What sells?

At Balkan eCommerce Summit 2026, Gabriel Bonciu delivered a highly practical session focused on one of the most important questions in eCommerce:

What actually drives online sales performance?

Using real transaction data from the Business League ecosystem, the presentation explored how customer behavior differs across industries, how timing and traffic sources influence conversions, and why basket structure plays a major role in overall profitability.

The session provided actionable insights for online stores looking to:

• Improve conversion rates
• Increase cart value
• Reduce marketing risk
• Optimize traffic allocation
• Better align campaigns with customer behavior

Rather than relying on assumptions or generic eCommerce “best practices,” the presentation emphasized the importance of using real behavioral data to guide decision-making.

The Problem: Most Stores Optimize Based on Assumptions

Gabriel began by explaining that many online stores still make strategic decisions based primarily on:

• Assumptions
• General trends
• Industry clichés
• Incomplete analytics

However, customer behavior differs dramatically depending on:

• Product category
• Purchase intent
• Timing
• Traffic source
• Basket structure

The presentation emphasized that there is no universal growth formula that works equally well across all eCommerce verticals.

Instead, each category behaves almost like a different customer personality.

Understanding these structural differences allows brands to:

• Build more accurate marketing strategies
• Improve ROI
• Avoid unnecessary scaling risks

The Data Behind the Analysis

The presentation was based on transaction data generated inside the Business League affiliate ecosystem.

According to Gabriel:

• The ecosystem includes approximately 700 merchants
• More than 3,500 affiliates participate in the platform
• The analyzed transaction volume exceeded €30 million
• The data reflected real performance from the current year

The insights presented were based on actual purchase behavior rather than surveys or theoretical models.

This allowed the session to focus on:

• Real conversion patterns
• Real order structures
• Real traffic performance
• Real customer behavior across multiple industries

Different Categories Behave Like Different Customer Personalities

One of the strongest insights from the presentation was that every eCommerce category behaves differently.

Although all categories shared one common pattern:

• peak website traffic around 9:00 PM

almost every other metric varied significantly depending on the industry.

These differences included:

• Conversion rates
• Average order value
• Time to conversion
• Purchase timing
• Basket size
• Traffic dependence

The presentation reinforced that optimization strategies should always be category-specific rather than universally applied.

Pharma vs Fashion: Completely Different Buying Behavior

Gabriel compared several major verticals including:

• Fashion
• Pharma
• Beauty
• Personal care
• Books

to demonstrate how dramatically customer behavior changes across categories.

Pharma: Fast Intent & Quick Conversion

Pharma customers showed:

• Very high purchase intent
• Fast conversion cycles
• Approximately 32-minute average time to conversion

These shoppers often:

• Know exactly what they need
• Purchase repeatedly
• Convert quickly after visiting the website

This means pharmaceutical and health-related stores benefit heavily from:

• Fast purchase flows
• Search visibility
• Strong availability signals
• Immediate conversion optimization

Fashion & Beauty: Longer Decision Cycles

Fashion and beauty categories behaved very differently.

These customers:

• Spend more time browsing
• Compare products longer
• Convert more slowly
• Show lower immediate conversion rates

Average time to conversion reached approximately:

• 63 minutes

This makes:

• Retargeting
• Reminder flows
• Content marketing
• Social proof
• Product inspiration

much more important for these industries.

The presentation highlighted that slower conversion behavior should not necessarily be interpreted as poor performance – it simply reflects a different buying psychology.

Revenue vs Volume: What Sells Most Isn’t Always Most Profitable

Another important takeaway focused on the difference between:

• sales volume
  and
• revenue generation

According to the data:

• Fashion generated fewer units sold
• But significantly higher order value and revenue

Meanwhile:

• Pharma generated much larger transaction volume
• But lower average order value and smaller margins

The session emphasized that stores must clearly understand whether their business model is built around:

• Frequency and volume
  or
• Higher-value purchases

because these strategies require completely different optimization approaches.

Basket Structure Has a Massive Impact on Revenue

One of the strongest practical insights from the session focused on basket composition.

According to the data:

• 54% of carts contained only a single item

However:

• the highest-value orders came from baskets containing 6 or more items

Despite representing only around:

• 9% of total orders

these larger baskets generated disproportionately high revenue.

This led to one of the session’s key recommendations:
brands should actively optimize for multi-item purchases.

Strategies discussed included:

• Product bundling
• Cross-selling
• Complementary recommendations
• Cart incentives
• Volume discounts

The presentation reinforced that increasing product count per order can often improve profitability more efficiently than simply acquiring more traffic.

Timing Matters More Than Most Brands Realize

The session also explored how customer activity changes throughout the day.

Across all categories:

• website traffic peaked around 9:00 PM

However, actual purchase timing differed significantly depending on the industry.

Beauty & Books

Purchases aligned closely with evening browsing behavior.

Fashion, Pharma & Personal Care

Conversions occurred much earlier in the day, often before noon.

This creates important strategic implications for:

• Ad scheduling
• Remarketing timing
• CRM automation
• Promotional pushes
• Budget allocation

Gabriel emphasized that stores should align their marketing intensity not with when users browse, but with when users actually convert.

The Risk of Channel Dependency

Another major focus of the presentation was traffic source concentration.

Some categories showed extremely high dependence on single acquisition channels.

For example:

• Pharma generated approximately 76% of sales from Google Shopping Ads

While effective in the short term, Gabriel warned that excessive dependence on one channel creates:

• Vulnerability
• Platform risk
• Scaling limitations
• Budget instability

Meanwhile:

• Fashion and beauty categories showed more diversified traffic structures across multiple channels.

The recommendation was clear:
brands should avoid becoming overly dependent on a single traffic source whenever possible.

Fast Categories Need Fast Marketing

A major strategic framework introduced during the session was the idea that marketing speed should match category behavior.

Fast-Decision Categories

such as pharma require:

• Fast-loading pages
• Immediate intent capture
• Efficient conversion flows
• Search-focused acquisition

Slower Categories

such as fashion and beauty require:

• Longer nurturing
• Retargeting sequences
• Emotional engagement
• Inspiration-based communication

This reinforces that:
customer journey design should always match purchase psychology.

Choosing Between Volume & Value

Toward the end of the presentation, Gabriel explained that eCommerce businesses often follow one of two major monetization models:

High-Value Strategy

Fewer purchases with higher average order value.

High-Volume Strategy

More frequent purchases with lower basket value.

Both approaches can work successfully.

However, problems appear when businesses attempt to combine both without:

• clear UX logic
• proper funnel design
• strategic alignment

Hybrid models are possible, but they require much more careful optimization and customer experience planning.

Business League’s Affiliate Ecosystem

The session concluded with an overview of the Business League model itself.

The platform operates primarily on:

• cost-per-sale performance logic

meaning merchants pay based on confirmed sales rather than fixed advertising costs.

The ecosystem also provides:

• affiliate traffic
• performance insights
• category benchmarking
• optimization guidance
• dedicated account specialists

Gabriel emphasized that one of the major advantages of ecosystem-based data is that businesses can compare their own performance patterns against broader market behavior.

Key Takeaways from the Session

The presentation delivered several highly actionable lessons for eCommerce businesses:

• Different categories require different optimization strategies
• There is no universal eCommerce growth formula
• Timing and customer behavior matter more than assumptions
• Basket size optimization significantly impacts profitability
• Traffic source diversification reduces risk
• Fast-conversion industries require fast marketing systems
• Slower categories need stronger nurturing and retargeting
• Revenue and sales volume are not the same thing

Perhaps the strongest message from the session was that eCommerce growth becomes far more effective when stores stop relying on intuition and start building strategies around real customer behavior data.

As Gabriel emphasized throughout the presentation:
understanding how customers actually buy is often more valuable than simply driving more traffic.