From Financial Services to Life Sciences, companies are working to define new methodologies around customer engagement work streams. The urge of defining a new way of customer engagement is rising from the shift in the customers’ mindset as mentioned by Deloitte. More than being a part of a segment, now, customers want to be treated as individuals. They want to receive personalized interactions and recommendations. In today’s world where data is massive and complex, to meet customers’ demands, companies need to have more advanced ways of engaging with them. At this point, Artificial Intelligence (AI) is seen as one of the most powerful enablers of personalization. With the correct implementation of Artificial Intelligence, companies can answer the question of “How to personalize the customer experience?”
This sounds great! But it’s likely that one hearing these arguments will take a step back and ask “why do companies need necessarily AI to go further? Why the classic statistical models are not enough to answer these questions?”
Shifting From Statistical Models to Artificial Intelligence
Let’s start by picturing the current business process. There are business analysts who look at dashboards and reports for pulling some insights from data by using their expertise. They are doing decision making by looking at those insights. This is so-called “backward-looking” data. Does it look like a scalable solution? Can a team of business analysts analyze a company with a customer universe of thousands of people who are all expecting personalized interactions at any time during the day? So, the answer is no! The current customer engagement methodology is not scalable and needs to be replaced with a more advanced way. At this point, Artificial Intelligence automates combining business intelligence and the decision-making process.
This doesn’t mean that Artificial Intelligence could take the role of business analyst. Independent of how much AI can produce meaningful results its role will be supporting decision-making and not replacing the human decision-maker. Let’s make a more clear example of this idea. By using statistical models companies can answer questions like “What is the correlation between digital channel engagement and customer satisfaction?”, because statistical models will allow us to perform inferences on the data. By answering such question business analysts does a great analysis of the current situation – backward-looking at data. On the other side, Artificial Intelligence will allow us to answer questions like “Based on the digital channels engagement what is this one customer’s experience likely to be in his next interaction?” So Artificial Intelligence will help us to find out patterns in data instead of relying on scoring models which only count the number of interactions. In order to do so business analysts are still playing a crucial role in providing human expertise.
Companies who are shifting towards AI-driven methodologies know pretty well that when customers feel they are viewed as individuals and their needs are met, they are far more inclined to be loyal to brands and become advocates. Through this journey, another important question to answer is which are the use cases of AI that can elevate customer engagement experience?
Artificial intelligence and Next Best Action Use Cases
Let’s look more deeply at what kind of use cases could be developed with Artificial Intelligence in the customer engagement field!
- Artificial Intelligence can support your business in defining a successful sales rhythm by analyzing the success over channels and using it as regular reminders to sales teams. Based on consumer interest and primary business objectives AI enables real-time decision-making.
- Make sense of unstructured data! For example, customer characteristics such as age, gender, etc. are structured data so they are easily comparable. An online bookstore can simply compare the preferences of their readers who are aged between 18-25 to 26-30. However, the content of the book is unstructured data. It is not easy to compare the content of two books just by their cover pages. Here comes the role of Artificial Intelligence which can tag the content of a book so we would know this book talks about a certain era in history. Then the online book company could make a more advanced analysis of the preferences of their customers thanks to AI.
- Targeting at right customers with relevant offers, a specific product, or a service-based solution depending on their needs. Personalized interactions add high value to customer experience. Recommenders help you find content that goes together. Content that you’re looking for or that you didn’t know that you wanted.
- AI speeds up your recurring decision-making process. Let’s give an example of ROI! If you need to make a one-time marketing campaign analysis and assess your ROI it’s fine to proceed with the traditional methods. But, what if you run many campaigns at the same time with thousands and millions of customers and want to take action in real-time? Then you’d need an AI system that can immediately analyze the current ROI and recommend the next best action.
- Is it possible to have a handwritten rule for all of your business processes? No, hand-coded rules are hard to maintain! AI can help you to transform business rules into automation by recognizing the trends and patterns in data so you can focus on more important questions.
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All of these sound appealing, right? It would be great to have a system that analyzes your business in real-time and gives you insights at the right time with the right message. The Next Best Action and Artificial Intelligence have a lot to do together. Next, we talk about how to start with a data and AI-driven approach in your Next Best Action Journey.
Creating an AI-data-driven approach for Next Best Action
Companies that start for the very first time with their Artificial Intelligence journey are mainly struggling with planning and placing the new solution into the existing business process. A soft approach is to start with evaluating data and technology maturity and define which are the small steps to take. AI is not something companies will buy or implement one time and use for a long time. On contrary, it requires starting small and evaluating routinely so the solution can be fine-tuned for your needs. So it’s better not to wait for your data to be perfect instead start small by understanding it.
This 4 step methodology can be insightful for starting your AI-driven NBA journey:
- Data foundation: Have a customer-data platform that provides a 360-degree view. Having so will allow you to monitor what is already available and what is required. Eventually, you will be able to formulate a data strategy.
- Extract insights: Collaborate with advanced analytics teams for following the main signals of your data. Understand how your business rules are related to the insights extracted. In the end, you’ll be able to map business intelligence and your decision-making process clearly.
- Desing AI models: Find a small business question that can be answered by AI – based on the mapping at step 2 – and start developing governance around this model. Identify the impact of your model by having a POC that runs parallel to the current methodology.
- Distribute: Once the stakeholders are satisfied with the success of the model it’s ready to be placed into the business process! Remember AI is not a one-shot integration so collecting feedback and improving the models will increase reliability while creating a culture of innovation.
