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Data Driven Strategy: concepts, models and applications


written by Paola Alunni (Editor Manager at ByTek)

Data collection and analysis are becoming increasingly important to define a marketing strategy.

The Data Driven Strategy can be defined as a series of strategic choices made after an accurate data analysis. Information should be carried out not only with the goal of monitoring and investigating, but also from a predictive point of view. The data provide us with useful information, indeed, not only for observation and screening, but also for elaborate possible scenarios, that can guide management strategies.

However, a company that decides to implement a data driven strategy must consider a series of transformations that will involve, first, technological function. Its components have to learn how to manage and develop increasingly advanced analyzes and models.

But how to exploit all the data potential, which models to adopt, and which are data driven applications?

What a data driven strategy consists of

A data driven business strategy allows you to make well-considered choices, both internally, by equipping the company with a structured organization and alinear workflow, and externally, presenting solid and successful strategies to interact with your customer.

Statistics and reports help you to evaluate customer satisfaction, to build and to evaluate benchmarks and measure performances. Descriptive analytics, indeed, help you to ultimately establish the fundamental metrics of a business. However, a boundless amount of information, if not properly analyzed, risks remaining simply a data archive. In order to implement a data driven strategy, it is therefore necessary to unlock the true potential of this information; data can be able to guide business strategies if and only if you know how to exploit and govern them.

Why to exploit Big Data potential

Big Data analysis, combined with corporate data sets, allows you to segment existing or potential customers and profile them. In this way it is possible to detect market trends, approach new targets of potential customers and prevent anomalous factors affecting sales. According to the research carried out by the Big Data Analytics & Business Intelligence Observatory of the School Management of the Politecnico di Milano, Big Data Analytics in Italy represents a very dynamic and continuously growing market of 1.7 billion euros in 2019. A market that keeps growing, indeed. The more mature companies have already internalized the necessary skills, with a continuous experimentation path to govern projects and decision-making processes from a data driven perspective.

Which is the right Data Driven Strategy

A data driven company puts customer satisfaction at the center of its strategies; a successful business is undoubtedly one that focuses on the customer, capable of offering the right product at the right time, reducing production and distribution costs.

If a top manager wants to guide strategic choices in the right direction, with a data-driven approach, he must refer to the 3 main points of a data driven strategy:

  • Clear objectives to guide decisions;
  • tools to collect and analyze data;
  • professional skills to know how to govern them.

So, once you have established these three pillars, you need to optimize your workflow.

Clear objectives

The first and fundamental step consists in identifying the general objectives: knowing what results are to be obtained in the light of market data, trends and customer needs. Once these have been established, it is possible to proceed to the collection and clustering of the data to be analyzed.


To analyze and integrate a huge amount of data, human capabilities must be supported by Artificial Intelligence algorithms. Companies able to analyze them with machine learning techniques are those that demonstrate the greatest growth margins.

Above all, first-party data, i.e. those collected directly by the company in Customer Relationship Management, are currently the only useful and usable ones. A lot of information that flows into a database that can then only be used through machine learning activities.

3rdPlace has developed DataLysm, the Augmented Analytics customer data platform capable of analyzing and integrating data using Artificial Intelligence. Information, collected in respect of privacy rules, helps you identifying specific and effective marketing actions on customers.

Skills and data culture

There is no data governance without training and specialization. A company that wants to adopt a data driven strategy must make a profound cultural change: new technologies must become part of business processes, at every stage and at every decision-making moment.

To do this, it is important that the decision-making processes are shared and that all company components are ready to accept future challenges. Digital skills must grow through training courses on big data and data analysis, digital integration of business processes, cyber security and much more.

Data driven models

Data driven business models (or DDBM, Data Driven Business Model) are based on the creation, collection, analysis and aggregation of first-party data. Millions of numbers and information that, if exploited to the fullest, allow you to make informed decisions supported by a scientific basis. These are currently the most used models.

Free Data Collector

These are data driven models for data acquisition based on free platforms that allow you clustering within a single dashboard. Open Data Kit is one of these and, through the community, allows you to access tools of different levels of complexity for data aggregation.

Analytics Services

Model based on analytics services platforms that helps collect data on customer choices and behaviors and build targeted and personalized initiatives. For instance, Factual, which uses the observation graph to analyze customer behavior and to address potential customers.

Data Generator

Use of open-source software that create data science services. Platforms like Knime Analytics Platform that helps you to understand data, integrate it and consequently help plan workflow. Knime in particular combines text formats (PDF, XLS, XML, CSV and others), unstructured data such as images, documents, networks, etc) and time series data.

Data aggregator as a service

Data driven model to aggregate information from different sources: for example, it allows you to cluster clothing products based on size or material, machines based on the year of registration, homes based on square meters and so on.

Multi source data mashup

Model that creates connections between different and numerous data sources. Klipfolio is the platform that offers a connection between many sources such as websites, spreadsheets and databases, starting from a single view with an intuitive interface.

3rdPlace solutions

How to provide your company with a data-based organization? A clear example of data driven customer intelligence solutions is represented by the Italo Treno case, for which 3rdPlace has rapidly studied valid solutions.

Italo is a company active in the field of rail transport, where the need was to study personalized marketing actions.

In this specific case, data was collected regarding the actions of users on the site, integrated with those from email marketing and offline data from electronic ticket offices and travel agencies. Then we moved on to:

  • import of CRM segmentation on advertising platforms.
  • identification of customers with the highest probability of re-purchase.
  • sending personalized e-mails for cart abandonment.

The strategy has brought concrete results closely:

  • the targeted actions on cart abandonment have generated repetition of purchases.
  • there was a + 3% conversion rate from prospecting activities.
  • + 20% conversion rate from remarketing with a -15% investment.

New technologies, skills but, above all, a profound cultural change: only in this way data-driven strategy can lead to concrete competitive advantages.