Harnessing AI to Serve Business Goals in Customer-Centric Service and Sales

Artificial intelligence (AI) has made a breakthrough in almost every area of business. Most companies already have projects underway trialling AI solutions, and significant investments have been made in AI. Technology is rapidly changing the operating environment of companies and organisations, as well as the lives of consumers. Many companies are currently experimenting with AI, and some have progressed further than others. For example, many content creators are utilising generative AI. In customer service and sales, various bots and other AI solutions are being deployed.

But how can the benefits of AI be ensured in a company with a broad customer base? How can we ensure that the customer experience remains excellent while developing a culture of learning and experimentation around AI opportunities?

My concrete background

Throughout my career, I have worked extensively on the production, utilisation, and development of data analytics. Since 2016, I was responsible for the development and operation of a CRM system ecosystem utilizing AI (including predictive and adaptive analytics). The speed of change is illustrated by the fact that the term 'AI' had not even become established in 2016. We scaled the system for multichannel use to streamline and improve sales (physical, outbound, and online) and customer service. The basic idea was to use AI, data analytics, and rule-based systems to provide the customer, or the salesperson or customer service agent serving them, with the best next recommendation – an offer or a service action – in a fraction of a second.

The potential of AI

AI has enormous potential to develop customer service and sales into efficient, productive, and customer-oriented operations. Yet, as a consumer, one still notices that even large organisations can face challenges in coordinating customer interactions. For example, the next department does not always know what the previous one promised the customer, and the same customer might be called multiple times regarding the same matter.

A pitfall of large-scale AI utilisation can be fragmented development, where significant benefits cannot be measured and the customer experience can become confusing.

Solutions:

Based on my experience – and when aiming to use AI in CRM solutions as a large-scale customer interface tool – AI should be linked to, or at least supported by, the following functionalities:

1. Customer identification: Everything starts with identifying and authenticating the customer.
2. Recognising customer context: Everything starts with the customer's context. When a customer is in the middle of a situation, they are looking for a solution to it, and everything else is secondary. For example, the smooth handling of banking matters or buying a birthday gift are situations where the customer needs a fast and efficient solution.
3. Utilising rule-based systems: By leveraging AI, efficiency, productivity, clarity, and fluency can be brought to sales and customer service. Rule-based systems ensure the fulfillment of business goals and an excellent customer experience. For example, customer structures and sales or customer service rules or interaction strategies can help AI produce solutions based on deeper customer understanding. Customer structures help to understand, for instance, whether the customer is only a user of the service in the household, or if they are also the decision-maker or contract-holder for the family. Are they perhaps a legal guardian for someone else? Do they act in a decision-making role in a company?
4. Memory – interaction history and events. Nothing is as annoying as having to explain everything from the beginning. It helps a customer significantly if they can trust that their interaction history is remembered and that future service needs and their fulfillment are based on it.

These functionalities create the necessary 'backbone' in an AI-enhanced environment to ensure a consistent customer experience across an AI-driven service and sales landscape.

Background on AI and rule-based systems

AI is based on various technologies and methods, such as:

• Rule-based systems: Programmed logical decision models.
• Machine Learning (ML): Using data to train models.
• Deep Learning (DL): Neural networks and the analysis of vast amounts of data.
• Natural Language Processing (NLP): Understanding text and speech.

Predictive analytics forecasts future events based on data, while adaptive analytics adjusts and improves its performance over time.

Examples of rule-based systems

1. Product structures and configurators: For example, modular products where parts can be combined according to specific rules.
2. Customer structures: Identified and unidentified customers with their respective roles.
3. Customer service progression rules: Processes that guide customer service.
4. Business process automation: Purchase requests, approval processes, etc.
5. Statutory systems: For example, payroll and customs clearance systems.
6. Price rules and discount calculation: Special pricing for loyal customers.

Rule-based systems work well in predictable situations, but flexibility is often needed in changing environments. AI machine learning offers additional potential for the optimal management and execution of processes.

AI, identifying the customer and their context, rule-based systems, and remembering interaction history together can revolutionise customer service and sales, provided they are implemented strategically and in a customer-centric way. Is your company ready to harness the full potential of AI? 🚀