Conversational marketing: AI and data serving the customer

[👉 Summarise this article using ChatGPT, Google AI or Perplexity.]

According to a HubSpot study, 82% of consumers expect an immediate response when asking a sales question. For the majority, this means less than 10 minutes.

This need for immediacy forces brands to rethink the way they interact with their customers. They must be able to listen and respond in real time, on the right channel and in the right tone.

👉 What is conversational marketing and how can you integrate it into your global marketing strategy? Discover how AI and data transform these conversations into performance drivers. 🔍

Unlike traditional top-down marketing, this approach is based on two-way communication between the brand and the customer. The aim is to understand customer needs, personalise responses and strengthen relationships in a customer-centric logic.

Conversational marketing was born with the rise of live chat around 2000–2010. The first chat windows integrated into e-commerce websites allowed direct interaction with an advisor.

Then came chatbots, capable of automatically answering simple questions, followed by voice assistants such as Siri, Alexa or Google Assistant.

Over time, these tools have become more powerful thanks to artificial intelligence. Conversational AIs can now understand context, adapt their tone and provide relevant real-time answers.

Meanwhile, the development of instant messaging platforms (WhatsApp, Messenger) and social media has significantly changed how brands communicate.

Today, even online search is becoming conversational. Tools like ChatGPT, Perplexity, and Google’s AI Mode extend this natural language exchange between humans and machines.

Consumers are now hyper-connected and expect instant, personalised exchanges. Long forms, delayed responses, and rigid purchase journeys cause friction and harm the experience.

This evolution is pushing brands to reinvent customer relationships. It’s also a way to stand out from competitors.

Conversational channels provide an answer to this need: 24/7 availability, direct interactions and relationship continuity. According to a Sinch study, 38% of consumers prefer to interact with customer service via messaging rather than by email or phone.

Conversational marketing is based on a variety of digital channels that allow different kinds of dialogue depending on the timing and the need.

The challenge is to choose the most appropriate channel depending on the goal and context.

Chatbots were among the first conversational marketing tools. Initially limited to pre-set scenarios (e.g. order tracking, returns, FAQs), they’ve evolved thanks to artificial intelligence.

Conversational AIs understand context, adapt tone and generate more natural responses. They rely on language models (LLMs) connected to company-specific knowledge bases — a technique known as Retrieval-Augmented Generation (RAG).

Thanks to generative AI, these tools go far beyond scripted bots. They can provide 24/7 support, qualify leads, recommend products or follow up with customers.

Virtual assistants and AI-based conversational search (ChatGPT, Perplexity, Google AI Mode) are changing how customers interact with brands. Exchanges become more contextual and personalised.

A new era is emerging, where each search becomes a true conversation. To stay visible, brands must appear in AI-generated summaries.

Levers include: establishing authority in your field, increasing visibility, and precisely meeting customer needs through your content.

This requires meticulous conversational design, where every element is crafted as a response to a potential customer query.

Every interaction generates a wealth of signals: intent, emotion, needs, preferences or friction. This information is invaluable: it drives personalisation.

First-party data allows brands to tailor messages, tone and timing to create truly personalised exchanges. It helps contextualise responses and adapt scenarios to customer profiles or situations.

Conversational AIs use this data through continuous learning loops to refine responses over time. This learning process is governed by transparency and GDPR-compliant confidentiality rules.

Use cases include:

💡 Example: A customer chats with an e-commerce bot. The system detects an abandoned cart and offers a personalised discount.

A Customer Data Platform collects and unifies customer data scattered across the company's systems (website, CRM, stores, support). By creating an up-to-date 360° customer view, it provides consistency and efficiency for delivering personalised experiences.

In its composable version, the CDP works directly from the company’s data warehouse without duplicating data. At DinMo, we’ve chosen this privacy-by-design approach to ensure compliance and coherence.

Our no-code interface enables business teams (marketing, sales, customer service) to activate data in their tools without technical dependencies.

💡 For example, it’s possible to tailor a conversation with technical support by using customer data.

We offer integrations with tools such as Zendesk or Intercom, allowing attributes (Lifetime Value, order history, churn risk, etc.) to enrich agent profiles.

Each agent has a complete, real-time context before interacting with a customer — making conversations more fluid and relevant. An approach already successfully adopted by our client Interflora. 👇

A strong conversational marketing strategy is built on clear goals, deep customer understanding, and smart data use.

1️⃣ Define objectives and journeys

First, identify customer journey moments where conversation adds value — discovery, conversion, after-sales, or loyalty. A quick exchange can reduce hesitation, speed up purchases, or boost post-sale satisfaction.

This analysis should include all relevant teams: marketing, support, product, sales. Their field knowledge helps identify friction points and opportunities for dialogue.

2️⃣ Choose the right channels and tools

The right channel depends on your audience, context, and expected interaction volume. Web chat may be ideal for support or conversion; messaging may suit post-purchase tracking better.

Brands can also combine channels within a single ecosystem. The goal is to offer a smooth and personalised omnichannel experience.

3️⃣ Centralise and activate customer data

A conversational strategy requires reliable, accessible data. A Customer Data Platform unifies all sources into a single customer profile.

It feeds business tools and powers personalised conversations across every channel.

4️⃣ Test, measure and optimise

Success depends on continuous improvement. Teams rely on key metrics: conversion rates, response time, customer satisfaction, or retention.

This feedback helps refine scenarios, tone and assess each channel’s real performance. Regular conversation analysis also reveals new customer needs and informs global strategy updates.

AI is transforming customer interactions, but it also introduces new challenges.

Success lies in finding the right balance between automation and human connection to deliver fluid, personalised and authentic experiences.

Conversational marketing is a powerful tool for enhancing customer relationships. It meets growing expectations for personalisation and immediacy.

Data contextualises each exchange to make it more helpful and effective. For this, brands need reliable, accessible information.

Our composable CDP activates customer data in real time, without duplication or technical bottlenecks. Teams can create conversational experiences that are personalised, compliant and customer-centric.

👉 Discover how DinMo helps you implement an effective and coherent conversational marketing strategy within a data-driven approach.