Why is data analysis so crucial for a company today?

If we make a brief research on Google News, we can easily discover that some of the most popular, prestigious and iconic brands of the world (regardless of the business area in which they operate) are expanding and/or enhancing, or even just renewing their data analysis sections. We are talking about brands such as L’Oréal, Nestlé or even Microsoft: actual giants of consumer goods’ production on a worldwide scale, who theoretically shouldn’t need such measures to keep on staying on the higher step, in terms of both sales and customer retention.

The reality is that data analysis has become through the years an apical element to help a company elaborate new marketing strategies, in order to stay constantly ahead of the curve, despite the increasing competition brought by the global market’s snares (this is why, in the latest years, many agencies started to offer a business guide to data analysis). And if even a big worldwide company needs to operate in that direction, relying on a good and accurate data analysis team becomes even more crucial for a medium-sized or a little one; even the local companies need it nowadays, because the competition is increasingly tougher on every scale, and it shouldn’t be underestimated, regardless of the company’s reputation, its core business or the geographic area covered by it.

The question is: why has data analysis become so important to determine the fate of a company, its success or otherwise its failure? The reasons are manifold, and most of them deal with the economic and financial structure of the business world in this day and age. Nonetheless, there are a few practical reasons that make this apparently inviolable centrality more comprehensible even to those who don’t have a master in International Business. Below, you’ll find the main ones.

  1. It helps a company determine whether a communication strategy is correct or not. Many factors can influence the success of a marketing strategy, but no one is able to realize what they are until a serious and deep analysis of the available insights is made. Only this operation allows a company to have a comprehensive picture of the situation, which is generally the best start to reconfigure its strategies (if necessary).
  2. It helps collect more details about the customer experience. This means, basically, having a clear picture of customers’ behavior, with detailed information about their age, gender, social status, habits. This way, a company has the chance to define its main target in terms of customers; or it could elaborate new strategies to drag different categories of potential clients.
  3. It allows to make veritable simulations, in order to measure the effects of any possible change. For example, if a company’s owner or CEO needs to know the effects of a modification on the company’s pricing policy, an operation of predictive analysis is able to provide him the most likely effects in terms of sales, with a low margin of error. This clearly helps the decision-making staff operate with increased safety in terms of results.
  4. It helps minimize the risks. Any data analysis methodology is based on mathematic models, so it is highly unlikely to witness a complete failure of their surveys. This ensures the companies to have a reliable summary of their businesses’ state of the art. Which is, in the end, all it takes to avoid any default risk.
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