Customer Services Have to Invest in Predictive Analytics

2542450115_6576d19185_b
Photo Flickr by Steve Jurvetson http://ow.ly/pRxH302nJqV

Oliver Renard, the CIO of La Poste Mobile, announced at Big Data Paris 2015: “To predict clients’ behavior and needs is the next horizon of customer services”.

In the field of digital data, technologies to detect, predict and find similarities have appeared. We talk about data science. In the business world, these techniques are based on algorithms and complex calculations. Yet, the point is simple: Analyze data generated and establish a behavior’s logic to anticipate potential outcomes.

In industry, we may think about products from cars to bikes and mobile phones. These tools generate huge quantities of data every day. For human beings, we talk about signals – either direct or indirect (calls, messages, product use) – captured through various connected objects.

Clearly, customer services have a lot to (l)earn by investing in data and predictive models.

Services, slowly, change. The realm of the possible expands. Traditional call center are a thing of the past. Now the possibilities are extended to brick and mortar shops. Customer services are no longer limited to the brand’s department. They have become dematerialized. The amount of data created by the client and the enterprise has exploded.

So why don’t we take advantage of that? Obviously, we need guidelines. Once the client and the brand agree, data exchange begins. It has been successfully tested in various sectors such as telecommunications.

The time has come to adopt predictive technologies. Thanks to models, the firm can now determine which precise action it may undertake for a particular client. In case of potential churn, the brand can then propose exclusive and special offers to head off a departure. The client’s reaction will be also analyzed. A learning cycle is in place  to continuously improve algorithms and predictions.

We should not look down upon it; it’s not about spying. Actually, it is to the client’s advantage. By predicting issues, the brand simplifies processes and can respond better to difficulties. Actions can be launched to avoid attrition – very necessary at a time when buying cycles are short. Indeed, it is a requirement to react as fast as possible to the market (product, challengers) and changing customer behaviors.

Because, yes, the customer is at the heart of the brand. The firm can now focus thanks to its new tools and the organized data from new machines.

A simpler life and more time to do what matters to people … and more efficient companies more respectful of their clients and their environment.

The beginning of a virtuous circle?

Comments are closed.