Data-driven marketing: definition, tools and key steps to success

According to Statista, the world will produce 181 zettabytes of digital data in 2025. That's three times more than in 2020, and 12 times more than in 2015. Just imagine: if each byte were a grain of rice, the volume produced would more than cover the surface of the planet!

Companies understand the importance of leveraging data to grow. However, to date, only 4 out of 10 of them have really adopted a data-driven strategy.

🔎 Find out everything you need to know about data marketing: its definition, its benefits, the types of data to be exploited and the essential tools. Get practical advice on integrating this strategy and maximising your performance. 🚀

For example, a fashion brand can analyse customers' purchasing habits to offer tailored recommendations or anticipate seasonal demands.

By combining this data with inputs from various sources (websites, social media, in-store interactions), companies create coherent and engaging experiences across channels.

Today, 8 out of 10 consumers are more likely to make a purchase when a brand delivers a personalised experience (Medallia). Yet, only 26% of customers feel they receive this level of personalisation.

McKinsey and BCG also reveal that companies with data-driven strategies see profits increase by up to 25%.

Data-driven marketing responds to the demand for tailored experiences. It offers deeper customer insights to craft effective, engaging strategies.

By relying on trustworthy data, businesses optimise budgets, adapt to market changes, and deliver memorable customer experiences.

In the field of data-driven marketing, three main categories of data play an essential and complementary role in the development of an effective marketing strategy.

Zero-party data are voluntarily shared by customers with a brand. These are their preferences, interests or expectations. Zero-party data provide unmatched accuracy as they directly reflect consumer wishes.

For example, a user might fill out a quiz on an e-commerce site to receive personalised recommendations. Subject to the same privacy and consent rules as first-party data, they are incredibly valuable for businesses.

Zero-party data enable targeted marketing actions and build trust by respecting customer preferences. Yet, Gartner notes that only 33% of companies are ready to fully leverage them.

These objectives will guide the organisation of your data and the choice of appropriate tools.

Make sure that the data is complete, accurate, up-to-date and compliant with regulations.

Measure performance: continuously monitor the performance of your campaigns using analysis tools. Choose the right marketing metrics and KPIs. This enables you to adjust your strategies in real time and optimise your return on investment (ROI).

Select the right tool for each stage of your project.

The DinMo composable CDP offers great flexibility for companies with a data warehouse. Thanks to its predictive attributes based on artificial intelligence, it enables precise segmentation and advanced personalisation.

Thanks to its no-code functions, your business teams can deploy their first use cases in thirty minutes.

👇 Discover the possibilities offered by the DinMo composable CDP for your data marketing projects.

Transparency with users: provide clear information about the collection and use of data, so as to establish a relationship of trust. 

Compliance: make sure you comply with regulations such as the RGPD or the CCPA. This involves obtaining the necessary consents, securing and accessing personal information.

Data quality: give priority to accurate, reliable and up-to-date information. This guarantees relevant campaigns and optimises your performance. 

Test and experiment: Data-driven marketing requires an iterative and flexible approach. Be prepared to test various hypotheses, experiment with different scenarios and evaluate the results.

Data marketing is not reserved for one industry in particular. It can be adapted to all sectors to meet the expectations of consumers and businesses.

E-commerce businesses use data marketing to personalise their campaigns and boost sales. 

You may already have had experience of this - Amazon is an emblematic example. Thanks to its predictive analysis algorithms, the platform offers product recommendations based on purchase history and browsing behaviour.

DinMo customer Interflora is a perfect example of the effectiveness of data marketing in e-commerce. Thanks to our composable CDP, the company was able to create precise audiences and orchestrate multi-channel paths to optimise its campaigns. 

This led to a 17% drop in CPC and a 32% rise in conversions. Optimised management of sales pressure, resulting in a better match with customer expectations.

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In B2B, data marketing plays a key role in customer acquisition and retention. HubSpot uses data to segment its prospects and offer personalised content at each stage of the buying process. 

This approach has led to an increase in qualified leads and a significant improvement in the conversion rate. 

In another example, Ankorstore used DinMo to boost its advertising performance on Google Ads. The B2B marketplace reduced its CPA by 20% and increased its conversion rate by 24% thanks to more personalised messages.

By leveraging our predictive metrics, Ankorstore is able to identify the specific needs of each segment to strengthen engagement and loyalty.

Data-driven marketing is now essential for staying competitive. It aims to improve the targeting of your campaigns, optimise your budgets and meet the growing expectations of consumers.

Artificial intelligence makes it possible to offer even more precise and relevant experiences. Zero-party and first-party data are becoming major assets to be exploited.

Composable CDPs, such as DinMo, are becoming key tools for segmenting and activating your data effectively. Switch to a data-driven strategy today and open up new opportunities for your business.

✨ Find out how DinMo can help you!

Data-driven marketing uses customer data to develop marketing strategies and deliver targeted campaigns. 

Rather than relying on assumptions, data-driven marketing uses information from real interactions and behavioural data to make informed decisions. This approach enables brands to target the right audience with the right message, improving effectiveness and return on investment (ROI). 

Using tools such as customer data platforms (CDPs) and analytics software, companies can create campaigns that really resonate with their audience and increase their business.

Companies can collect first-party data through owned channels like websites, apps, and CRM systems, while zero-party data comes directly from customers via surveys or preference centres. 

Data warehouses and composable CDPs are invaluable allies when it comes to centralising, segmenting and activating data. They enable rapid, secure and compliant integration with companies' marketing and sales tools.

This approach fosters personalisation and improves the customer experience at all points of contact.

Data-driven marketing enables brands to better understand their customers' behaviour and preferences. 

They can then create tailored experiences to achieve their objectives: winning over new segments, optimising their campaigns or improving loyalty. 

For example, analysing purchase histories helps to predict customer preferences, enabling personalised product recommendations. Similarly, segmentation tools help to create targeted campaigns that respond directly to the specific needs of the audience. 

Research shows that customers are more likely to engage with brands that offer relevant and timely interactions. 

By leveraging these insights, businesses not only improve customer satisfaction but also build lasting relationships and increase repeat purchases.

Data-driven marketing involves several specialised roles that help businesses make the most of their data. A data analyst focuses on interpreting data to identify trends and insights for campaign optimisation. 

The data scientist builds predictive models and algorithms to forecast customer behaviour. Marketing analysts use data to assess campaign performance and audience engagement. 

A CRM manager leverages customer data for personalised marketing strategies, while a marketing automation specialist manages tools to automate data-driven campaigns. 

Together, these professionals ensure data accuracy, better decision-making, and targeted customer experiences that drive business growth.