Today's users expect a lot from technology, like connecting us or giving ubiquitous, up-to-date information at all times. We expect technology to adapt to our specific user profile, to inform us about the future for unknown events, and to support us in decision-making.

In any situation where we have information about what we want to predict, we can use large amounts of aggregated data to build models to predict future events. Of course, this works better in some areas than in others. A longstanding application of these techniques has been, for instance, in credit risk management: the customer's payment history, loan applications, debt-to-income ratio, and other factors can be used to predict the likelihood of customer default on new loans. This information will help lenders make better decisions to reduce the likelihood of default and optimize their bottom line. Marketers can use data to create targeted, specific advertisements that target the right customer bases to optimize the likelihood of purchase and increase sales.

Nowadays, CRM systems must solve a number of tasks: users need to be able to access information, manage accounts, track leads, generate new leads and opportunities, and close deals. With predictive analytics, almost every CRM system can be upgraded and provide you that way with crucial information that guarantees you being one step ahead of the competition. For example, you can determine which sales opportunities are most likely to close based on factors such as revenue, proximity to location, past customer deals, and many other factors, which can lead CRM system users to focus on leads that are most likely to generate revenue for the business. All of this is a result of the power of relevant, connected, and accurate predictions.

Another point where predictive analytics can help is when there is a delay in sales cycles, and you want to find out the reasons why that is so. Predictive analytics can help determine which characteristics of a potential customer are responsible for this or whether it is the company's relationship activities. With this information, companies can bridge the gap and remedy these inefficiencies from within, helping a company both externally and internally.

Many factors contribute to this, such as missing lead follow-ups, not establishing a relationship with the right customer base, missing relevant product suggestions, or gaps in your customer support channels.

With the help of predictive analytics, it can be determined what is causing customers to make the decision to leave a brand, and ultimately cast their economic voice elsewhere. Predictive analytics makes it possible to build a model to understand what is causing this problem so that companies can improve customer loyalty and provide better service to their customers. It is one of many emerging technologies with applications in CRM that is used in the medical, automotive and travel industries, enabling consumers as well as companies and their employees to make more informed decisions. In addition, it is a reliable tool to continuously improve business relationships or to keep them at a high level.

Although predictive analytics is primarily used to analyze consumer behavior, it will increasingly be used to analyze risks to assert oneself in the market and be competitive in the future.

By Daniela La Marca