Behavioural Analytics: the key to customised data-based marketing

The customer journey has long stopped being linear and has become a somewhat chaotic process. For years, we’ve known that the purchase decision gets made from multiple points and different routes based on individual preferences.

Consequently, knowing the user has become a requisite to establish mutually beneficial relationships. Is collecting operational data enough? Does only collecting sociodemographic and information on segments solely based on purely statistical criteria suffice?

A successful strategy will bet on knowing consumer behavior: decision-makers continually send signals that will, when well-analyzed, serve as an accompaniment to our global strategy.

Also, when comparing insights from various surveys, we appreciate the advantages stemming from behavioral analytics: the latest study from Gallup found that insights gained from behavioral analysis helped conversion 85% better than other insights.

The stages for knowing the user

Where do these insights come from? From the data, naturally. Organizations’ interest in data has evolved to the degree that we have seen all kinds of companies transform data into something else: improved efficiency, reduced costs, competitive advantage…all the way to, in some cases, turning the data into its main value proposition. Remember how much Facebook paid for WhatsApp? The interest of organizations in data has evolved to the extent that we have seen all kinds of companies turn information into something else: improved efficiency, reduced costs, competitive advantage … Until converting the data, in some cases, into the company’s primary value. Remember how much Facebook paid for WhatsApp?

Today’s technology and easy Internet access, coupled with the growing use of social media platforms, have made information inputs multiply. This improvement in information systems has allowed us to digitize and structure the different processes needed to optimize data analysis, but as the table shows, this has not only occurred within Operations departments.

Source: Customer Analytics, Mejorando la inteligencia del cliente a través de los datos, Jordi Conesa i Caralt (Coordinator) Núria Braulio Gil, Josep Curto Díaz

What is behavioral data? Behavioral Analytics stems from the need to know how, when, and why people interact with digital media, whether it be a mobile app, online game, website, e-commerce site, and so on. It is ultimately about understanding consumers’ online actions to allow for accurate predictions about their likely future behavior.

Understanding the why behind certain actions from the part of the consumer will let us optimize entire purchase cycle and generate new business points and points of interactions in every phase of the customer journey.

What types of data can we find?

To gather, use, and generate user behavior data and, create an online strategy in parallel using this information, we must consider that there are three types of data that we can transform into valuable information:

1. Registered data: Data stored in our CRM or marketing automatic tool.
2. Observed data: how our users behave on our website or how they interact with the different elements of the platforms in which we are present. Observing their behavior gives us clues about their interest and how they react to our messaging, published. We examine their behavior, and this gives us clues about their interests and their way of responding to our messages, distributed elements, or phases of each journey.
3. The voice of the consumer: how consumers feel about our services, and how they express those feelings. They can express them either reactively through surveys, focus groups, workshops, or proactively, through social listening, where users, without asking, express their opinions, present doubts, propose improvements or simply participate in conversations about our products, services or issues related to our brand.

Are we connecting the dots to capitalize on this knowledge?

Depending on the maturity level of our data implementation, we may have some data stored in unrelated, closed silos or, at best, we’re only taking advantage of some of them. Perhaps some connections are sure: a CRM with email, a CMS with Analytics, a marketing automation tool with email and maybe also with a CRM…

But, what happens if we want to connect all that related data in an agile way? The exploitation of this data usually requires an enormous amount of time and work. This is not operational if we want to respond to consumers quickly according to their behavior, and even less so when we have a billowing amount of data, which makes scalability more difficult.

To respond to these issues, in advertising, we can create audiences through a Data Management Platform (DMP) that helps integrate multiple data sources and segment it by combining different inputs. But, what happens when we need to personalize content and give automatic responses through all the digital signs the consumer has sent us?

An intrinsic view of the consumer

To answer that, we need to build a unified profile that not only aggregates demographic information, user preferences, and their needs. This unified profile must also integrate variables like the point they are in the customer journey, occupation, behavior across different channels, affinity for products and services, or their response to the various offers that go sent to them.

To consolidate the different data sources and maintain this intrinsic vision of the consumer, we need to pull two levers:

1. Technology

Implementing a Data Mart -a sufficiently practical marketing automation solution- or directly to a Customer Data Platform (CDP) will let us interconnect our CRM, marketing automation tool, email, analytics systems and CMS, to provide an automated and customized response based on the behavioral data of our users. In short, we gather all these digital indicators and use them to:

• Segment based on statistics.
• Design a communication strategy based on increased knowledge of the consumer.
• Facilitate the display of data through actionable control panels.
• Rely on predictive models.
• Provide relevant experiences

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2. Strategy

Implementing a CDP or a technology solution connected to consumer behavior data sources is not the elixir. It all this work is not complemented by a strategy defining what our goals are, who our audience is, how we want them to perceive us, etc., we will have a beautiful albeit useless dashboard.

We also need to develop a plan that denotes how we will personalize our messaging using data, in what formats, pieces of creative production, what our style is, how we will organize our data…In short, we need a digital strategy that depends on the behavioral data made available by our technological base

Once we have pulled both levers, we will have reached, or at least approached, the continuous aspiration of personalized and data-based marketing, an objective that is no longer unattainable, but something for more and more organizations to aim to achieve. In this environment defined by data and technology, this is what is at stake to make our relationship with customers lasting and mutually beneficial.