What Your Customer Calls Are Trying to Tell You

Martin Kokalov is CEO and Co-founder of Omnilinx, a SaaS company that helps modern businesses improve customer communication through both technology and practical implementation. With a telecom engineering background, he built a leading business telephony platform that later evolved into Omnilinx, bringing phone, chat, messaging, and email into one omnichannel customer communication platform.

Vera Ivanova is Marketing Director at Omnilinx, a B2B communication platform that helps companies turn customer interactions into a structured, measurable process. She leads marketing strategy, positioning, and go-to-market across Omnilinx’s portfolio – from cloud PBX and contact center operations to AI-powered conversation analytics. With a background in consultative B2B marketing, Vera focuses on bridging the gap between technology capabilities and real business outcomes. At BES 2026, she moderates a session on what customer calls reveal about missed revenue, service friction, and automation opportunities.

Martin Srebrov is the founder and CEO of Sky Holding, a Bulgarian group of companies with leading positions in real estate, construction, and lending. Over the past 18 years, the group has developed more than 100,000 sq. m of built-up area and contributed to the sale of over 30,000 properties. Its portfolio includes some of Bulgaria’s most recognized real estate brands, including Home2U, INclusive, beSOLD, CreditMall, and TERITORIA.

At Balkan eCommerce Summit 2026, Martin Kokalov, Vera Ivanova, and Martin Srebrov participated in a discussion focused on one of the most overlooked business assets in modern customer experience management:

customer conversations.

The session explored how phone calls, chats, emails, and customer interactions contain valuable business intelligence that most companies still fail to analyze effectively.

The discussion focused on:

• what businesses can learn from customer conversations,
• why dashboards alone are not enough,
• how AI changes customer communication analysis,
• why structured data matters,
• and how centralized communication systems can improve both customer experience and operational scalability.

Although part of the discussion came from the real estate industry, many of the lessons applied directly to eCommerce, customer support, sales operations, and digital customer journey management.

Dashboards Show Volume – But Not Meaning

The discussion opened with an important observation:
most businesses today already have dashboards.

They can track:

• number of calls,
• missed calls,
• response time,
• average handling time,
• chat volume,
• ticket volume,
• and general communication statistics.

However, according to Martin Kokalov, what companies still often lack is:
an understanding of what actually happens inside those conversations.

Businesses may know:

• how many calls happened,
  but not:
• where customers hesitate,
• why deals are lost,
• when frustration appears,
• or why customers disappear after positive interactions.

The discussion emphasized that:
metrics alone rarely explain customer behavior.

The Invisible Gap Between Good Metrics & Lost Sales

Martin Kokalov shared a practical example from sales teams.

A company may appear operationally healthy:

• all calls are answered,
• response times are fast,
• conversations sound professional,
• and dashboards show no major problems.

Yet sales performance may still underperform.

Why?

Because inside the conversations, patterns emerge that traditional dashboards cannot detect.

For example:
customers may repeatedly ask:

• about pricing,
• product features,
• or next steps,

and sales representatives may answer correctly – but fail to move the conversation toward commitment.

The salesperson never asks:

• “Would you like an offer?”
• “Should we schedule a meeting?”
• “Would you like to move forward?”

The call ends politely, but the sale never progresses.

According to Martin, this is one of the biggest blind spots in customer communication management:
companies see activity,
but not conversational patterns.

AI Makes Conversation Analysis Scalable

A central topic throughout the discussion was the role of AI in analyzing customer communication at scale.

Modern AI systems can now analyze:

• phone calls,
• emails,
• chats,
• support tickets,
• and customer sentiment

to identify:

• recurring problems,
• emotional signals,
• sales friction,
• operational weaknesses,
• and coaching opportunities.

Instead of manually reviewing isolated conversations, companies can now identify:

• patterns across hundreds or thousands of interactions.

This allows businesses to move from:
reactive management
toward
systematic operational improvement.

The Real Value Is in the Patterns

One of the strongest messages during the discussion was that businesses should avoid overreacting to isolated customer interactions.

Martin Kokalov warned against:

• listening to one negative call
  and
• immediately changing processes.

Instead, companies should:

• review larger samples,
• identify recurring themes,
• and analyze broader behavioral patterns.

Sometimes the issue is:

• employee performance.

But often the real problem lies elsewhere:

• broken processes,
• unclear policies,
• poor customer journeys,
• insufficient training,
• or unrealistic expectations.

AI analysis becomes valuable when it helps organizations identify:
what repeatedly goes wrong across the system.

Sky Holding’s Journey Toward AI & Structured Data

Martin Srebrov shared the perspective of a large operational business managing:

• real estate agencies,
• digital lead generation,
• and high-volume customer communication.

Although not a traditional eCommerce company, Sky Holding operates with:

• online lead generation,
• digital customer journeys,
• and high-intensity customer interactions,

making the challenges very similar to modern eCommerce operations.

Martin explained that two years ago, the company started exploring AI technologies and consulting with various providers.

Very quickly, one recurring issue became obvious:
everything depends on data quality.

The Biggest Challenge: Data Structure

One of the most important realizations for Sky Holding was that most of their existing customer data:

• was manually entered by employees,
• inconsistently structured,
• and often unreliable.

This created a major obstacle for implementing AI effectively.

According to Martin:
AI is only valuable when businesses have:

• enough data,
• clean data,
• centralized data,
• and structured data.

Without that foundation, advanced analytics become extremely difficult.

The discussion reinforced a broader message:
AI does not magically fix operational chaos.
Businesses still need proper systems and data infrastructure first.

Why Centralized Communication Systems Matter

Sky Holding eventually implemented centralized phone systems and communication tracking across the organization.

This created several immediate benefits:

• visibility into actual communication volume,
• better operational transparency,
• standardized customer interaction tracking,
• and centralized reporting.

Martin explained that before implementing these systems, the company relied heavily on:
manual reporting from employees.

However, employee-reported activity often differed from reality.

Once communications became centralized, management gained:

• objective operational visibility,
• accurate activity tracking,
• and measurable customer interaction data.

Customer Calls Reveal What CRM Systems Miss

One of the strongest themes throughout the discussion was that:
customer calls often reveal information that CRM systems alone never capture.

Employees may not always report:

• customer frustration,
• objections,
• dissatisfaction,
• hesitation,
• or emotional reactions.

But these signals become visible through conversation analysis.

Martin Srebrov emphasized that customers often communicate dissatisfaction long before escalation occurs.

Without analyzing conversations, businesses usually discover problems:
too late.

AI-powered communication analysis allows organizations to:

• identify emotional warning signs earlier,
• improve processes proactively,
• and reduce customer frustration before escalation happens.

AI Can Be More Honest Than Employees

One memorable point from the discussion was Martin Srebrov’s observation that:
“AI is sometimes more honest than the employee.”

When companies rely only on internal reporting, important problems may remain hidden.

Employees naturally avoid emphasizing:

• failed conversations,
• customer dissatisfaction,
• or weak performance.

AI analysis introduces:

• objectivity,
• consistency,
• and large-scale pattern recognition.

This helps management better understand:

• customer expectations,
• employee behavior,
• and operational bottlenecks.

Scaling Requires Systems & Technology

Another major theme was scalability.

Martin explained that smaller businesses can often operate successfully without:

• advanced dashboards,
• AI systems,
• or centralized communication platforms.

However, once companies begin scaling:

• complexity increases rapidly.

Managing:

• hundreds of employees,
• large customer volumes,
• and decentralized communication

becomes almost impossible without structured systems.

Sky Holding currently manages:

• approximately 450 employees,

and Martin emphasized that scaling management capacity traditionally requires:

• larger management teams,
• longer training periods,
• and operational expansion.

AI and centralized systems help reduce this dependency by giving managers:

• better visibility,
• faster insights,
• and more scalable oversight.

Communication Data Is the “Blood System” of Sales Organizations

A particularly strong analogy from the discussion described calls as:
“the blood system of every sales organization.”

Regardless of industry, customer communication remains central to:

• sales,
• customer experience,
• support,
• and operational quality.

This is especially true for:

• real estate,
• high-ticket sales,
• consulting,
• and customer-driven industries.

The discussion emphasized that businesses often underestimate how much strategic information exists inside:

• phone calls,
• support conversations,
• and customer interactions.

AI Should Support – Not Replace – Teams

The panel also addressed growing conversations around:

• voice bots,
• AI chatbots,
• and automation.

Martin Kokalov advised businesses not to focus immediately on replacing people with AI.

Instead, companies should first:

• centralize communication,
• structure their data,
• understand customer flows,
• and analyze conversations properly.

Only then can automation become effective.

The discussion emphasized that businesses are not ready for advanced AI automation unless they can already:

• understand customer journeys,
• structure operational data,
• and predict communication needs.

Observation Creates Competitive Advantage

Toward the end of the discussion, Martin Srebrov shared a practical insight that inspired many of Sky Holding’s decisions.

He noticed that even courier companies:

• record calls,
• centralize communication,
• and analyze interactions.

This raised an important question:
if even highly transactional businesses track communication quality, why shouldn’t companies managing complex customer relationships do the same?

The conclusion was simple:
every customer interaction contains operational signals.

Companies that observe and analyze those signals gain:

• operational clarity,
• faster learning,
• and stronger customer experience management.

Key Takeaways from the Discussion

The discussion delivered several highly practical insights for businesses managing customer communication at scale:

• Dashboards show operational volume but often miss conversational meaning
• Customer calls contain valuable business intelligence
• AI helps identify patterns that humans often overlook
• Structured and centralized data is essential for effective AI implementation
• Customer dissatisfaction often appears inside conversations before escalation happens
• Communication analysis can reveal process problems, not only employee mistakes
• Scaling organizations require centralized systems and operational visibility
• AI should initially support understanding and coaching before replacing human interaction
• The quality of customer conversations directly impacts sales and customer experience

Perhaps the strongest message from the discussion was that businesses already possess enormous amounts of customer insight – hidden inside their daily conversations.

The real challenge is no longer collecting data.

The challenge is finally understanding what customers are trying to say.

The presentation is not available for sharing.