, enabling scenario-based planning.

Introduction: Raising the Bar in Operational Risk Management

In the insurance sector, controlling operating costs while maintaining agent performance is crucial. Initially, our client used a simple decision-making model that flagged high-risk transactions based only on limited, single-transaction data. While a start, this approach was insufficient for holistic risk management.

“Managing operational risk meant constantly identifying which agents and transactions posed the greatest threats, but doing so efficiently was a daily challenge for our team,” explains the risk management lead at the insurance company we supported.

The Problem: Limited Perspective on Risk

The initial model’s narrow focus on isolated transactions meant:

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  Lack of insight into the broader behavior of insurance agencies.
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  No comprehensive view of how an agent’s overall sales portfolio influenced risk exposure.
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  Challenges in proactively managing agents contributing to high operational costs.

The Solution: Complex Algorithm-Driven Agency Assessment

We implemented an automated risk assessment process that analyzes an agency’s quality using real-time data on current behavior and complete sales portfolios. The advanced algorithm identifies agencies generating risks linked to increased operational costs, allowing dynamic and ongoing monitoring.

Results: Significant Cost Reduction

Our client achieved a 30% reduction in operating costs by focusing resources where risks were objectively highest, effectively managing their agents’ performance and related expenses.

Key results:

• 30% reduction in operating costs by focusing on high-risk agents
• Real-time automated monitoring of agent behavior and sales portfolios
• Improved prioritization and proactive management of operational risks
• Increased efficiency through algorithm-driven risk assessment
• Enhanced stability and predictability in operational performance

Conclusion: