Revenue growth can conceal a growing dependence on platforms. Drawing on Fraport, JUB, Kotányi and Peugeot, Miloš Suša explains how to turn first-party data, measurement and customer conversations into better business decisions.

Two companies can achieve the same revenue growth and build very different futures. After each campaign, the first understands its customers better and applies that knowledge to its offering and sales. The second primarily needs a larger budget on the same platform to achieve its next result.

The difference may be invisible in a sales report. It becomes apparent when reaching customers gets more expensive, the buying journey changes or a new competitor enters the market.

A company can grow while losing influence over the conditions for its future growth. That is why, with every major investment, we should also ask what will remain within the company afterwards and how we will use it.

When the algorithm learns faster than the company

An advertising system may become increasingly good at identifying responsive users, while the company still has little understanding of why people buy, what holds them back and why they return. Optimisation is working, but the knowledge needed for the next business decision remains scattered or inaccessible.

I see this as one of leadership’s most important responsibilities: ensuring that the organisation also learns from its investments. An insight from a campaign should improve a sales conversation. A customer’s question should reach the product development team. The team should be able to explain why it is proposing a different budget allocation.

External platforms and partners have an important role to play. I understand a company’s independence as its ability to choose: retaining access to its own knowledge and data, and using them to change how it works.

First-party data must improve decisions

Ljubljana Airport FLY, established by Fraport Slovenia and iPROM, is built on first-party data collected directly through relationships with users. Fraport Slovenia collects, manages and uses this data through iPROM Private DMP to advertise flight destinations to relevant audiences on the open web.

The AI upgrade enables continuous learning from results and adjustments to advertising decisions. According to the published case study, following the upgrade in 2024, the conversion rate increased from 12% to 15.9%, while the cost per ticket sold fell from €2.06 to €1.85.

Ljubljana Airport FLY 2.0 subsequently won Best Use of AI in Planning at the 2026 Digiday Media Buying & Planning Awards.

For me, the business value lies in the connection between data, decisions and results. The company develops a capability it can use and improve across multiple campaigns. This gives it a stronger basis for its next decisions.

Our metrics determine where we invest

Such a system needs the right metrics. If we judge success solely by immediate responses, we may undervalue activities that build future demand.

In JUB’s JUPOL campaign, we used the iPROM Brand Lift Study (BLS) to compare an ad-exposed group with a control group and measure ad recall and purchase intent. According to the published results, 71% of users for whom a positive advertising effect was measured did not click on the ad. The findings helped adjust frequency, budget and format selection while the campaign was still running.

For leadership, this makes a meaningful difference to investment decisions. We monitor purchase intent as an indicator alongside sales results. This gives us a broader basis for assessing which activities support today’s sales and which make customers more likely to think of us when they next buy.

Customer conversations can guide product development

With Kotányi AI Chef, developed by Retoba for Kotányi Slovenia, the relationship with the brand starts with a practical question: what can I cook with the ingredients I have, and which spices should I use?

Users receive help, while the company gains an opportunity to understand their needs better. According to the published project data, analysing conversations through the Retoba PulseDialog approach helped identify three new product proposals in less than five months.

A conversation that helps with today’s lunch can contribute to tomorrow’s product offering. Its value therefore extends beyond the individual interaction.

The project received gold at the Digital Communication Awards 2025 in AI Communications & Content Creation, and silver at the MIXX Awards Europe 2025 in Next-Gen Tech Activation Campaign.

For leadership, this example presents a specific task: deciding who will assess recurring customer needs and which of them the company will test in developing its offering.

Customer knowledge continues to develop after the purchase

The Peugeot 2008 AI Ambassador, developed by Peugeot Slovenia and Retoba, helps customers understand the vehicle and choose a version that suits their needs. The published comparison of periods before and after its introduction reports 30% more sales opportunities and 28% more offers prepared.

Another finding is particularly relevant to the long-term customer relationship: existing vehicle owners also turned to the ambassador for practical explanations and help with using their cars. It therefore became useful after the purchase as well.

The project won the Disruptive Practices category at the global Peugeot Awards 2024. This recognition is awarded by the brand’s leadership to local subsidiaries and importers around the world.

I see an opportunity here for companies to connect sales and support. Owners’ questions show where clearer explanations are needed and where the service could be improved. Knowledge gained after one purchase can help the next customer.

Leadership must turn knowledge into practice

I see a common direction in these examples: alongside an immediate result, an investment creates knowledge for the next step. International recognition shows that we can develop these approaches in Slovenia and gain recognition internationally. Our ambition must be to develop them over the long term together with our clients.

This will matter on new buying journeys too. I expect customers to delegate more research and individual purchasing steps to their AI agents. In agent experience, or AX, this raises the question of how an agent can understand an offering and complete a task with the user’s permission.

A company that organises its product and service knowledge, connects its data and removes obstacles to purchase today is also laying a stronger foundation for this development.

As leaders, we can start with something concrete. At the next review of growth investments, let us ask for answers to three questions about one important project:

• What have we learnt about customers, and where is that knowledge accessible?
• Which decision will we make differently as a result?
• Who will implement the change, and how will we assess its impact?

Then let us choose one change for the next quarter. It might be a better budget allocation, testing a new offering or helping customers with a recurring question. Let us assign an owner and define a measure of success.

Growth becomes more valuable when it improves our ability to generate the next phase of growth. That is the standard by which I want us to judge the value of our work for clients too.

Let us start with the decision you want to improve. Email me and let us explore the data, measurement and ways of working you need to support it.