What are Predictive Analytics, and How Do They Work?

A company that wants to provide its customers with an exceptional experience has to be on top of its game, all the time. It comes as no surprise that well over half of consumers (57%, according to one study) will stop purchasing from a business if the competition offers a better overall experience.

Granted, superior CX has a ton of moving parts. Nevertheless, companies that leverage innovative CX technologies and techniques are in a much better position to give their customers what they really want. One key component in such a strategy is predictive analytics (also known as PA).

What are Predictive Analytics?

Put simply, predictive analytics (PA) "describes the use of statistics and modeling to determine future performance based on current and historical data." If that definition sounds a little like the same thing you might use to describe "artificial intelligence", that's because it is. PA is an applied version of AI - one which was designed to support customers.

In the world of customer experience, initiating a predictive analytics project involves 3 basic steps:

  • Selecting the data set that you want to track. For example, if you run a car dealership, then you may want to track which vehicle type is your top seller for a six month period.
  • Compiling the data. This is self-explanatory: you ensure that all relevant data is captured in an easily accessible location.
  • Analyzing the captured data. In this phase, you look for trends that will inform future performance. Going back to the car dealership example, if you determine that your customers love buying polka-dotted Volkswagen minibusses, then you may decide to bring more onto your lot. Or, you know, not.

The bottom line is, PA can be a powerful tool to help you avoid major problems, and grow your business even faster. And predictive analytics are especially valuable when used within a CX framework. This leads to another question...

How Does it Work with CX?

In the realm of customer experience management, predictive analytics can be put to work in several ways. However, we'll just highlight two key applications that a PA solution can provide when it comes to customer service: having the right people in place, and assisting them to say the right things.

The Right People

Your customer service representatives are the most important link between your customers and your brand. That being the case, you should put forth serious effort to retain reliable and high-performing employees.

Predictive analytics can help you to do so by uncovering significant trends in employee performance level, turnover rate, and other key data points. For instance, your PA model may predict that employees at high risk of quitting include:

  • Employees that have a long commute to work
  • Employees that are paid at or below a certain hourly rate
  • Employees that work 2nd or 3rd shift
  • Employees that have been in the same role for 3 years or longer

With this analysis in hand, you can identify which talented employees you especially want to keep, and take proactive measures to retain their services. Perhaps you can change their roles to some extent, provide them with an adjusted schedule, or investigate the possibility of remote work, to name just a few options.

These are important steps to take, because of this key fact: only high quality people can provide a high quality experience to your customers.

The Right Words

In addition to keeping the best and brightest of your workforce, you can also utilize PA to help your customer service reps in real-time. That's right: developers are continuing to build and fine-tune CXM tools that leverage predictive analytics to help your agents say the right things during a customer interaction.

As an example of how this process may work, imagine that a customer calls into your contact center. She has already called twice before about the same issue, and by this point is rather frustrated that her problem has not yet been resolved. A robust predictive analytics platform, coupled with previous customer interaction records, has to potential to provide a superior experience in a number of ways. These could include:

  • Automatically transferring the customer to the issue resolution desk, where highly trained agents have the authority and expertise to satisfactorily address her issue
  • Providing the agent that receives her call a go/no-go indication on upselling (probably a "No," in case you were wondering)
  • Providing real-time suggestions on how to approach the interaction, and even which departments to transfer her to, should an escalation become necessary

Such real-time feedback, powered by PA, can enhance the customer experience that your company provides on an enterprise-wide level. And with some 70% of buying experiences based on how customers feel they were treated, this capability should not be underestimated.

In summary, predictive analytics can help you to optimize your CXM, and keep your customers coming back for more. Of course, you need to have the right PA tools in place to accomplish this worthy goal.

At Augment CXM, our platform contains a robust PA solution that can actually help your customer service reps to analyze their interactions in real-time, and respond appropriately. If you'd like to set up a demo to see how our platform works, reach out today.

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