Customer Analytics

What's disruptive about companies basing their strategies on evidence? What type of partners should we choose if our aim is customer loyalty? Do we know in which part of the journey our customer is?

These are just some of the questions that we asked ourselves at the seminar on Customer Analytics we organized on November 30 in collaboration with Josep Curto.

We should consider several things to address Customer Analytics tasks successfully. Here are some key questions:

  • Do you use intuition and experience to offer your products to your customers?
  • Do you know your customers? Are you listening to what they are saying?
  • Can you ask them appropriate questions?

Harvard Business Review (HBR) stated in the article The future of decision-making published in 2010 that algorithms, data, and cognitive computing will be key for companies to decide what to do since the 46% of  wrong actions taken during decision-making processes are due to the traditional use of intuition.

In this frame, Josep Curto said that companies that base their decisions on data enable us to have better relations with them, but also to use the scientific method, or, in other words, evidence.

This scientific method is nothing less than approach, observation, trial and error, and start again. Although it might seem simple, it requires a change of mentality that is not limited to the exploitation of data in the marketing department.

But what do we need to implement this strategy within the company? Have we already overcome the barrier represented by the implementation of a strategy based on data (Data-Driven Organization)? If so, and if departmental silos in terms of data management have disappeared, it is time to see what data do we have, how is it stored, and what do we use it for.

Customers do what they want with our brand, and we must make the most of everything they are telling us to satisfy them.



In this sense, there is a constant dialogue between a supplier of products or services and its clients, and we cannot ignore it anymore. In the current decade, an overwhelming amount of communication platforms has emerged, each one with its own public and its own languages and conventions. So, on the one hand, customers may make use of many channels to express their views and, on the other, companies have a huge, valuable source of data upon which to base their decisions.

However, to develop an effective business strategy starting from what we now call the “Voice of the Customer”, we should take into consideration four primary factors:

  • The resources required to process large volumes of data.
  • Time, in all its dimensions.
  • The variety of formats, structured and unstructured data.
  • The spontaneous aspect of certain types of information.

It is evident that the more information we have at our disposal, the more resources we will need to take advantage of it. Are we sure that all the data generated around our products have a real value for the company? And if so, how much time does its analysis require? In this digital era, decision-making cannot be based only on stored data: we must consider – and perhaps prioritize – the information generated in real time.

These concepts and the difficulties they entail for companies constitute the introduction of the talk given by Antonio Matarranz, Marketing and Sales Director at Sngular Data & Analytics.

A successful business must be capable of processing large amount of data and focus exclusively on those that provide useful information. These data, known as insights, are the key to agile analytics. Companies, products, and services are subject to constant judgment by various players, such as customers, users, or developers: being able to adjust business strategies according to this feedback is what makes the difference on the road to success.

The information that comes to us through social media does not have a predefined structure such as what we would get from a form or a survey. For this reason, we should bear in mind that in order to take advantage of all type of content, we must classify it depending on preset criteria. In this way, it is possible to compare data in different formats that talk about the same topic (a product, a customer service experience, a failure in the facilities, etc.).

All the difficulties mentioned when analyzing information are implicit in the concept of big data. The diverse nature of data, along with the spontaneity of certain contributions or the storage of unsolicited information (transactions, geolocation, biometric information, and so on) requires us to look for patterns that enable to locate insights in a reasonable time – and thus ignore what slows down the analysis process – and give them a proper format.

The advantage provided by text analytics and unstructured data classification lies in the possibility of getting valuable insights from different platforms (tweets, surveys, social conversations, customer contact) through a consistent analysis focused on our real needs.

For example, are we listening to what our customers are saying? We presented as an example the analysis of the Voice of Customer in the insurance industry and showed the different ways in which the customer reaches out a brand and the different purposes of this contact. Are customers talking negatively about the insurer? Are they thinking about canceling an insurance contract and switching provider? The analysis model enables to identify threats like these and take immediate action. With MeaningCloud, you can recognize both real and potential threats, so it is a crucial tool to develop predictive marketing strategies.

Below, you can enjoy the presentations by Josep Curto and Antonio Matarranz and the recording of the seminary (all in Spanish).

Here you have the recording of the seminary:

[Translated by Luca De Filippis]