More personal, faster, closer: How conversational commerce is transforming digital shopping

Definition: What is conversational commerce?

Chris Messina coined the term "conversational commerce" in 2015. What's more, the former Google developer advocate has been instrumental in driving this innovative approach forward.

Conversational commerce refers to direct, dialogue-based communication between companies and customers, e.g., via chatbots, messengers, or shopping assistants. Chatbots and similar applications are not new in themselves, but technological change—especially AI-based systems such as large language models (e.g., ChatGPT, Gemini)— is transforming simple, pre-programmed responses into genuine conversations that respond individually to users and can serve multiple customers simultaneously.

This development significantly increases the relevance of conversational commerce, as user behavior and expectations are also changing: users increasingly want to communicate in natural language and receive fast, personalized solutions.

This form of communication supports the purchasing process, making it easier, more accessible, and more personalized —for both sides. Customers ask questions, seek personal advice, or make direct purchases via the channel that is most convenient for them. Companies can thus engage in direct dialogue, build trust, and accompany their customers every step of the way through the purchasing process.

For example, you can offer them digital purchasing advice that helps them find what they are looking for quickly and efficiently.

The importance of conversational commerce for online retail

A 2021 report by McKinsey emphasizes the importance of conversational commerce. It states that 71% of consumers expect personalized interactions —and 76% are frustrated when this is not the case. In addition, 40% of fast-growing companies would increase their sales through personalization measures.

Although these figures do not explicitly refer to conversational commerce, they clearly show the overall relevance of personalization in digital commerce, which is precisely where conversational commerce comes in. Dialogue-based applications such as chatbots are particularly efficient at implementing personalized experiences.

Around 60% of users confirm that personalized recommendations from chatbots have a positive influence on their purchasing decisions, especially when these are used specifically in e-commerce or on websites. This increases brand loyalty and conversion rates. In addition, current figures show that 83% of consumers are more loyal to brands when companies actively use chatbots.

The following key figures also show how strongly conversational commerce can support online retail:

• 58% of consumers particularly appreciate the round-the-clock availability of virtual assistants.⁴ This is a clear advantage over traditional service hours.
• Chatbots successfully resolve 71% of customer inquiries, which significantly increases satisfaction.⁵
• In many cases, chatbots lead to an average conversion increase of 23% —in e-commerce, values of up to 70% are possible.
• At the same time, up to 80% of standard inquiries can be processed automatically, reducing the support workload by up to 30%.⁶

The combination of personalization and interaction is crucial, and conversational commerce brings both together. Smart, context-sensitive communication not only informs customers, but also provides them with targeted advice and ultimately motivates them to make a purchase—efficiently, individually, and scalably.

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Goals and benefits of conversational commerce

The wider the range of products and services offered on the internet, the more important customer service becomes. By engaging with potential customers early on and providing them with personalized support, you simplify and optimize their experience with your brand and your store, even on their preferred channel. After all, customers want to interact and shop where they enjoy spending time and regularly visit—whether on social media, in messenger apps, or directly in your online store. This shortens the process and allows you to identify and address requests or problems immediately, often in real time.

Channels and APIs: Prerequisites for conversational commerce

You have various options if you want to integrate conversational commerce into your processes. The key question is: Where is your target audience located, and through which channels do your potential customers communicate? Once you know this, you can specifically target the appropriate communication channels—such as messaging services or your own online store—and connect them with the necessary technical interfaces (APIs) to enable seamless, dialogue-based experiences.

Technology: How conversational commerce works

How can intelligent, smooth dialogues with customers be guaranteed? The answer lies in the interaction of various modern technologies —above all, artificial intelligence (AI). AI is a collective term that encompasses various specialized technologies that together form the technical basis for conversational commerce.

An overview:

• Artificial intelligence (AI) generally describes systems that perform tasks that typically require human intelligence, such as understanding language or making decisions.
• Machine learning (ML) is a subfield of AI. Here, systems learn independently from data in order to continuously improve their performance.
• Deep learning (DL) is a specialized method within machine learning. Based on artificial neural networks, it recognizes particularly complex relationships. Deep learning is the basis for many modern speech and image processing systems.
• Natural Language Processing (NLP) is also part of AI and enables computers to understand, interpret, and respond to human language, whether spoken or written.
• Large language models (LLMs) are particularly powerful AI models within the field of NLP. They are based on extensive language data and can generate highly complex, context-related texts or answer questions.
• Generative AI (GenAI) is an umbrella term for AI systems that can independently generate new content such as text, images, or recommendations. LLMs are an example of generative AI in the field of language.

The potential of these technologies is particularly evident in conversational commerce: NLP enables systems to understand natural language in text or speech form. LLMs such as GPT models enable particularly natural, flexible, and context-sensitive dialogues. Based on this, generative AI can even make product recommendations or handle complex customer inquiries independently. And thanks to machine learning, these systems are continuously improving through interactions, feedback, and user data.

The following graphic illustrates the relationships between the technologies mentioned. It shows how the various areas of AI interlock.

Interrelationships between key AI technologies that serve as the basis for conversational commerce (source: own illustration)

Another important aspect is semantics. Only when systems not only recognize keywords but also understand the actual meaning behind a customer query can a truly helpful, natural dialogue emerge. This ability is particularly crucial for support or purchasing advice. After all, nothing frustrates customers more than inappropriate or generic responses.

Application: AI Shopping Assistant as a possible use case

Conversational commerce can support the digital shopping process in many ways—depending on which channels your customers prefer and how they like to communicate. An AI shopping assistant is a particularly innovative and central example of this application, providing customers with targeted guidance and support during their shopping experience.

Use cases and touchpoints along the customer journey

Conversational commerce makes it possible digital shopping experience more human – from orientation to purchase and beyond. Especially in the Consideration phase does this type of digital technical advice plays a central role: it removes uncertainties, provides targeted support for decision-making, and helps to reduce the return rate.

During the consideration phase, potential customers actively compare different products in order to prepare for a purchase decision
, which is why individual advice is crucial. (Source: Own representation)

Where is conversational commerce headed?

Conversational commerce is developing steadily and rapidly. This makes it all the more important to stay up to date so you don't miss out on innovative technologies and possible uses. These developments show where the journey is headed:

• Omnichannel dialogues: Customers expect a consistent experience, regardless of which channel they are using. That is why it is becoming increasingly important to enable seamless communication across multiple touchpoints, where no information is lost and the context is preserved.
• Voice commerce: Customers no longer want to just type, they also want to be able to search and shop conveniently, hands-free, and intuitively using voice commands. This not only makes it easier to use, but also increases accessibility.
• Hyper-personalization through AI: Demands for relevance and individuality are increasing. Artificial intelligence enables you to analyze user behavior in real time and provide offers and content that are precisely tailored to their interests, preferences, and needs.

While various developments show where the trend is heading, mobile commerce has long since become a basic requirement. Conversational interfaces must therefore be designed for mobile shopping environments. Since most customers shop on the go, spontaneous, context-related purchases via smartphone have long been part of everyday life. Those who are not present here are wasting valuable potential.

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A glimpse into the future: Agentic Commerce

While conversational commerce has already established itself in many companies—often through classic solutions such as live chats or rule-based chatbots—it is continuously gaining importance due to the increasing use of intelligent AI-based systems. At the same time, agentic commerce is emerging as a possible next stage in its evolution. This refers to AI systems that not only respond to inputs but also act independently on behalf of the user —for example, by comparing offers, weighing decisions, or making recommendations without the need for direct interaction.

Interactive dialogue as we know it today is taking a back seat. Instead, AI agents are proactively taking on tasks—increasingly on behalf of their users.

According to Gartner, agent-based AI is one of the most important technology trends of 2025. Companies could use it to build a kind of virtual workforce that complements human teams, reduces their workload, and makes processes more efficient.⁷

But as promising as that sounds, the move toward autonomous AI systems also raises new questions: When can we trust AI enough to allow it to make decisions on its own? How can we ensure that these decisions are made in the interests of users? And what guidelines are needed to clearly regulate responsibility and control?

Agentic commerce is still in the future at present – but it offers exciting prospects for the further development of digital commerce.

Conclusion: Conversational commerce as the key to a modern shopping experience

Digital commerce is becoming increasingly dialogue-oriented. Conversational commerce is not a new concept, but it is gaining in importance thanks to technological advances in AI and large language models (LLMs). Intelligent, dialogue-based applications such as chatbots, voice assistants, and AI-powered shopping assistants make digital commerce more personal, direct, and efficient. This allows you to offer your customers personalized advice, fast support, and intuitive purchasing processes—right where they already interact. This not only improves your service, but also builds genuine closeness and trust with your target group. You present yourself as a modern brand that has recognized what matters: an outstanding customer experience and long-term customer loyalty. By investing in conversational commerce today, you secure the loyalty and satisfaction of your customers tomorrow.

Sources: ¹ McKinsey, ² LocaliQ, ³ Leadoo, ⁴ Nordlight Research, ⁵ Glassix, ⁶ invesp, ⁷ Gartner

Frequently asked questions about conversational commerce