As we move towards a digital world, the relationship between businesses and customers has been changing over the last few years. With customers' expectations higher than ever, companies need to find new ways to interact with them and improve their processes and services' efficiency and quality. It’s in this context that several organizations are starting to board the AI train to enhance their customer service with smarter experiences and process automation.
Artificial intelligence can not only help companies accelerate application development, but it also allows end-users to interact with applications with greater ease. I’ll leave the whole AI-assisted development for another time. In this article, let’s focus on why so many companies invest in intelligent solutions and the most common uses of AI in customer service.
Why Investing In AI: Opportunities and Challenges
Long gone are the times when artificial intelligence was seen as the villain and the doom of humankind—but let's be honest, no one would watch the Terminator if T-800 looked like Siri, right? According to Gartner Hype Cycle for Artificial Intelligence, 2020, although the COVID-19 pandemic has slowed down the investment in this type of technology, only 16% of the companies inquired temporarily suspended their AI initiatives and 7% decreased them, while 30% increased their investment.
The reason for that is the verified benefits associated with AI that include:
- Improved user satisfaction: according to a study by Aberdeen and IBM, 33% of users are more likely to increase their satisfaction due to the personalized experiences offered by AI.
- Customer acquisition: companies that have invested in AI are three times more likely to acquire new customers.
- Customer retention: companies that have adopted AI are 2.5 times more likely to improve customer retention.
As a result, companies that invest in AI are able to increase their revenue and sales while saving a lot of money on operational and mundane tasks thanks to the automation provided by intelligent solutions.
However, only one in 10 companies have been able to take AI to production. Smaller companies struggle to adopt it, while larger ones with dedicated teams and tools only use it on the most strategic projects. Why is that? The answer usually lies in at least one of the following culprits:
- Lack of talent: it’s hard to acquire and retain experienced and knowledgeable AI developers and data scientists.
- Access to data: most companies don’t have access to quality and centralized data that AI-embedded apps can use and learn from.
- Know what to do: most organizations don’t know how to do it, what to do, and how to start an AI initiative effectively.
If you want to learn more about how to surpass these challenges, I invite you to look at my recent Trending TechTalk, Leveraging AI Across the SDLC to Create Great Experiences, where I cover these topics in greater detail.
AI Use Cases to Improve Your Customer Service
Now, if you’re looking to invest in artificial intelligent solutions but don’t know where to start, let’s take a look at a few of the most common use cases where organizations are using AI to improve their customer service while delivering engaging and modern applications.
Chatbots are a great way to provide a carefree engagement that fits your customer’s busy schedules and one of the most common use cases of AI in customer service. With chatbots, you can:
- Scale the first line of support: rather than having individuals respond to very specific needs, companies are using chatbots with AI-infused technology to answer the most common questions of users and, thus, decrease traffic to other support channels.
- Engage with customers: customers want intuitive and immediate experiences; they don’t want to fill out a form to put in a request when asking for simple information. With chatbots, you can give your customers feedback in a very conversational style.
- Support employees: a lot of the information your employees need to do their job can be stored in knowledge bases or Q&A formats. Chatbots can help them deliver that information much more efficiently by having an interface where they can just query and ask in a natural way.
Popular chatbot software include Drift and Intercom but, nowadays, modern app development platforms also give you the tools to quickly build your own chatbot services fully customized to your business needs. An example of that is CredAbility that decided to use a modern app dev to create fast-evolving chatbots that work as personal financial assistants to help users create individual action plans to achieve goals like improving their credit score or getting on the property ladder.
2. Language Analysis
Language analysis tools enable collaborators to extract key information from customer feedback and, based on that, adapt their communication.
Language analysis is a great asset to improve your call center experience. With it, your agents can detect if the customer they’re talking to is happy or unhappy and adjust their tone and actions accordingly.
A great language analysis example is Behavioural and Emotional Analytics Tool (BEAT), an application developed by Deloitte for a large financial services institution. BEAT listens to phone calls that the agents have with customers and transcribes the words and the sentiment in how the conversation happens to determine whether that customer seemed to be vulnerable and at risk of a bad outcome.
“Conduct risk is front of mind for both businesses and regulators. However, identifying it is often a time-consuming process driven by random selection. For financial services firms, it often involves reviewing significant volumes of customer interactions at considerable cost. Deloitte True Voice flags when language or behavioural aspects used to sell financial products could be at risk of non-compliance, raising it before it poses a serious conduct risk to organisations. Furthermore, it can help identify insights within firms’ customer interactions, driving value, optimising processes, and improving staff training.”
—Andy Whitton, Partner, Risk Analytics at Deloitte
3. Object Detection
Object detection solutions allow you to automate tasks related to image recognition. If you’re a Revolut user, you might have experienced this use case.
Think of a banking or insurance institution that wants to accelerate and improve new customers' onboarding experience. With object detection, your customers can simply upload a picture of their ID, take a selfie, and then automatically match the face in the ID with the selfie and validate the customer’s identification, replacing the whole cumbersome process of doing it in-person.
This will save you a lot of time and provide a much better experience for your customer.
4. Optical Character Recognition (OCR)
Optical character recognition is commonly used in document processing automation.
With OCR, you can train your systems to read a document, like an invoice or an order, extract relevant information and automatically populate the right fields in your system. This way, you're able to process documents in a more digital and efficient way, to support quicker and more accurate information retrieval from paper documents.
5. Machine Learning Models
You can train machine learning models and integrate them into your apps to weave and evolve predictive analytics that will help you make better and more informed business decisions. There are several use cases to machine learning, the most common being:
- Automation of an approval decision, risk analysis, or business outcome;
- Classify tickets for triaging support, assigning to the right team, recommending a solution.
- Predict sales discounts, product demand, customer support demand, and available stock.
Randstad, for example, built an application that uses machine learning to enable recruitment consultants to instantly scour the entire Randstad candidate database to find the best people to fill their clients’ vacancies.
“Before Spotter, getting the right information from the database was so labor-intensive and complex that many of our recruiters simply didn't use it in the right way—they’d just work with the handful of candidates they knew. Now, Spotter makes it really easy for recruiters to quickly identify the best talent within our entire candidate database, finding talent that is outside of their own network, so user adoption has been incredibly high—we have 2,000 recruiters using it in the Netherlands already.”
—Anne Reuver, Principal ICT Manager, Randstad Groep Nederland
Take Your Customer Service to the Next Level
Customers today are more demanding than ever. They expect smooth and effortless experiences that rival the best experience they have ever had. And artificial intelligence can play a crucial role in this equation by improving the efficiency and—let me add— the coolness of your processes and services provided.
If you want to see these five use cases of AI in action, join me in my TechTalk Leveraging AI Across the SDLC to Create Great Experiences where together with my colleague Riva Uy, we’ll show how you can add AI to apps to enhance the user experience and better engage customers, while sharing a variety of real-world examples.