Proactive customer service in the digital realm isn’t new. It’s already table stakes for most B2C enterprises. Many use cases employ an element of early-generation AI or machine learning to detect and learn from customer behaviors to enhance future service.
However, the explosion of generative AI and sophisticated large language models (LLMs) is changing the game entirely. In fact, in our latest report ‘Where AI and Marketing Collide: 2024 CX Predictions’ – which polled 200 organizations and over 2,000 consumers – proactive customer service was identified as one of the key AI-powered CX trends for 2024.
We can all expect increasingly attentive, adaptive, AI-empowered engagement from service providers. But what might this look like, what’s in it for the brands, and what are the key AI enablers? Let’s take a look.
The generative gamechanger
The numbers hinting at the scale of AI’s likely impact on proactive customer service are…large. The aforementioned 2024 CX trends report, for example, cites the global chatbot market size as growing at a CAGR of almost 20% and forecasts it will be worth close to $5 billion by 2032.
So what is it that has gotten everyone so excited? What are the expectations underpinning these statistics? The big ticket AI deliverables for proactive customer service (that are either here or on the way) include:
Enhanced, empathetic CX
Arguably this is AI’s CX lightning in a bottle moment: enabling a personalized online service that combines the 24/7 efficiency, speed, and customer data analytics of the machine, with the intelligent, empathetic, and responsive approach of the human operator. Using machine learning to analyze and interpret behavioral and touchpoint data to provide individualized offers, recommendations, and experiences – even in multiple languages – is set to be a key source of competitive advantage for brands.
Handling complex enquiries
The ever-increasing sophistication in how AI applies natural language processing (NLP) to understand enquiries means they can successfully undertake more complex interactions without having to escalate to a human counterpart. Some organizations are also looking to apply ‘multi-bot architecture’ where different AI specialists can be called on to solve subject enquiries.
Greater customer understanding
AI-driven analytics enables organizations to collate and react to customer preferences and complaints in order to continually improve the CX offer.
Maximize sales, minimize cart abandonment
An AI’s provision of timely (proactive) information can go a long way to remove pre-purchase doubts and complete sales. A virtual assistant answering questions such as “will a size 14 be right for me?” or “is it compatible with my existing platform?” enables customers to buy confidently. Where items are left in the shopping cart, brands can initiate proactive conversations via customers’ channel of choice to give light touch reminders (and a fast route to purchase).
Cross-sell / upsell opportunities
Analysis of associated product sets as well as buyer behavior patterns enables organizations to make intelligent, personalized suggestions and product recommendations at the point of purchase.
Improved resource management
AI-enabled customer service can reduce brands’ cost-to-serve. The bulk of simpler enquiries are handled by the automated system leaving human operators to deal with the most complex, judgment-reliant questions as well as to oversee the quality of AI output. Our latest CX report confirmed that 65% of brands believe AI helps to streamline their customer service functions.
Sentiment analysis
Some AI platforms can now infer how customers are feeling based upon tone of inputs and can, and tailor responses accordingly.
Visual and voice interfaces
These are in the earlier stages of AI evolution. While basic visual interfaces (like text recognition) have been around for a while, it won’t be long before brands can use visual AI to solve customer problems in real-time. Meanwhile, advances in text-to-speech and speech-to-text technology are making it easier for AIs to participate in spoken interactions – this is nascent AI tech…but it’s coming.
AI systems have the potential to unite consumers’ insatiable desire to self-serve (45% of 35 – 54-year-olds actually prefer chatbots to human interactions) with brands’ desire to provide 24/7 always on customer service, a friction-free purchase journey, and enhanced CX.
A long way from plug and play – the data imperative
It’s a persistent misconception that AI integration is a plug-and-play exercise. Just hand over the car keys to AI, point at CX nirvana and say “Go for it, take us there!”. It isn’t, it won’t, and it can’t. Generative AI creates proactive, individualized experiences (and thus generates value) through the careful preparation and management of data. Here are four key considerations to shape this process:
- Data Health & Hygiene – ensure all datasets in the organization’s tech stack are desiloed, regularly updated, accurate, and united into a single version of customer truth. This will require some form of identity solution to ensure accurate record matching and alignment (distinguishing your J Smith, from your Joanne Smith, from your Jonas Smith).
- Data Enhancement – enhancing business-owned first-party data with second-party or third-party information, can add greater depth and detail to understanding of individual customers. This might involve forming strategic data partnerships with other organizations via data clean rooms, or ethically sourcing information from carefully vetted providers.
- Data Management – this is a multi-faceted consideration. Firstly, contact data degrades over time as peoples’ lives change (new jobs, new homes, new interests for example), thus customer records need to be kept up-to-date. Secondly, careful thought must be given as to what data is made available to AI-driven systems and how that data is used. Training the AI with customer details and behaviors must be carried out with close adherence to both the letter and the spirit of privacy regulations.
- Data Governance & Explainability – allowing an AI solution to process sensitive personal information (like PII) potentially exposes a business to a lot of risk and thus requires watertight governance. Neither math nor machines have morals and if the wrong data (such as health information) is used or exposed inadvertently by AI systems, the financial and reputational impact could be severe.
AI explainability meanwhile is the requirement for organizations to have processes that enable them to validate and therefore trust how the AI algorithms produce certain outputs or insights. Proactive customer service based on AI ‘hallucinations’ (non-existent connections, patterns, or inferences drawn by the AI), risks confusing and potentially alienating customers.
Get expert help
The complexities of integrating AI means the process needs to be carried out with care – and most probably with help. Strategic partnerships with data and identity specialists, such as Acxiom, can help businesses overcome the exacting data requirements of AI implementation.
When it comes to AI’s impact on CX, the headlines and the frontiers will likely remain the preserve of large tech enterprises. However, for every brand with customer facing functions the CX impact can still be transformative.
With AI-empowered proactive customer service, the last great barrier between the convenience of the machine and the empathy of the person is starting to evaporate. For further AI-CX insight and real-world examples, don’t forget to download our new report: ‘Where AI and Marketing Collide: 2024 CX Predictions’.
