Marketing 2030
Reference Model

Future Marketing’s Reference Model for marketing organisations that need to cater for both human attention and AI recommendations simultaneously.

The problem with marketing is not speed, but simultaneity.
Today, the same customer group makes decisions in parallel across several structurally different environments: some conduct their own research and compare options, whilst others seek advice through dialogue. Yet another group receives a shortlist generated by AI. A growing proportion is expected to delegate the procurement process entirely to an agent. These environments do not replace one another; they coexist, often within the same individuals, depending on the product, occasion and risk.

A traditional campaign can only serve one of these environments; only an operating system can handle all of them simultaneously. Our Marketing 2030 Reference Model describes how this operating model must be structured. We call it: The Marketing Growth Engine.

The two
Rails A + R

Eine Nahaufnahme des Gesichts einer Person, die ihr braunes Auge, ihre Augenbraue und den oberen Wangenbereich zeigt. Die Haut ist glatt, und die Wimpern sind lang und durch Mascara betont – ein Look, der in Beauty-Kampagnen als Leitbild für das „Marketing 2030“-Referenzmodell für zukünftiges Marketing dienen könnte.

A – Human Attention

On the ‘Human Attention’ rail, design, timing, recognition and emotion are key. Impact is created through attention, memory and trust — and this impact can be further amplified through reach.

Abstraktes digitales Bild einer leuchtend blauen, kreisförmigen Oberfläche mit segmentierten Ringen, Lichtstrahlen und Funken, die an ein futuristisches Auge oder einen Technologietunnel vor dunklem Hintergrund erinnert – und damit den Innovationsgeist des Referenzmodells „Marketing 2030“ sowie die Entwicklung des Marketings der Zukunft verkörpert.

R – AI Recommendation

At Rail AI Recommendation, clarity, structure, consistency and verifiability are key. Impact is achieved when a system correctly interprets the brand and mentions it in a recommendation. Reach cannot be bought here.

Marketing Imperative

Both channels must be managed simultaneously and consistently — the same brand, two ways of interpreting it. And where a touchpoint comes with its own AI, its nature changes: a communication asset becomes a digital product – involving operation, ongoing maintenance, responsibility and liability.

Decision
Modes

The traditional, linear and human-driven customer journey – from awareness through consideration to conversion at the company’s touchpoints – is no longer the norm when it comes to product and brand choice. It is being replaced by new journeys that reflect customers’ different decision-making modes. All of these must be catered for simultaneously. Whilst a target customer may have a basic predisposition as to how much they are open to AI-driven recommendations, they react differently depending on the reason for their search or purchase.

The Compressed Journey

The classic journey from awareness through consideration to decision-making takes place in a single interaction. What used to take weeks and involve a dozen touchpoints now happens in a single dialogue.

For marketing, this means there is no longer a middle section of the funnel where further adjustments can be made. Anything that is not available during this single interaction – proof, price, availability, comparison – does not feature in the decision-making process.

AI Shortlist & Answer Brokers

A model generates a shortlist from which a selection can be made. Whereas a search results page might previously have shown ten options, a generated answer now lists just three. As a result, visibility is more concentrated than ever before and cannot be bought.

Visibility arises when a brand can be interpreted unambiguously, is consistently substantiated and is described without contradiction across different sources.

Agent-Native Commerce

An AI agent carries out the selection, comparison and/or completion of a purchase on behalf of the customer. The purchase criteria are defined in a machine-readable format by availability, price stability, data quality in the product feed, returns and service history, and the traceability of information.

In this scenario, those who lose out do not lose out to a stronger campaign, but to a better-maintained data model.

Ambient / Visual-to-Voice Commerce

Discovery begins with an image or the surroundings and transitions immediately into speech or a transaction. A photo becomes a search query; an observation becomes an impulse to buy.

For brands, this means that visual recognition and machine identifiability become one and the same task — the product must be recognised and named before it can be advertised.

Zero-UI Advisory

Consultation takes place without a traditional user interface, through dialogue, via voice or within an assistance system. The brand no longer appears as a designed interface, but as a response.

This shifts the focus of quality: it is not how something looks that matters, but whether the underlying logic recommends the right course of action in the specific situation.

Building the
Marketing
Growth Engine

There are three design principles to consider when building the Marketing Growth Engine, which are derived directly from the marketing imperatives. They form the framework for the operational development of the engine across the four areas of operationalisation.

Ein strahlendes, diagonal verlaufendes weißes Licht durchdringt eine dunkle Oberfläche und erzeugt auf beiden Seiten einen Farbverlauf aus Blau-, Violett- und Rosatönen – ein abstraktes, futuristisches digitales Kunstwerk, das vom „Marketing 2030 Reference Model“ inspiriert ist, einer fantasievollen Vision des Marketings der Zukunft.

Integration

Brand, communication, interface and digital product are treated as a single design task. This stems from the convergence within our own AI platforms: as soon as a touchpoint provides advice and finalises a transaction, it is simultaneously both a means of communication and a product, and can no longer be treated as separate entities.

Nahaufnahme von gebogenen, dünnen Metallstäben, die in parallelen Reihen angeordnet sind und ein wellenförmiges Muster bilden. Das Foto ist in Blautönen gehalten und erzeugt einen abstrakten und modernen visuellen Effekt, der den Innovationsgeist des Referenzmodells „Marketing 2030“ widerspiegelt – und damit die Essenz des Marketings der Zukunft einfängt.

Orchestration

Rather than being organised along departmental lines, value creation is driven along cross-channel value streams. Marketing takes the lead without carrying out everything itself. It links data, touchpoints, commerce functions and teams into a continuous stream, and creates new roles to achieve this.

Bunte, verschwommene Punkte und Linien bilden ein digitales Muster auf dunklem Hintergrund, das an Datenströme, Code oder futuristische Technologie erinnert. Inspiriert vom „Marketing 2030 Reference Model“ vermitteln die Neonlichter ein Gefühl von Bewegung und Tiefe und fangen den pulsierenden Rhythmus des Marketings der Zukunft ein.

Ownership

The elements of marketing that are crucial to decision-making remain in-house: our own data and signals, our own reasoning, our own AI architecture. Ownership is the action; marketing sovereignty is the state that results from it.

The Reference Model

House of Marketing in the AI era – the Marketing 2030 Reference Model at a glance..
The starting point is the current fragmentation of target audiences, media consumption and decision-making pathways. Many customer interactions and decisions follow a dual-track approach: via human attention and via AI recommendations. Depending on the customer’s attitude towards the use of assistants, as well as the situational context of the search or purchase occasion, the same customer may exhibit different decision modes, which marketing must serve in parallel and consistently. Three design principles form the framework for establishing the necessary operating model: the Marketing Growth Engine. The engine is then set up and operated across four interdependent operational areas, it is only effective when functioning as a whole. The ultimate goal should be to achieve marketing sovereignty.

PDF

The four
operationalization
fields

Each of these four areas must be established twice – once for people, once for machines. But fully integrated
The core of the model lies in two things: that marketing for human attention and AI recommendations are built in an integrated manner, and that operations are managed via a robust orchestration function. All four operational areas must deliver this performance in a coordinated manner, only then will the Marketing Growth Engine be effective.

Action Field 1
Brand & Experience OS

Consistent principles and signal
architecture across A and R

A — Human Attention:

From static guidelines to modular, trainable principles
Experience defined by decision environment, not by channel
Recognisability of machine-generated brand messages

R — AI Recommendation:

Signal architecture, entity model
and evidence base
Consistency across own and third-party sources
Response coverage for questions critical to purchasing decisions

Action Field 2
Marketing OS

Continuous operation of parallel
Decision Environments

A — Human Attention:

Operating model for the continuous loop rather than campaign cycles
Squads, war rooms and/or decision centres rather than linear chains
AI and automation in planning, production and optimisation

R — AI Recommendation:

Set-up and operation of in-house AI interfaces: owners, SLAs, release cycles
Ongoing maintenance of feeds,
entities and responses
Protocol integration for
third-party agents

Action Field 3
Marketing Reasoning

Situational decision-making models,
separate truth system

A — Human Attention:

From fixed segments to situational decision-making models
First-party data augmented with operational decision-making and AI data
Objective: to improve decision-making logic, not just data volume

R — AI Recommendation:

Separate setup of reasoning and truth systems
Answers derived from proprietary sources rather than model knowledge
Optimisation for conversion rather than ranking

Action Field 4
Marketing Governance

Regulatory & Brand Compliance,
Liability, risk-based approval

A — Human Attention:

Governance functions and approvals carried out, in part, by agents
Responsibility remains with humans, within flatter hierarchies
From comprehensive review to risk-based approval

R — AI Recommendation:

Introduction of risk classes and an AI governance register
Clear accountability for each
risk class
Audit trail down to the model version and documented interchangeability

Zielbild ist die Marketing Sovereignty

Das bedeutet eigene Daten, eigenes Reasoning und eigene KI-Architektur verantworten und verändern können. So entsteht profitables Wachstum mit dauerhaftem Wettbewerbsvorteil.

Publications
& further
Information

In our downloads section, you will find all previous publications relating to the Marketing 2030 Reference Model, each updated to the latest version. The series is being continuously expanded.

PUBLICATIONS

The model is currently in development. We are deliberately publishing it in this state.
Do you have any questions or suggestions? Let’s have a chat – simply leave us a message!

CONTACT

Glossary

Here you will find explanations of terms we use in our publications