The Sunday Mail
Cresencia Marjorie Chiremba
“Artificial intelligence will continue to change the way we interact with customers” – John Tschohl
The future of artificial intelligence (AI) and customer experience is exciting, as more changes in the way businesses interact with their customers are likely.
AI is already providing more personalised experiences for customers by monitoring their interactions across channels and consolidating useful data to customise services and product recommendations.
Most companies have changed the way they are serving their customers, because AI has made it easier for them to streamline customer experience and making support interactions faster, accurate and more consistent.
AI is raising the bar on how companies talk to customers.
It is also redefining how best to treat a ticket.
Treating a ticket is the process of tagging incoming tickets and assigning them to team members best-equipped to handle them.
When a customer query arrives at the help desk, customer service representatives need to read it, categorise it and route it to the person or team in charge of handling it.
With the help of AI, support teams are now automating simple solutions and addressing tickets more efficiently. Machine learning also helps companies to gain insights about customer issues.
Tools and automations that simplify workflows are boosting productivity and efficiency.
Chat-bots for business are used in handling simple requests, while automated processes are eliminating time-consuming repetitive tasks and removing occurrences of human errors.
Tasks that were once traditionally done manually, such as data collection and analysis, are now being automated to free up customer service representatives so that they can focus on complex issues.
This has resulted in improved customer experience, reduced costs and increased sales, as well as helping businesses to enhance customer lifetime value.
Customers are now getting personalised experience, thanks to AI.
Instead of grouping customers together into broad categories, AI can help firms target according to individual needs.
AI-based algorithms can analyse customer data to identify preferences, behaviour patterns and past interactions with a brand.
This data can be used to deliver personalised experiences, such as customised product recommendations, tailored promotions and personalised messaging.
Applications like machine learning and predictive analytics can uncover common customer issues and even offer insight into what is causing problems for users.
On the other hand, businesses are also being assisted to improve lead generation, generate data-driven customer insights, customised content and to streamline workflows, while preventing employee burnout.
Unlike traditional data analytics tools, with AI an organisation is able to predict client behaviour by regularly learning and improving from the data analysis.
This enables businesses to deliver extremely relevant content, which results in increased sales.
On the flipside, it is not all rosy when serving customers.
Using AI can be dangerous, if not used properly.
For instance, one of the dangers of AI in customer service is that it can be used to automate tasks that should not be automated.
Suppose a customer has a complex issue that requires human interaction, AI may not be able to provide the necessary support.
Some organisations are using this technology to collect and analyse customers’ data without their knowledge or consent.
This can lead to privacy issues.
Sometimes, the programing may not be done properly, which may lead to bias.
For example, if an AI system is trained on data that is biased against certain groups of people, it may make decisions that are unfair or discriminatory.
AI is also driving job losses.
As a result, the dependency ratio is going to increase, leaving the ones who are still employed with low disposable incomes.
According to a recent Brookings Institute report, AI may lead to a surge of wicked, legacy problems such as product steering, discriminatory pricing, unfair credit rationing and exclusionary filtering, among others.
1. Product Steering
This is when a company uses data to steer customers toward certain products or services. For example, if a customer is searching for a product on a website, the company may use AI to steer the customer toward more expensive products.
2. Discriminatory Pricing
AI can cause price discrimination by using data to charge different prices to different customers. For instance, if a company uses AI to analyse customer data and determines that a customer is willing to pay more for a product, they may charge that customer more. This can lead to unfair pricing practices and discrimination.
3. Unfair Credit Rating
Unfair credit rating is when data is used to determine which customers are eligible for credit and at what rates. Suppose a company uses AI to analyse customer data and determines that a customer is not eligible for credit, they may deny that customer credit or charge them higher interest rates, leading to unfair credit practices and discrimination.
4. Exclusionary Filtering
This is when a company uses data to exclude certain customers from seeing certain products or services. If a company uses AI to analyse customer data and determines that a customer is not likely to purchase a certain product, they may exclude that customer from seeing that product.
5. Digital Red-lining
Just like exclusionary filtering, they both use data to exclude certain groups of people from accessing certain products or services. However, digital red-lining is more focused on excluding certain groups of people based on their demographics, while exclusionary filtering is more focused on excluding certain groups of people based on their likelihood to purchase a certain product.
*Cresencia Marjorie Chiremba is a marketing enthusiast with a strong passion for customer service. For comments, suggestions, and training, she can be reached at [email protected] or at +263 712 979 461, 0719 978 335
