As a result of digitization, companies from all industries have more data than ever at their disposal: about their customers' needs, perspectives, and behaviors. The challenge is to use modern technologies such as Natural Language Processing (NLP) and conversational analytics, together with artificial intelligence (AI) and machine learning (ML), to raise and evaluate this wealth of data in order to design a customer journey with a consistently positive experience, i.e. make data-based decisions regarding its content offerings, identify trends, monitor the progress of campaigns or detect mood swings at an early stage. Conversations with customers are compared with their actual behavior to derive strategies for the target groups and to see the customer experience from the perspective of the customer.
In the future, marketers could be relieved of decision-making regarding the optimal subject line of an email or even the entire campaign content. Natural Language Processing (NLP) captures natural language and processes it computer-based with rules and algorithms. NLP uses various methods from linguistics and combines them with modern computer science and artificial intelligence (AI). In email marketing, appropriate solutions can propose sentences, words or phrases that work particularly well within a certain content paradigm or for a certain target group: e.g. in relation to economic conversions or human reading and consumption habits. AI holds an almost infinite potential for use in marketing: assessment of customer potential through neural networks, highly intelligent chatbots through natural language processing, or automated target group and sales analysis through deep learning. But while new insights and tools are constantly emerging in the AI context, their use in practice still lags far behind.
Fortunately, not all industries are “AI-shy”, especially not in the B2C sector, but B2B industry, like brand manufacturers since companies in these industries want to build first a solid database before dealing with further trend topics. Here too, the focus is on automating the very own marketing processes, improving existing data quality, and breaking down data silos. Anyone who is in a dynamic and highly competitive market not only has to meet customer expectations, but create added value in a highly competitive environment, too. NLP and AI-supported analysis technologies can e.g. interpret sentences and reveal the emotional reaction of customers as well as discrepancies and tensions in unstructured customer feedback.
An omnichannel strategy does not consider the channels in isolation, but rather analyses the entire feedback of all digital touchpoints in one context. The insights are gained from the interactions across all core channels, including CRM, social media, and chat conversations. Whether during the registration, trial subscription, program selection, conclusion of a contract or the payment options, the influence of the individual digital touchpoints along the customer journey provides all teams involved with a context-related understanding. In this way, strengthening internal company communication contributes as well to process improvement.
By Daniela La Marca