After enjoying years of steady growth, your business suddenly sees your customer satisfaction scores sinking. What happened?
When you investigate, you find that your customer support team is simply not keeping up with the volume of requests that they’re receiving. Customers have to wait two or more days for a first response, and they’re voicing their discontent in growing numbers on social media.
What Are The Best Customer Service Automation Technologies?
You don’t have enough money in the budget to hire and train more support staff, so the only realistic solution is to use AI and automation. Which use cases should you try to automate, and which tools should you use? What will deliver the highest ROI for your business?
1. Virtual Assistants For Automated Customer Service
Virtual assistants are common in customer service. Also called bots, chatbots, or digital assistants, they interact directly with customers to provide information, process support inquiries, or solve simple problems.
Virtual assistants vary in technical complexity, ranging from being simple scripted experiences to leveraging state-of-the-art natural language processing (NLP) and understanding (NLU) techniques. However, even the best bots on the market can’t handle every potential customer request, especially if the request is complex. As a result, most companies opt for some sort of cooperative model, in which bots and human agents work in tandem.
Should your business use a front-end bot or a hybrid model? Check out our model comparison article to learn about the factors that you should probably consider when making your decision.
2. Agent-Facing Bots For Faster Human Service
Chatbots aren’t just for irate customers. You can also deploy virtual assistants to support your agents, such as by providing quick-reply templates, conducting faster searches of internal knowledge bases, or supporting other operational steps. While these bots don’t interact directly with your customers, they can dramatically improve customer experience by decreasing the average resolution time for your support team.
Microsoft AI offers agent-facing bots as part of its Dynamics 365 solution. Internal departments at Microsoft, as well as HP and Macy’s, are already using this technology to improve overall customer satisfaction and to handle more requests in a shorter amount of time.
3. Chatbots For Conversational Commerce
Virtual agents are also useful in sales and marketing to convert casual browsers into paying customers. Many brands have deployed chat- or voice-based retail experiences on Facebook Messenger, Amazon Echo, or other interactive platforms. For example, Domino’s chatbot take your pizza order if you type “pizza” in Facebook Messenger, while its Alexa Skill even tracks your order when asked! More elaborate examples include eBay’s ShopBot, which will find specific items if you provide a name or upload a photo, or Hipmunk on Facebook Messenger, Slack, or Skype, which you can talk to when planning and booking your next trip.
4. Sentiment Analysis For Customer Insights
How do customer really feel about your brand and your products? Sentiment analysis analyzes textual data, such as emails, social media posts, survey responses, or chat and call logs, for emotional information.
Though sentiment analysis has been used for decades, AI-powered methods can now transform subtle nuances in textual data into accurate insights about a customer’s feelings, needs, and wants. You’ll be able to find out when customers are having a specific issue with your product, which will help you to take more focused action in a timely manner.
IBM Watson’s Tone Analyzer, for example, can parse through online customer feedback and determine the general tone of users reviewing a product.
5. Automated Routing For Streamlined Issue Handling
Where should you route a customer email? AI-powered solutions can catalogue customer intent, such as trying to get more information about an item, requesting a refund, or updating a delivery address, and route them to the right recipient in much less time than humans could. Magoosh, a student prep company, reported that deploying the DigitalGenius AI software for ZenDesk had reduced its customer queue by half.
6. Emotion AI for Increased Customer Satisfaction
Emotion AI, or affective computing, trains machines to recognize, interpret, and respond to human emotion in text, voice, facial expressions, or body language. Imagine that a customer has been chatting with a customer service representative who doesn’t understand the issue, emotion AI would promptly escalate the customer to a supervisor based on the frustration that it detected through word choice or tone.
The use of emotion AI can also benefit physical retailers. Cloverleaf, a retail technology company, has incorporated Affectiva’s Emotion AI technology into its shelfPoint solution. Adding emotion AI allowed shelfPoint, which displays digital advertising in high-definition LCD display strips that are wrapped around shelf faces in a store, to capture customer engagement and sentiment data at the moment of purchase. By utilizing data on the shopper’s affect, age, gender, and ethnicity, Cloverleaf reported that impulse buys and purchases for P&G products were 40% higher with shelfPoint than on shelves with conventional advertising materials.
7. Recommender Systems For Cross-Selling And Up-Selling
Recommender systems personalize product placement and search results for each consumer. Recommending products or content that customers are more likely to purchase gives the customer a better sales experience while driving more revenue for businesses through cross-selling and up-selling.
Several algorithms currently power the majority of these systems:
- Collaborative filtering is based on the assumption that people with similar characteristics and interests are more likely to prefer the same items. This approach was considered “state-of-the-art” in 2009, but it’s still the most widely-used in business settings. However, it needs some information about a user before it can make good recommendations, which is a serious limitation for new businesses..
- Clustering algorithms group together users who have similar interests. This approach works well when business lack sufficient customer information, or as a first step for recommender systems when collaborative filtering can’t be applied directly due to the sheer number of users or items.
- Deep learning uses neural networks to assist in the filtering of items and then rank the recommendations according to a user’s history and contextual conditions. Using deep neural networks is currently the state-of-the-art approach for recommender systems.
8. Contextual Analysis To Increase Retail Sales Volume
You’ve just used up your last bit of shampoo, and after you get out of the shower, you notice that Amazon has automatically ordered a refill that will arrive tomorrow. This scenario sound futuristic, but some companies are already offering just-in-time sales.
Do you drink black coffee on Monday mornings next to your office but prefer Triple Mocha Frappuccinos while at the Sunday farmer’s market? Starbucks employs sophisticated AI-powered algorithms to predict your preferences at any particular location or time. Besides considering standard factors like purchase history, its algorithm also considers contextual factors such as the time of day and weather conditions. When a customer approaches a Starbucks location, its app may send out a text message or a notification with a relevant recommendation. Ordering is as simple as responding to the message.
9. Facial Recognition For Automated Payment
Beyond just giving recommendations, AI may be able to predict your entire order just by analyzing your face! Alipay and KFC have teamed together to test facial recognition software that allows customers to automatically pay their bills by smiling at a camera. The software easily recognizes a returning customer and uses order history information to improve menu recommendations.
10. RPA Solutions For Reducing Business Response Time
Robotic process automation (RPA) automates tedious, routine tasks by mimicking how human users would carry out tasks within a specific workflow. Large banks with millions of daily customer interactions will likely be the pioneers in deploying RPA solutions. Example use cases for RPA can include verifying a credit applicant’s income, expenses, and exposures across several databases to speed up credit assessment or tracking bank account transactions in real-time to assist with fraud detection.
ICICI Bank, one of India’s largest banks, has deployed RPA in over 200 functions across its organization, including in retail banking, foreign exchange, trade, and HR management. Customer-facing bots process remittances and research loan choices, while internal company bots sort and route customer emails based on criteria like transaction status. Deploying RPA has cut ICICI’s customer response time by up to 60%, and some cases, has decreased the error rate to virtually zero.
11. Anomaly Detection for Reducing Fraud
In order to protect consumers, credit card companies will decline transactions if they appear to be fraudulent. Unfortunately, these decision support systems often err on the side of over-protection and decline legitimate transactions.
In the US, e-commerce retailers lost an estimated $8.6 billion dollars from false declines in 2016, $2 billion dollars more than the fraud that these systems were designed to prevent. This strategy is costly to retailers because a third of customers will stop shopping at a retailer after experiencing false declines. Moreover, false declines undermine the retailer’s ability to accurately combat fraud.
To address these kinds of issues, payment processors like Paypal and Mastercard increasingly rely on AI technology to reduce fraud. Their algorithms learn from their customers’ purchase histories and compare payments against likely patterns of fraud. Mastercard‘s Decision Intelligence tool, for example, leverages information like customer value segmentation, risk profiling, location, merchant, device data, time of day, and type of purchase made to determine what are normal and abnormal spending patterns.
Businesses Increasingly Rely On AI To Meet Customer Demands
Many companies have already recognized the power of artificial intelligence in improving their customer experience model. As a result, customers are already becoming used to personalized experiences and 24/7 customer support that are accurate and consistent across all of channels. In order to meet growing expectations and to keep up with their competitors, companies should carefully examine where they can implement AI technology to improve their own services.