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Data-driven business: the voice of the digital customer

Data-driven business: the voice of the digital customer

Digital transformation strategy should be data-driven and consider consumer opinions and new habits. In an increasingly online world the business in-person “counter chat” is often hidden in Big Data.

Listening to the customer is a habit that every small entrepreneur knows well. Understanding consumers’ wants and needs is where commercial strategy is born – and it helps create loyal customers. This is not just for small businesses, of course. Large companies can learn a lot when they listen to their own audience. 

But during the pandemic, when we all dramatically increase our online consumption because we are confined at home, how do we continue that dialog? How can I hear the digitally transformed customer voice without the opportunity to talk behind the counter?

The answer lies in the title of this article: through data

Digital products, such as applications and portals, can collect a large amount of data that reflects customers’ experience with brands. This data is a kind of “dialog,” as it reveals a lot about customers’ preferences, needs, and expectations, enabling brands to provide better quality services.

Companies have explored this possibility more and more over the last few years, as digital business transformation gained traction, but the pandemic accelerated it at a rate never seen before. Now the concept of Data Agility becomes pertinent: It gives businesses the agility to use data to make decisions and strategies. 

Companies all over the world today store huge amounts of data from various sources. Why not use technology to extract value from those sources to make more accurate decisions? Can market research still be expected, which can take months to get ready?

Additionally, the data is now where the customer voice lies. Through this analysis we can listen to the customers, understand their behavior and will. This is even more critical when we remember that habits have changed, perhaps forever, and people are less patient. They want quick answers, and instant brand relationships. They also want autonomy to solve problems on their own.

For small businesses that have now joined the online world, data can help replace the feeling of lost touch. For large corporations, it can be a way to solve a historical problem: a more assertive, personalized, and close-to-the-consumer service, on any available channel – such omnicality, where consumers decide what, where, when, and how to buy. 

The more a company’s data management matures, the more it will be able to understand its customers. And if over time you expand the way you acquire and interact with customers, you will come to understand their feelings and intentions. But it is a long journey, which many businesses are just beginning. 

Let’s learn the ropes.

Collecting and storing data is just the beginning. Solutions that enable deep business and customer knowledge are important, but they are not enough. A fundamental and often forgotten issue is the establishment of a data-driven corporate culture. Without the awareness that data is an asset capable of furthering corporate strategies on a day-to-day basis, the changes will not be great enough to affect results.

By establishing this culture, companies will be able to use the data as a strategy and path for continuous innovation. Every organization needs to innovate, it is critical in the post-pandemic world, and it can no longer happen in events or waves. Innovation becomes an evolutionary process.

Is there one right way to use this data?

The answer is no. Each situation demands a type of approach, and each company will find a way to do it according to its culture and strategy. But one thing is common: the need to be aware of all traces users leave behind. 

By doing that, and with the use of artificial intelligence techniques such as Machine Learning, the company can automate complex tasks that make the customer’s life easier. Recommending products is one example. 

There are, of course, good practices that can help avoid problems. One of them is always to measure the effectiveness of actions taken automatically. Recommendations may end up generating too many notifications, for example, possibly annoying customers. It is important to measure the effectiveness of resources through gradual rollouts, made available over time to smaller groups of users. And, only after good results have been proven, expand it.

These experiments and tests are important and should replace the logic of quick and easy gains. Successful strategies can wear out quickly and end up not being sustainable over time. The key is experimenting to validate hypotheses, which is only possible through analyzing the data. 

Another important tip: Engines, such as chatbots, that automatically interact with users can put the user in a bubble. That’s because they only consider preferences that users have expressed to make suggestions, so they may not list all available options, including some that users may prefer. This is where the introduction of random factors comes in. Show options and recommendations that the engine does not yet know if the user will like, such as new products and services or rarely seen news. 

Another key point: Data security and privacy are imperative. People are concerned about the use of data that they allow companies (or should allow) to access. That is why we must all be vigilant about safety. During the pandemic, episodes of clients, tools, and hacked companies were prevalent.

 

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By Bill Coutinho – Transformation Director at Cinq, & Everton Gago – Chief Data Officer at Cinq

 

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