Increasing Share Of Customer Means

As the global economic system struggles with the consequences of the health emergency linked to the COVID-19 pandemic, most companies are developing plans for the next stage of recovery and new growth.

Even those organizations that have lagged behind in the adoption of digital technologies are realizing that without a data strategy, it will be difficult to restart stronger than before. During this phase, a data-centric vision is more indispensable than ever. We are heading toward a restructuring of many aspects of our social and economic life that will make new demands on our businesses from organizational and logistical standpoints.

Consequently, understanding the contents of your database and pooling that information with a cross-sectoral perspective could soon become a vital lifeline for the market, as well as for individual players. Leveraging data so that businesses can read and interpret current trends and intentions, as well as enable companies to make predictions and generate insights, will become the key to competitive advantage.

However, for data monetization to be effective and valuable, company data must have four key characteristics:

The data must be shared within the same company even before it is made available to the outside world and not be locked in silos
It is essential that the data is connected, in other words, that individual pieces of data can "communicate" with one another and are easily accessible
More than anything, the data has to be autonomous, capable of automatically generating value thanks to structural management and analysis systems and robotic process automation techniques
Consequently the data will also be sustainable because it can generate monetization and key synergies not only from a cross-departmental perspective, but also from a cross-company standpoint. Monetizing a company's data by making it available to the outside world does not necessarily mean selling it—it is often more profitable to share it

Data analysis has become an essential activity for companies in managing competition and growing the business. However, the data alone is not enough—a careful data strategy is needed to ensure that all resources are used, shared and analyzed in a simple and efficient manner. Often, in fact, the presence of distinct silos in which the data is stored and processed makes it difficult to communicate and share information within a company, with a consequent loss of focus and a lower return.

When it comes to data monetization—the ability to convert and monetize the mine of information available to a company—two main possibilities usually emerge: either the data is sold externally, leading to a direct economic profit, or it is used within the same company and integrated into systems that make it possible to generate an indirect profit, perhaps as a result of a new business model.

A third possibility, which is the most profitable but the least common, is the sharing of data. This practice can be a viable option for companies that have different core businesses and that, thanks to this approach, are able to open themselves up to new market opportunities generated from a comparison with the outside world.

This is precisely the new business model on which Reply has been working for some time. The company has started to bring together several companies from different markets and sectors with the aim of creating value from the data collected and integrated into systems.

How is value created? First, by sharing and maintaining a critical but open approach. All the available data is placed in a joint data lake. More importantly, the data is integrated so that the individual pieces of data can interact with one another. This increases their value exponentially.

Often, when it comes to data obtained from businesses that are diametrically opposed in terms of the market, the target audience and the business goals, it is difficult to imagine that common ground might exist. But we have found that it is precisely by starting with the data that common strategies and mutual benefits can be identified and leveraged.

One of the first opportunities is the ability to create better understanding of the company's customer base, by broadening the analysis to include the context in which the consumer moves. By analyzing mobility and the interactions that a customer has with an individual brand, purchasing habits can be identified. This can pave the way for the creation of new services or of new customizations of products that are already available.

Understanding a customer, knowing how and how much they move, what else they buy, and what specific context they live in allows a company to customize its offer to the maximum, with a significant advantage for both the consumer and the company. In short, this is a perfect example of a business-to-business-to-consumer (B2B2C) data strategy. Mixing and correlating information obtained from the production chain with consumer mobility data also make it possible to define a more advanced retail model, as this approach also enables the remodeling of the distribution and allocation activities. The result is also a more sustainable socioeconomic model.

Pooling expertise and information enables a company to exponentially increase the value of the individual pieces of data. A combination of information from different environments and businesses can lead to the creation of a range of information that could not otherwise be derived from a single specific data set.

Like the different pieces of a puzzle, the data, once reassembled, can give us the full image of our customers. Too often, companies do not take into consideration the fact that their customers have desires, tastes, habits and characteristics that the single, bilateral interaction with a brand fails to understand and highlight. A telco operator will only be aware of certain aspects associated with the customer. The same is true for a company operating in the retail sector. The first will have a picture of the customer's network interactions, of how much traffic they use indoors or outdoors, of whether they prefer voice or data traffic and of what their online habits are. The second, on the other hand, will understand the customer's personal tastes, perhaps their food-buying habits or those related to clothing.

Collecting transversal information and joining this information to one's customer base makes it possible to understand how to modify or approach your business in the best possible way. Shared data management also makes it possible to eliminate redundancies in the information relating to the same customer. When that information is not automatically linked back to the same subject, it creates "clones," which weakens the data.

As businesses emerge from the pandemic, a strong data strategy will be crucial for charting a course amid the uncertainty. Organizations that are able to create a cross-sector view that goes beyond their own data set will be able to develop new understandings of their customers and the way forward.

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