Stop Discounting, Start Deciding: How to Utilize Customer Data for Revenue in the Retail and eCommerce Sector

Co-founder and managing partner, driving business forward with empathy. Expert in relationship marketing and organizational change management. Supports companies in building trust and improving their marketing strategies to drive growth.

As Global Strategy Director, Wiktor Goliszek is responsible for expanding operations in international markets and strengthening the company’s presence in the CEE region. He specializes in developing marketing and digital strategies for retail and consumer brands, combining creativity, data, and technology. He focuses on loyalty, designing programs that build engagement and deliver real business value. In his work, he prioritizes rapid, effective implementation and solutions based on an understanding of customer needs and behaviors.

At Balkan eCommerce Summit 2026, Dominik Zacharewicz and Wiktor Goliszek explored how retailers and eCommerce companies can stop relying on mass discounting and instead use customer data and loyalty intelligence to drive sustainable revenue growth. The lecture focused on how most companies waste promotional budgets, misuse loyalty programs, and fail to activate the customer data they already possess. Through multiple case studies, the speakers demonstrated how data-driven loyalty strategies can reduce costs, improve retention, and increase profitability.

Most Promotional Budgets Are Being Wasted

The lecture opened with a strong statement:

30% to 50% of promotional budgets are wasted every year.

According to the speakers:
a large portion of discounts are given to customers who would have purchased anyway, even without promotions.

This problem exists because many companies:

• lack actionable customer data
• do not properly analyze buying behavior
• and rely on mass discounting instead of strategic decision-making.

The presentation argued that:

many retailers still operate with a “discount first” mindset rather than a profitability mindset.

Most Companies Collect Data but Don’t Use It

Loyalty Point introduced three types of organizations based on how they use customer data:

1. Blind Discounters

Companies without meaningful loyalty systems or customer intelligence.
These businesses rely on:

• mass promotions
• generic discounts
• and intuition-based marketing.

2. Data Collectors

Companies that already have loyalty programs and customer data,
but:

do almost nothing meaningful with it.

The data stays:

• inside dashboards
• reports
• or CRM systems,
  without influencing real business decisions.

According to the speakers:
this represents nearly 60% of companies.

3. Optimizers

Only a small percentage of organizations actively use customer data to:

• optimize promotions
• personalize offers
• improve retention
• and increase margin efficiency.

The key message was:

collecting data alone creates no value unless the organization actively uses it.

Loyalty Programs Should Be Decision Engines, Not Discount Systems

One of the central ideas of the lecture was:

loyalty programs should not function primarily as discount distribution systems.

Instead,
they should become:

• behavioral intelligence systems
• customer understanding platforms
• and decision-making engines.

The speakers emphasized that:
companies often mistake:

• plastic loyalty cards
• points
• and rewards

for actual loyalty strategy.

In reality:

the real value of loyalty programs comes from understanding customer behavior and optimizing commercial decisions.

Case Study: Reducing Costs While Increasing Loyalty Participation

The first case study focused on a large grocery retailer operating in a highly competitive, low-margin market.

The company already had a loyalty program,
but faced two major problems:

• very high operating costs
• and low customer engagement.

Only a relatively small percentage of customers actively used the program,
while the financial burden continued to grow.

Instead of removing benefits entirely,
Loyalty Point:

• analyzed transaction-level customer data
• created deep behavioral segmentation
• redesigned reward mechanics
• optimized expiration rules
• and improved communication and UX.

The result was:

lower program costs combined with higher customer participation.

An important insight from the lecture was:
customers often perceive loyalty programs as more valuable when:

• communication
• structure
• and experience improve,

even if the actual financial benefits are slightly reduced.

Discounts Don’t Create Loyalty in Low-Frequency Businesses

The second case study focused on:

Rainbow,

one of Poland’s largest travel operators.

The travel industry presents a difficult loyalty challenge because:

• purchase frequency is extremely low
• customers switch brands frequently
• and most sales happen during last-minute booking periods.

The company initially used:

• first-purchase discounts
• and generic promotions,

but these tactics failed to generate repeat purchases.

Cashback Was More Effective Than Traditional Discounts

To solve the retention problem,
Loyalty Point redesigned the loyalty strategy around:

cashback mechanics instead of immediate discounts.

The key idea was:

the reward becomes valuable only when the customer returns.

This approach:

• reduced unnecessary discounting
• encouraged repeat purchases
• and aligned promotional spending with long-term retention goals.

The presentation highlighted that:

loyalty should reward future behavior, not only current transactions.

AI and Machine Learning Improve Communication Efficiency

Another important topic was:

communication optimization through machine learning.

Loyalty Point used AI-driven models to determine:

• how often customers should be contacted
• through which channels
• and with what intensity.

This reduced:

• customer fatigue
• unsubscribe rates
• and database wear-out.

According to the lecture:
communication pressure itself can reduce loyalty performance when overused.

The company achieved:

a 24% reduction in communication-related consent withdrawals.

Margin Growth Matters More Than Revenue Growth

A strong strategic point throughout the lecture was:

loyalty optimization should focus on margin growth, not just turnover.

Instead of maximizing sales volume through discounts,
the goal became:

• protecting margins
• increasing customer lifetime value
• and improving profitability.

The speakers explained that:
successful loyalty strategies do not simply push the products customers already intend to buy.
Instead,
they:

• encourage higher-value behavior
• optimize product mix
• and drive incremental purchases.

There Is No Universal Loyalty Formula

The presentation stressed that:

every company requires a tailored loyalty strategy.

Because businesses differ in:

• customer behavior
• margins
• competitive environments
• and operational systems,

there is no single universal loyalty model.

However,
the speakers emphasized that:
the core principles remain consistent:

• behavioral segmentation
• data-driven decisions
• communication optimization
• and profitability-focused mechanics.

Key Takeaways

The lecture demonstrated that:
a significant portion of promotional spending is wasted on customers who would purchase without discounts
most companies either fail to collect customer data properly or fail to use the data they already have
loyalty programs should function as decision engines rather than simple discount systems
deep behavioral segmentation allows businesses to optimize promotions more intelligently
customer experience and communication design can increase loyalty participation even while reducing program costs
cashback and future-oriented rewards are often more effective than direct discounts for retention
AI and machine learning can improve communication timing, frequency, and channel selection
successful loyalty strategies focus on margin growth and long-term profitability rather than only sales volume
over-communication can damage loyalty and increase customer fatigue
effective loyalty programs must be tailored to the specific economics and behavior of each business.

The presentation is not available for sharing.