Why Punxsutawney Phil should not be the norm for prediction in customer service

Punxsutawney, a small town in Pennsylvania, draws thousands of visitors every year on February 2nd when Phil the Groundhog predicts the weather. On February 2nd, 2018 it will be the 132nd time. According to a legend, if Punxsutawney Phil sees his shadow, there will be six more weeks of winter weather. If he does not see his shadow, there will be an early spring. This legend originates from the German-speaking areas where a badger used to forecast the weather.

But no matter which animal is used, those predictions are rarely accurate. In fact, Phil is only right 39% of the time. In order to predict the weather, or anything else for that matter, you need data. And the only data animals have is how much longer they want to sleep. That’s why actual weather forecasts utilize data from the past to predict the upcoming changes for the wind direction, likelihood of rain, etc.

This is very similar for brands who want to utilize predictive targeting for their customer engagement. Without historic data there is nothing on which a prediction can be based. Before thinking about adding a prediction engine to customer service, brands have to take a close look at the data they have available. This can be recordings from calls that have been transcribed, chat scripts, customer journey data, etc. The more the better. This allows the prediction to be more accurate over time.

If there is not much historical data available, brands can use current information from their customer engagement tools. For example, implementing a virtual assistant and a live chat in several digital channels allows the brand to gather new data and insights. These can then be leveraged to improve the prediction over time.

The best way to create a great customer engagement experience is to continually gather customer data. Every bit of information that can be received during conversations can be utilized for valuable and meaningful insights, which then result in a better optimization process that then allows the brand to predict customer behavior better and better.

This information loop can be augmented by humans who help analyze the data, adjust it to put into the right context and, in addition, help the prediction engine and the underlying machine learning algorithm to learn what to look for. The combination of both automation and humans drives higher accuracy and a better experience for the user.

This said, I’ll still be hoping that that Phil doesn’t see his shadow.

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