Here are some simple truths: one, customers don’t like to spend time talking to customer service agents. Two: repetitive, low-value tasks lead to demotivated employees and higher attrition rates.

Fortunately, AI can now help businesses avoid or at least, improve both situations. In this age of Big Data, businesses now have reams of data at their fingertips, especially customer service functions that are highly process oriented. But without the right tools, having the data is of no use. With AI technologies such as machine learning and Natural Language Processing, businesses can now make use of this data and glean “automatic insights”.

Here are some ways AI can help:

1. Recommendation engines: Recommendation engines can help customers firstly by personalizing product recommendations based on their customer profile. Secondly, when a customer reports a problem, recommendation engines can offer troubleshooting options based on her past preferences.

2. IoT devices: By virtue of being connected to the internet, IoT devices let customer service agents find accurate product information and diagnose product status. This helps reduce diagnostic errors and reduces average handling time. They are easily updated and may even be equipped with the ability to self-repair, thus reducing down-time and increasing customer satisfaction. Devices like the Amazon Echo also act as a sales channel, helping customers find products faster.

3. Chatbots: Customer care centers can offer 24/7 support with the help of chatbots. Using Natural Language Processing (NLP), chatbots can “understand” customer messages. They can respond to routine inquiries and help capture customer information reducing the need for a human agent.

4. Email processing: Large businesses are flooded with emails from customers every day. NLP and machine learning algorithms help extract relevant information from emails and the system then either takes immediate, automatic action or routes the email to an agent.

5. Intelligent troubleshooting: Studying historical troubleshooting data can help resolve customer problems more efficiently. Through predictive analytics, an agent is directed to the predicted best solution based on how similar problems were resolved historically. Alternatively, cases may be routed to agents based on their past success in resolving problems of that particular nature. This increases agent confidence and delivers a speedy resolution to the customer, saving time and costs. On the same basis, voice call or Interactive Voice Response (IVR) scripts can be setup to automatically adapt to real-time responses.

6. Task automation: Routine processes such as onboarding customers or straightforward troubleshooting can be automated using Robotic Process Automation (RPA) solutions. By reducing manual effort and automating repetitive actions, the organization’s employees are able to focus on more complex tasks.

7. Sentiment analysis: Using NLP and machine learning technology, emails and calls can be mined for behavioral signals to determine case priority and routed to an agent with the appropriate skillset. Sentiment analysis also helps analyze customer feedback and social media reviews to generate insights on where the company is doing well and where it’s not. Negative reviews can be flagged for an immediate response.

8. Anomaly detection: Consider a scenario where a regular customer orders 5000 cases of raw material instead of her usual order quantity of 500. Or consider a situation where an employee sends in multiple invoices for the same expense item to be reimbursed. Machine learning algorithms can flag such orders for manual intervention. Orders can then be corrected, if needed, and any invoices found to be fraudulent are rejected before payment. Anomaly detection also helps trusted customers fly through the checkout process (think Amazon’s 1-Click) while adding additional checks for cases signaling suspicious activity.

A win-win situation

When business owners and managers begin to share data visualizations and analytics’ insights with their employees across teams, it helps build a culture of analytics in the company. When employees are empowered to make better decisions, employee morale and performance goes up, leading to more satisfied customers.