There are so many industries and so many businesses across different markets around the world. Though there may be a vast difference in the product and services of the industries and business, when discussing the bottom line of each company you will find a common ground. Each business wants to keep its consumers or customers pleased. Let’s face it the customers are ones who bring in the profits which ensures that the businesses functions. This is where big data plays an integral role. We have established in our earlier blog post that big data provides essential information to companies about their customers. The question remains what can companies and even more so, what can machines do with all this information, and how can we use it to help customer service. One of the key answers is Predictive Analytics.
So, what is predictive analytics?
By now we have established that big data can provide useful information for businesses about their customer. As the name suggests predictive analytics is used for future prediction based on many factors. Now you may be wondering how it is possible to predict the future, but you must take the statement with a little grain of salt. Predictive analytics uses the help of statistical modeling, machine learning and data mining to review historical trends for future predictions.
How is predictive analytics useful?
To put it in perspective consider a few real world situations where predictive analytics may have made your life easier. For example, the last time you planned a vacation, you may have checked the weather to make sure you packed appropriately. Weather forecasts are a form of predictive analytics because they are based on historical trends for future predictions. Future earnings predictions by companies are a form of predictive analytics; they also base their predictions on historical trends. While there is no guarantee that it will rain during your trip or that a company will achieve their future earnings report number, the likelihood of these outcomes are realistic given the historical trend. Predictive analytics helps provides us perspective on the data we have.
How can predictive analytics help businesses with customer knowledge?
Now that we have established what predictive analytics is and its usefulness, let’s understand how predictive analytics can help with your customers. Once you have gathered data on your customers through various platforms, predictive analytics is used to assess the trends.These trends could provide details on customer preferences, sales effectiveness, marketing feat etc. For example this information can be useful in identifying first time customers so businesses can channelize company resources accordingly.
How does it help Customer Service?
By implementing predictive analytics in customer service, the trends established can help companies predict customer behavior and problems across multiple media channels. Examples of predictions could be:
Predict the customers future purchase behavior to predict the likelihood of buying up-sell products
Predict the likelihood of the customer becoming a brand advocate
Predict future problems that will arise with the companies offering based on current set of questions from customers
Predict the channel preference for communication
Predict the best time to reach the customer
Predict the probability of customer turn
In effect the customer service interaction will be short and sweet, and leave the customer feeling that the company understands their needs.