Data-driven marketing builds plans and strategies starting from the data of consumers (already acquired or potential) with whom it comes into contact (or that it aims to intercept). It is an approach – which is based on technological premises and has important cultural implications – that is increasingly adopted by organizations of all sectors and sizes to:
- implement initiatives with a high level of personalization,
- achieve a higher level of engagement with their target audiences,
- maximize ROI.
With data-driven marketing, data represents the starting point for creating effective and targeted campaigns, promotions and initiatives of various kinds. Let’s go further.
Data Analysis: From mysterious practice to accessible process
Not so long ago, data-driven marketing was shrouded in an aura of mystery, a kind of unknown territory into which only technicians with specialized skills could venture. And for this reason, it was rather rare.
Then the focus of software houses – at least those that supported companies in creating current and relevant customer experiences – shifted to the automation and simplification of the activities of individual company functions (marketing and sales primarily).
In this context of renewed attention to professionals and consumers, specific solutions have been developed to free data analysis from unnecessary reverential fears and finally recognize it as a process that is not only essential but also accessible.
This evolution has proceeded in two directions:
- New knowledge and tools have made it possible to leverage the multitude of digital channels to meet consumer expectations;
- By participating more actively in the conversation with the brand, users-consumers have provided useful information to create the wealth of knowledge available to the brand itself.
Data-driven marketing, equipped to juggle different media and channels, now operates using the vast amount of information that organizations have access to, information that comes from a variety of sources, both proprietary and third-party.
Behavioral, contextual, psychographic, demographic, and geographic data, as well as the results of less immediate measurements such as customer satisfaction with a brand, are used to give operational meaning to each interaction with the brand and to build more profiled and meaningful messages from this interpretation.
Data collection tools: the central role of CRM
Marketers collect incoming data from virtual and physical touchpoints scattered along the path to purchase and shape it through tools such as artificial intelligence, machine learning, and CRM software.
In particular, the CRM tool sits firmly at the center of the enterprise technology infrastructure, becoming increasingly crucial to the conduct of data-driven marketing. In fact, CRM data can be exploited to create personalized communications and more engaging customer experiences. This is because CRM is not a static entity, but evolves by constantly collecting customer data that is then analyzed to deepen relationships and improve the results of marketing activities.
Any customer-facing marketer, salesperson, or customer care professional is greatly benefited by the presence of CRM in their “toolbox”: they can develop their communication from a deeper and more articulate knowledge of their interlocutor. And by taking the first stages of knowledge for granted, he is able to proceed more quickly to resolve specific needs.
The result of the collection and analysis activities is a dynamic picture in which the performance of the resources employed and the channels manned are monitored in real time to determine which generated the most interactions and which offered the highest ROI. Observing these metrics is useful in providing insights, thanks to which to redefine what we have called “data-driven creativity” in a timely manner.
The customer situation to which creatives have access thanks to platforms such as CRM, is already profiled at a level that was once unthinkable, making it easier and more immediate to open and maintain a vital channel with the consumer, offering him the information he actually needs. It’s the seemingly unstoppable trend towards personalization that we’ve often talked about on this blog and that is expressed in its most evolved form in personalized videos.
Market research, lead campaigns, data-telling: the starting point is data
There are three applications of data-marketing that today seem to attract the attention of brands – because they can have a real impact in terms of productivity and turnover margins – and they certainly deserve a separate discussion. Here, we will limit ourselves to a brief description, reserving in-depth study for other articles: data-driven market research, data-driven lead generation campaigns, and data-telling.
- Data-driven market research provides business owners, managers, and marketers with a snapshot of their customers’ buying habits. Recognizing buying trends helps a company set its marketing strategy and helps design those communication and advertising initiatives that aim to achieve increased conversions and sales. This particular approach to market research involves the collection, selection, organization, and interpretation of consumer data: from personal information such as age, date of birth, marital status, and income levels to more complex qualitative information such as feedback and expectations, consumption behaviors, preferences, and navigation patterns of different touchpoints.
- Data-driven lead generation campaigns use information that comes from sales and marketing activities and from the various touch points between brand and consumer throughout the funnel, focusing less on quantity for its own sake and more on lead quality.
- Data-telling represents one of the most recent outcomes of the digital transformation applied to storytelling. Mass digitization has determined a radical rethinking of the various creative formats (for example, video content), intervening in practically every phase of the customer journey. Data storytelling (or data-telling) is an engagement and involvement technique where the “narrator” (in this case, the brand) has the possibility of having new resources for creating stories where consumers and clients can act. Data-telling represents a sort of enhancement of storytelling: the story takes on new dimensions that are more “digitized” through the use of information that comes from data (structured data, as in the case of a CRM management system, and unstructured data, such as the monitoring of online conversations). The keystone is, once again, the customer experience, the goal and starting point at the same time of a virtuous circle fed by data processing and management systems.
The advantages of data-driven marketing: objectives and benefits
Data-driven marketing is above all a methodology that uses technological innovation to generate new opportunities for improving the customer experience.
On the company side, data-driven marketing was created to offer concrete support in achieving three fundamental objectives:
- shorten and smooth the customer’s path to purchase,
- increase the level of customer satisfaction,
- obtain higher ROI.
With sophisticated data analytics, marketers can use more nuanced consumer profiles to personalize customer experiences and create and reinforce a bond of trust between company and consumer.
The benefits are many, and to describe them in detail would require a longer, more in-depth discussion. Below we have tried to summarize them.
1. Optimizing budget allocation
The analytical tools used in data-driven marketing allow marketers to more confidently identify how much of their budget should be allocated to individual actions (campaigns, market research, creative, promotion, etc.), based on an assessment of the expected (or desired) impact at different stages of the journey.
For example: marketers can see if and to what extent ads and campaigns attract potential customers. At that point, they are in a position to make the most appropriate decisions to optimize spending (e.g., acting on awareness or mobilizing for more conversions). In other words: with data-driven marketing, companies are more likely to allocate their budgets correctly by observing in real time which initiatives manage to “move” prospects and existing customers along the purchase funnel.
2. Creating more relevant copy and content
Even today, there still seem to be obstacles to aligning creative with target audience expectations:
- Blog content has increased by 800% in recent years, but sharing on social media has declined by nearly 90%. Thus, there would seem to be a disconnect between what brands are communicating and what users would like to read, hear, and see (source: Marketing Platform).
- 74% of consumers are annoyed by brand ads that they perceive as irrelevant and invasive (source: Adverity).
Providing the right content at the right time, intercepting the personal interests and declining in the right way the message with respect to different media and channels, is essential to connect with consumers and create value for each of them. From this point of view, data-driven marketing offers the most effective solutions for creating relevant copy and content: it presents detailed information about the creativity with which the target audience prefers to interact (type of content, distribution channels, mode of use) in an immediate way.
3. Improved decision making
Two out of three marketers believe that adopting a data-driven approach to marketing, rather than a gut instinct or generic talent, enables them to make more informed decisions (source: Adverity). Data analysis therefore allows you to make a choice from observing real-world use cases rather than relying on theoretical elements. This, however, is only part of the story: the human element – experience, insight, contextual knowledge, empathy – continues to be fundamental even in the case of a data-driven approach. The consumer’s purchase decision is in fact very often influenced, if not actually guided, by emotional considerations.
Marketers must evaluate data while taking into account both the rational and emotional aspects that determine consumer choice, to ensure that they are properly balanced within campaigns. In this sense, data analysis is a sort of objective counterpart to a decision-making process that must also take into account consumer psychology (which by definition is never completely knowable), so as to be able to develop content that resonates with the audience.
How data-marketing is changing: the challenges for the immediate future
Between March and August 2020, one in five consumers switched brands and seven in 10 consumers experimented with new digital shopping channels (source: McKinsey).
Digitization in the retail sectors has accelerated dramatically: a 10-year leap forward in a matter of months. As a result, the flow of data has grown exponentially and brands have been faced not so much with the need to equip themselves with data-driven marketing tools and methodologies – an approach whose usefulness is now fully recognized – but with the need to update outdated data modeling that no longer seems able to capture change with the granularity and speed required.
Data-driven marketing uses models that are trained to recognize and draw inferences from consumer behaviors. In the post-pandemic “new normal”, these same behaviors have become more difficult to read and categorize; they have become even more elusive, susceptible to deviations from patterns that had taken a recurring form. Faced with a situation where historical data and patterns fail to provide a basis for accurate predictive analysis, many marketers have chosen the path already taken: they have gone back to mass communications and promotions.
Data-driven marketing offers a completely different perspective: by refining tools that already exist – algorithms that are more powerful and flexible because they are trained on selected data sets – companies can design more precise and accurate strategies so as to foster meaningful customer acquisition even in the face of unpredictable events. To keep up with changing needs and expectations and to anticipate changes in customer behavior, brands must commit to updating the way they manage data, from capturing new types of information (often unstructured and complex) to retraining algorithms.
Companies can no longer do without data-driven marketing because it is to date the only approach that can evolve contextually with changes in habits and consumption paths (sometimes preceding and producing those same changes).
The more marketers understand the customer journey, the greater their chances of developing the right messages, and meeting consumers at the right time and where they prefer.
