In the realm of customer experience (CX), Artificial Intelligence (AI) stands as a transformative force, reshaping how businesses interact with their customers.
CX refers to the customers’ perceptions and feelings resulting from interactions with a brand‘s products/services. The perception involves every touchpoint a customer has with a company, including customer service, marketing, branding, and product design.
CX is often misinterpreted as User Experience (UX) as job descriptions and conference topics often interchangeably use them.
To understand more about the differences between CX and UX, read our previous article.
The Advantages AI Offers to CX
One-to-One Personalisation
Also known as hyper-personalisation is a marketing strategy that uses AI (Artificial Intelligence) and ML (Machine Learning) to deliver personalised and relevant experiences to customers.
By analysing customers’ past behaviours, interests, and preferences, brands meet customers’ expectations and benefit both customers and the brand, as customers expect brands to know their preferences.
AI analyses customer data to create a holistic view of each customer, including purchase history, browsing behaviour, and social media activity, to identify patterns that businesses can use to recommend products/services to customers.
By delivering personalised experiences, brands can increase their revenue as a study by Epsilon Marketing shows that customers are more likely to make a purchase when they receive personalised recommendations. In addition, customers who receive personalised experiences are also more likely to become loyal customers, increasing customer lifetime value.
As seen in Spotify, the platform uses AI to create personalised playlists for each user based on their listening history and preferences. It improves the customer experience and encourages listeners to continue using the platform.
Predictive Analytics
Predictive Analytics uses AI-powered algorithms to analyse customer data, identifying patterns and trends that can help businesses foresee customer needs and preferences.
This algorithm helps identify potential customer churn, the loss of customers or subscribers for any reason. By analysing customer data, businesses can identify customers who are likely to leave and take proactive measures to retain them.
Predictive analytics benefits businesses by improving operational efficiency by identifying patterns and trends in customer data, allowing businesses to optimise their process to serve their customers better.
Predictive analytics in various industries and applications:
- Telecommunication Companies – Used to identify customers likely to cancel their services and provide targeted promotions or discounts to encourage them to stay.
- Airline Companies – Used to optimise flight schedules and reduce wait times for passengers to improve their travel experience.
Sentiment Analysis
Sentiment Analysis uses AI algorithms to analyse customer feedback and reviews to gain insights into customer opinions, emotions and attitudes.
Businesses can identify areas of improvement and address customer concerns by analysing customer feedback across a range of channels, such as social media, online reviews and customer surveys.
An example of a business using sentiment analysis is Airbnb. By analysing sentiments expressed in user interactions, reviews, and social media discussions, Airbnb can identify areas for improvement. By using the feedback to improve its services, the business makes itself a go-to option for those looking for short-term accommodation.
Ethical Concerns Regarding AI in CX
While AI can potentially enhance overall customer satisfaction, it also raises significant ethical concerns.
Data Privacy & Security
AI systems rely on vast amounts of customer data, raising concerns about how it is collected, stored, and used and whether it adequately protects customers’ privacy and sensitive information.
As the data collected and stored by AI systems increases, the risk of data breaches also rises. Hackers or data breaches can expose sensitive customer information if AI systems are not adequately secured.
Data breaches could lead to a loss of trust as doubt will arise regarding a business’ ability to safeguard customers’ personal information, resulting in negative publicity – making it difficult to attract and retain customers.
Algorithmic Bias
AI algorithms are designed to process large amounts of data to make automated decisions; if the AI’s training data is not representative, the algorithm could introduce biases, leading to unfair treatment or discrimination.
Algorithmic bias can be seen in recruitment algorithms companies use to screen resumes or assess job applications. Training the algorithm based on historical data could result in biased patterns – such as preferring candidates from specific backgrounds, limiting workforce diversity and perpetuating existing hiring disparities.
Amazon’s discontinued usage of recruitment algorithms is due to the discovery of gender bias towards women. The engineers used data from resumes submitted to Amazon over 10 years from predominantly white males to create the algorithm.
As a result, the algorithm recognised word patterns in resumes rather than relevant skill sets, penalising any resume that contained the word “women’s” in the text and downgraded the resumes of women who attended women’s colleges and benchmarked against the company’s predominantly male engineering department to determine an applicant’s fit.
Importance of CX
Good CX occurs when a company consistently meets or exceeds a customer’s expectations at every stage of the customer journey.
Good CX is important as unsatisfied customers switch brands or move to a brand that offers better CX. Research shows that 61% of customers are ready to change brands immediately after just one negative experience. A report written by Oracle shows that 84% of companies see a revenue boost after improving their customer experience.
Additionally, good CX helps increase customer retention by fostering customer loyalty, leading to repeat purchases and higher spending. Retained customers make more purchases and spend more often than new customers as they understand the value of the brand’s product/service.
Will AI replace CX Agents?
The purpose of AI implementation is not to replace – but to enhance the overall customer experience.
A business’s overall operations have been made easier with AI solutions with automation features. By automating mundane tasks, human agents can work on higher-value work, such as closing deals.
However, businesses should not rely heavily on AI solutions as human agents are needed to handle tasks that require empathy, emotional intelligence, complex reasoning, and critical thinking skills.
If a customer has a complex or unique issue that falls outside the typical scenarios an AI is trained to handle, such as cancelling services for a deceased loved one, they may feel frustrated or ignored if the AI fails to provide a satisfactory solution.
Conclusion
While AI can perform some tasks more efficiently, it cannot replace the human ability to demonstrate creativity and empathy. The collaboration between AI and human agents leverages businesses’ strengths to deliver the best customer experience.
