Why data-driven sales are becoming a strategic core competency for banks and insurance companies
The round table as a starting point: Why decision-makers needed to meet to discuss data-driven sales
On November 13, 2025, executives, sales managers, data experts, and strategists from banks, insurance companies, and consulting firms gathered in Zurich for a round table discussion on "Data-Driven Sales." The aim of the meeting was not to present individual technology products or best practices in isolation. Rather, it was about developing a common understanding of how sales in the financial services sector are fundamentally changing and what role data will have to play in the future.
The starting point for the discussion was a common observation made by all participants: traditional, campaign-driven sales are increasingly reaching their limits. Today's customers are better informed, compare offers across channels, and expect to be addressed in a way that takes their individual situation into account. At the same time, regulatory requirements are increasing, margins are coming under pressure, and the costs of personal consulting are rising. In this challenging environment, it is becoming increasingly difficult for banks and insurance companies to achieve growth through product innovation or higher contact frequencies alone.
The round table was therefore deliberately chosen as a format for open and practical discussion of experiences, challenges, and possible solutions. Among other things, the discussion focused on how data-driven sales works in everyday practice, what organizational and cultural prerequisites are necessary, and why data-driven sales must be understood not as an IT project, but as a strategic management task. The contributions by Ralph Hientzsch from Consileon and Prof. Dr. Andreas Dietrich from Lucerne University of Applied Sciences and Arts provided the technical framework for this and offered numerous ideas for discussion.
Data-driven sales: From buzzword to strategic necessity
The term "data-driven sales" is still used vaguely in many organizations. It is often equated with better reporting, modern customer relationship management software, or more targeted marketing campaigns. However, the discussions at the round table made it clear that this view is too narrow.
Data-driven sales describes a paradigm shift in sales. Decisions about when to approach a customer, with what content, via which channel, and through which role are no longer made primarily on the basis of experience or gut feeling, but are systematically derived from data. The aim is not to maximize contacts, but to maximize relevance. Relevance arises when an offer or consultation precisely matches a specific need, a life event, or a change in the customer's situation.
This approach is particularly effective in the financial services sector. Banks and insurance companies have a wealth of high-quality data at their disposal, for example on transactions, product usage, contract terms, asset structures, and risk profiles. If this data is intelligently combined and analyzed, concrete recommendations for action can be derived for sales. Data-driven sales therefore means systematically using existing knowledge and translating it into value-adding customer interactions.
The limitations of the traditional distribution model
A key topic of discussion at the round table was the critical examination of traditional sales approaches. Many institutions still favor a model that is heavily campaign-oriented. Products are planned centrally, target groups are defined, and then marketed through various channels. Responsibility for success often lies with sales, which "works through" campaigns without always knowing why a customer is being targeted or how relevant the topic actually is to them.
This approach leads to several problems. On the one hand, there are high scatter losses, as many customers are contacted without having a specific need. Second, customer acceptance declines as customers become increasingly annoyed by generic offers. In addition, campaigns are often too slow to respond to short-term changes in customer behavior.
Data-driven sales addresses precisely these weaknesses. Instead of periodic campaigns, customer contacts are managed continuously on the basis of data. Analytical models identify relevant events, such as larger cash inflows, changes in savings rates, product expirations, or indications of life events. On this basis, so-called next best actions or next best offers are generated, which provide sales with concrete, prioritized recommendations for action.
Next Best Offer: The core of data-driven sales
A central element of data-driven sales is the concept of the next best offer. This is not a simple product recommendation, but rather a holistic recommendation that can include product, service, and consulting topics. The key factor is that this recommendation is context-specific and takes the customer's current needs into account.
The round table discussion made it clear that successful institutions do not view next best offers in isolation, but rather integrate them into an overarching sales architecture. Digital channels, call centers, customer advisors, and marketing all work together in this process. For example, a customer may receive a digital impulse that, if they are interested, is seamlessly transferred to a consultation. Conversely, a personal conversation can be supported by data-based information that gives the advisor additional security and structure.
Prioritization is particularly important here. Data-driven sales also means consciously deciding which issues will not be addressed. Findings about dissatisfaction, high churn probability, or ongoing complaints lead to sales initiatives being put on hold and service or retention measures being taken instead. Data-driven sales is therefore not aggressive sales, but intelligent relationship building.
Personalization as a new customer expectation
Another focus of the round table was the growing importance of personalization. Today's customers expect their bank or insurance company to understand their individual situation. Standardized approaches are becoming less and less acceptable, especially when it comes to complex financial issues.
Prof. Dr. Andreas Dietrich demonstrated that personalization goes far beyond demographic characteristics. Modern data models take attitudes, preferences, behavior patterns, and life stages into account. For example, differences can be identified between security-oriented and return-oriented customers, or between customers who attach great importance to sustainability and those for whom flexibility is paramount.
This form of personalization makes it possible to choose not only the right product, but also the right form of communication. A customer who prefers digital channels expects to be addressed differently than someone who values personal contact. Data-driven sales combines these insights, creating the basis for consistent, individual customer experiences across all channels.
Artificial intelligence as an accelerator of data-driven sales models
A key topic for the future that was discussed intensively at the round table is the use of artificial intelligence in sales. While AI is already established in areas such as fraud detection and risk management, it is becoming increasingly important in sales. AI-based models make it possible to analyze large amounts of data in real time and derive concrete recommendations from it.
The development toward agentic systems is particularly exciting. These systems are not only capable of making recommendations, but can also act independently within defined parameters. Examples include automated contract conclusions, proactive precautionary advice, and the independent processing of standardized customer requests. At the same time, the round table emphasized that such systems require clear governance structures, transparency, and ethical guidelines. Trust is a core value in the financial sector that must be strengthened, not jeopardized, by the use of technology.
Data-driven sales as an organizational and cultural issue
A key finding of the round table was that data-driven sales cannot be achieved through technology alone. Rather, it requires a cultural change within organizations. Sales, marketing, IT, and data analysis must work more closely together and pursue common goals. Managers play a central role in this by promoting data-based decisions and creating appropriate incentive systems.
In addition, the role of customer advisors is changing. Data-driven systems do not relieve them of their responsibilities, but rather support them in conducting better conversations. Successful institutions therefore invest specifically in qualification, training, and change management. Only when data is perceived as support rather than control can data-driven sales unfold its full effect.
Conclusion: Data-driven sales as a competitive advantage of the future
The round table discussion on November 13, 2025 made it clear that data-driven sales is not a short-term trend, but rather a strategic necessity for banks and insurance companies. In a market environment characterized by increasing competitive pressure, high customer expectations, and growing regulation, data-driven sales offer the opportunity to combine efficiency and customer orientation.
Institutions that consistently implement data-driven sales lay the foundation for sustainable growth, stronger customer loyalty, and a future-proof sales organization. At the same time, the round table showed that the path to achieving this is challenging and requires technological, organizational, and cultural changes. This is precisely why the open exchange that took place in this format is particularly important.
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Consileon supports banks and insurance companies on their journey toward a data-driven sales model. We support you in using your data strategically, developing relevant next-best actions, and aligning sales, marketing, and IT in a sustainable manner.