Improve Customer Service With A Planned Approach To Machine Learning

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These days, it’s hard not to run across a story in the news about machine learning (ML) or artificial intelligence (AI) announcing some new feat or potential use. (And it’s easy to be confused by the two.)

As technology races to develop ML and AI, companies across industries are standing by to adopt these new solutions to increase speed and efficiency of work throughout all aspects of their business.

Customer service is one such area seeing an influx of potential applications.

Two recent pieces of research–an Accenture report and a survey–illustrate some of the challenges facing customer service today, how ML can benefit it, and the planned adoption rate of ML and AI by CIOs. Both are recommended reading, but to summarize just a few points drawn from the report:

  • 27% of customer service environments have more than 100 possible assignment groups for new customer cases
  • 15% reported having over 100 categories to choose from when attempting to classify cases
  • As a result of these many choices, is it a surprise customer support functions spend approximately 12% of their time categorizing, prioritizing, and assigning cases?

The bottom line is that customer service is spending a lot of manual time reviewing and sorting through cases, time that could be spent on other higher-value work, such as working directly with customers. But what is necessary to start up, enjoy, and succeed with ML or AI in customer service?

Identify Automation Opportunities

The Accenture report demonstrates the business value of ML and AI is there, but potentially only in certain circumstances–so choose wisely.

Per the report, one recommendation is that unstructured, redundant, and mundane tasks are the work patterns that typically benefit from automation. For example, following the central study points of the report, consider focusing on the classification and routing of customer service cases. This high-volume, arduous activity can be slow and fraught with errors. Unlike humans, machines can handle tens of records just as quickly as thousands. As such, ML and AI can not only improve the speed and accuracy of categorization, prioritization, and assignment of customer cases, but it can have a positive impact on customer satisfaction by preventing such delays to resolution. The increases in productivity and time savings then allow customer service agents to focus on other important work.

There’s an additional benefit to moving this type of work from humans to machines. Employees are less stressed, bored, and exhausted by performing this monotonous task, aiding in retention as well as reducing the error rate.

Adoption And Ongoing Use

ML and AI have become more mainstream and significantly easier to adopt. The challenges of getting started with and maintaining ML and AI capabilities are simply no longer present. It’s not necessary to find and employ an army of data scientists and others with highly specialized skills.

One key reason this has occurred is that ML and AI have become a standard offering with modern customer service management solutions. This allows companies to create the initial data models themselves. From there, ongoing adjustments and updates with new data are just as easily performed.

High-Quality Data Required

Data is the basis upon which ML is initially founded and will empower or limit its capabilities, notably in terms of accuracy. That being the case, what is possible through the automation of tasks like case categorization, prioritization, and assignment and initial success will be highly dependent upon data quality.

Companies must evaluate the quality of data prior to turning it over to ML to build its own logic. They may also need to determine if their processes have been digitized to the extent they have been capturing the correct data to feed their ML algorithms. Modern customer service management solutions have guidance to assist here. It is also worth investigating if there is third-party data obtainable that could further enhance the quality of ML efforts.

Measure And Report

It is critical to continuously measure outcomes to reinforce the value ML and AI brings to customer service. The same metrics for volume, productivity, and efficiency of cases categorized, prioritized, and assigned are initially useful to compare just how much faster and cost-effective ML and AI are in comparison to human efforts, but other metrics are necessary for true improvement.

ML can process tens of thousands of records, but starting out accuracy rates may be low. Despite the best efforts, algorithms and data models will not perfectly sort all cases in the beginning and cases may need to be manually addressed when errors occur. Just as with humans performing the work, monitor the accuracy rates. When errors occur, use the opportunity to teach the machine why it was wrong so it doesn’t occur next time. Accuracy rates will improve.

Empowering Your Customer Service With ML and AI

As ML and AI continue to evolve and improve, the limits of what’s possible will further erode. Simultaneously, it will also become easier for companies to adopt and expand automation throughout their business. Customer service is one such area benefiting from this trend. The latest and greatest customer service management platforms are removing the mundane, daily work for customer service agents thanks to ML and AI that doesn’t rely on data scientists so that agent skills can be utilized elsewhere.

Companies that fail to take advantage of ML and AI will not only continue to struggle to keep up with customer service work, but they might also be at a competitive disadvantage as a result of slower, less responsive customer service. The path forward is clear: it’s getting easier and easier to adopt ML and AI in customer service, and doing so means not only will employees welcome the reprieve from monotonous work, but customers will benefit from the faster resolution of their problems.

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