Collaborative intelligence applied to automotive strategy: a new model

The challenge of a change in the codes of luxury cars

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The premium automotive sector is undergoing a profound change. Classic definitions of luxury — pure performance, social status, traditional refinement — are gradually giving way to new territories of value. Sustainability, technological minimalism, on-board well-being, intuitive experience: all dimensions that redefine the codes of the sector.

This transformation poses a methodological challenge to manufacturers. How to understand such complex and emerging concepts? How to identify the consumer segments that are driving this evolution? And above all, how do you project your brand into possible futures that are still uncertain?

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Traditional study approaches face their limits

Faced with these challenges, classical methodologies show their limits:

• The standard quantitative survey Struggling to grasp the complexity and subtlety of the new premium codes
• The isolated qualitative study lack of representativeness to inform major strategic decisions
• Generic documentary research provides trends, but not brand-specific drivers of action

The challenge becomes threefold: Frame an abstract and multifaceted concept, qualify in depth the strategic segments, and projecting the implications in contrasting future scenarios.

Collaborative intelligence: orchestrate data to illuminate complexity

It is in this context that a premium car manufacturer asked us to define its strategy in the face of these sectoral changes. Our response: to deploy a hybrid research cycle, combining three complementary capabilities in a collaborative intelligence approach.

Time 1: Framing by augmented documentary research

We first conducted a multi-source documentary exploration to analyze structuring tensions of the premium car market: sustainability versus performance, minimalism versus technology, ownership versus use. This first step, based on our DeCodia capacity, made it possible to define the fundamental axes that polarize the evolutions of the sector.

Time 2: Dynamic integration of existing knowledge

Instead of starting from scratch, we have drawn from an existing strategic asset : a robust segmentation study that we had carried out for the customer in the past as well as the concept test of a new vehicle. Our work consisted of Resonate the key data of these approaches with the insights from documentary research.

We focused on the most relevant segments for the future to understand how their specific expectations and needs were linked to the new facets of premium cars.

Time 3: Strategic projection using augmented scenarios

The insights resulting from this resonance have nourished the construction of several contrasting future scenarios. Based on qualified data on the segmentation and purchase intentions of the new vehicle (carried out via a CBC), we were able to simulate the performance and relevance of the brand in each of these futures, thus identifying the most robust strategies.

A new model: from data to strategic intelligence

This approach illustrates a fundamental change. Collaborative intelligence is not about collecting more data, but about orchestrate intelligently multiple sources to shed light on complex dynamics.

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A particularly innovative aspect of this approach lies in our ability to combine previous studies and capitalize on acquired knowledge to generate new intelligence. This approach has several strategic advantages:

• Effectiveness : valorize investments already made rather than starting from scratch
• Depth : combining proven data with emerging insights
• Continuity : rely on a detailed knowledge of segments to shed light on new challenges

Orchestration as a differentiator

What sets this approach apart is the method : intentional context construction, precise query engineering, gradual alignment between the various sources. Artificial intelligence amplifies the ability to analyze, but it is human expertise that presides over the intention, frames the process and gives meaning to the results.

Deliverables that transform decision-making

Beyond the traditional report, this research cycle has produced deliverables that can be directly activated:

1. A clear and actionable definition new sectoral codes and their components
2. A thorough qualification strategic targets in the face of these developments
3. A prospective decision-making framework to guide choices in terms of innovation, communication and product positioning

Towards a new generation of strategic studies

This experience foreshadows the evolution of the profession from studies toAugmented marketing intelligence. Today's strategic challenges — transformation of sectoral codes, the emergence of new value paradigms, acceleration of decision cycles — call for novel methodological approaches.

Collaborative intelligence represents this new generation: it combines the rigor of proven methodologies with the combinatorial power of artificial intelligence, under the constant supervision of human expertise. The result: a capacity for analysis and strategic projection that qualitatively exceeds what traditional approaches allowed.

In a changing sector such as the automotive industry, this ability to analyze is becoming a decisive competitive advantage.

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