It is a sentence pronounced more and more often.
Those who work in the world of marketing and communication feel it, but also those who live in companies and in public administration. The right conception, understanding and analysis of the data can determine the success of a broader and more important strategy. There are raw data, refined data and third-party data. Explicit or explicit data, data that immediately catch the eye and data that can only be identified thanks to patient skimming work. Reality is increasingly inextricably linked to the data lurking in our smallest daily actions, and to the precious information that they reveal to us.
In the world of web marketing, the data is now an indispensable tool: it is upstream and downstream of the adopted strategy, it gives it impetus on one side and on the other it aims at acquiring further information that progressively improves its effectiveness. The data is precious because it naturally intertwines with people’s lives, accompanying them from morning to evening. The figure has fallen so deeply into the lives of human beings that it is able to “tell” them. And it is for this reason that the attention around the data strategy is intensifying.
Those who have the right dataset, and know how to use it, will be able to achieve their results with a huge advantage compared to those who cannot collect, organize and use the data, or do not use them. Studying the life cycle of a data allows to retrace the customer experience and improve it, making it more and more satisfying for the individual who uses a product or a service. It is not simply a matter of measuring the performance of the campaigns by reading the data, but of constructing it by inserting the data, in order to create a circular process that positively impacts one’s business.
This approach to the data is of recent conception.
Until a few years ago, in companies, everything that had to do with user data was seen as an “IT problem”, a problem limited to information technology. Data referring to users was a critical mass that was difficult to read and access, and they were mostly archived, without adopting a strategy that could systematically enhance them.
Over time the data has multiplied by quantity and quality, and with them the tools able to read and decode them. Today we talk about data strategy: a strategy that includes the different ways to acquire, store, manage, share and use data, as well as enable and facilitate decision-making processes, connected to them, at every level.
According to SAS, a good data strategy consists of 5 steps.
The first step is data identification to explore its true meaning, regardless of structure, origin and location.
The second step concerns the archiving that foresees the realization of an infrastructure able to guarantee and facilitate access and data processing.
The integration phase makes it possible to move and combine the data stored in different environments, providing the various interlocutors with a unified view of the data.
Then follows the distribution, that is to say to make the data available respecting the rules and guidelines on the accesses.
Finally, given the data of a patrimony to be protected, good governance allows the definition of rules to ensure effective and controlled use. (source: SAS)
There are different types of data.
Those that we can acquire directly (web analytics, social data, CRM, etc.), and that refer to our platforms, services or products. But there are also data provided by third parties (interests, behavioral data, attitudinal data, etc.), which can be very useful to enrich our dataset. And precisely the variety and quality of the data in our possession allow us to set a specific strategy, focused precisely on the information that these data contain.
At the moment there are few data able to “express themselves” independently, without relying on other data. More frequent is the need to set up different types of data, coming from different sources and which must be segmented and correlated according to the objectives to be achieved. It is not so important to focus on the data itself, but on the relationships between the data themselves.
It is precisely these relationships between data that help us:
These are just a few examples of the different ways we can use the data we collect and put into a system. What really matters is to consider them as a mosaic that, if properly composed, can show us if our strategy is correct, how it can be improved, and how it can benefit our business, regardless of the type of economic activity.
Equipping yourself with a data strategy means simplifying decision-making processes in a more efficient and closer to the consumer perspective. It means doing some reasoning and drawing conclusions thanks to the support of the information that the data can provide us. And the benefits deriving from this approach are innumerable, transversal and continuous over time.
An example of a “grounding” of a data strategy is the customer journey: the path that defines the experience of each individual user through online and offline touch points. To be able to follow the consumer along the way, which typically includes the discovery, choice, purchase and use of a product or service, it is necessary to know their behaviors and motivations. The data collected, organized and interconnected, allows us to acquire this valuable information.
Another example is audience targeting: starting from user segments, defined on the basis of available data, it is possible to identify marketing strategies aimed at satisfying their real needs. Defining the different types of individuals who use our service, allows us to customize it for each of the identified segments, guaranteeing an experience more in line with people’s expectations and needs.
Beyond these examples of possible data applications, what really matters is the type of data approach. It is about creating a real data ecosystem, able to feed itself, maintain itself (and eventually correct itself) and generate relevant insights.
This way of understanding the data and using it requires an additional effort in the planning phase, but once it is fully operational it allows you to master and acquire a valuable knowledge, which will increase its value over time. A value that will be perceived above all by those who buy and use goods and services every day, generating data continuously: people.