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Customer Service Machine Learning Applications: 5 Things to Consider

Customer Service Machine Learning Applications: 5 Things to Consider

As a customer support manager, you may be looking for a machine learning application to better analyze and understand your current customer support data. When it comes to customer ticket routing accuracy and handling, black box solutions no longer cut it in an increasingly complex world. Instead of moving forward with no idea how an algorithm works, you want transparency. You need to know that you can input your own company’s data in a way that generates actionable results to move you toward greater customer satisfaction and retention. In other words, you need a machine learning (ML) application you can trust.

When looking to implement a machine learning application for your customer support operation, here are five things to consider:

1. Is the application transparent and controllable?

Gone are the days where enterprise-level ML learning applications can operate under the veil of mystery. After all, human-machine working relationships should be built upon a solid, transparent foundation in order for human users to trust the system.

Thomas H. Davenport, co-founder of the International Institute for Analytic, puts it this way: “Humans will want to know how the cognitive technologies came up with their decision or recommendation. If they can’t get into the black box, they won’t trust it as a colleague.”

In a real-time interactive environment like customer support—where any mistakes have real business costs—any technology affecting customer interactions must be transparent and controllable. Machine learning is not infallible; the decisions artificial intelligence makes will not be 100-percent correct, 100-percent of the time. Therefore, it’s important for support leaders to have insights into these decisions, as well as the opportunity to regulate them on a case-by-case basis. Only then can they control the risk—and the cost—of a potential failure.

Customer Service Machine Learning Applications: 5 Things to Consider

2. Are you using your own data?

Every business is unique and has its own corpus of historical, machine-readable data. When it comes to machine learning, one of the major advantages is its ability to provide highly accurate results that reflect a business’s unique support processes. Be skeptical of ML applications that are based on an overly generic data set. Relying on tools powered by data that isn’t specific to your system negates that key advantage of ML: the ability to learn from historical data and adapt to changing conditions that are specific to your business.

Technology analyst Kurt Marko echoes this idea. “The best predictive systems improve over time, learn from previous events, adapt to changing conditions, and optimize to improve key performance metrics. These attributes are especially important in customer support systems, where the customer mix, channel usage, quality and quantity of data, and business priorities are quite dynamic.”

When choosing a machine learning application, be sure it’s one that will be able to utilize and learn from your individual company’s data.

3. How actionable are the results?

Many ML tools in the marketplace help business owners with predictive analytics. This type of business intelligence is critical in customer support scenarios, where machines can learn from past customer interactions and work to provide quicker solutions in the future.

On the other hand, any predictive analysis that doesn’t drive specific, actionable business decisions is just noise.

When purchasing an ML app, consider how well it will bridge the gap between data and action. Applications which enable augmented and automated process improvements will have a direct impact on your key performance indicators and overall business success.

4. Do you need a fully customized app?

There are a lot of ML consultancies that will build custom applications, based on a core platform they’ve already developed. This approach can be very powerful because of its high level of customization. However, it’s important to understand the development resources required to create these solutions. Before you agree to purchase, establish clear expectations of the development timeline and associated costs.

On the other hand, if you are looking for a tool that will start generating ROI within a matter of weeks, production-ready applications that are build to tackle specific use cases might be a better fit.

5. Are you prepared for change?

Effective ML tools can fundamentally change your customer support model, delivering greater efficiency and consistency in your daily operations. However, you can’t just wave a magic wand and expect to start seeing results.

In order for artificial intelligence (AI) to learn from and replicate the decisions that your agents are already making, your practices must be systemized and consistent. Take some time to organize your existing data before bringing machine learning tools to the table. Then, be prepared to deliberately and systematically replace older manual systems with automated ones, and be patient with your agents in the process. As time goes on, ML tools will streamline and improve your highly complex manual processes, leading to more efficient customer support communications overall.

For more information about how machine learning can impact your bottom line, download our Essential Guide to Automating Customer Service.

The Essential Guide to Automating Customer Services, Wise.io

Topics: Machine Learning, Customer Success, Customer Support