AI is the go-to answer for process automation problems of many kinds, including marketing automation. But how can AI enhance marketing automation beyond its obvious potential to cut steps out of those processes?
Even the conventional tasks that AI takes on in marketing automation are by no means trivial. They include content distribution across channels, lost opportunity/reengagement notifications, customer nurture prompts (birthday/anniversary notifications, etc.) and passive lead scoring -- qualifying leads based on analysis of demographic, behavioral and social media factors.
AI can supercharge some marketing automation tasks and processes beyond mere automation, offering considerable ROI. Here are some examples.
Chatbots. AI is already making a difference in marketing automation deployment of chatbots -- enhancing marketing campaigns with highly targeted, optimized outreach and decision support. Any chatbot can follow a script as customer support bots do, but a marketing chatbot must respond far more flexibly to off-script dialogue from a potential customer. Its range of activity might include product launch contacts, data collection, lead evaluation, reengagement, loyalty scoring and other functions. Marketing chatbots must be deft, learning as they go.
Personalization. AI-based analysis of customer profile information, combined with continuous analysis of online consumer behaviors, can increase customer engagement and retention rates by bolstering comfort and satisfaction at every interaction event. AI can analyze customer purchase history and browsing behaviors; anticipate customer online presence and receptivity; and select personalized content.
Voice and visual search. Many customers turn to visual sources when seeking products they need -- a trend that's been in place for some time. However, they also turn to voice-based devices in those searches, making voice and visual search correspondingly important to marketing automation. AI is essential to both of these emerging technologies, providing them with recognition capability that uses machine learning.
Frequency optimization. One of the negative effects of the personalization trend is that it sometimes runs aground when information about individual customers is missing. One such case is frequency optimization -- if businesses contact a customer too often, they get annoyed; not often enough, they may drift away. Through machine learning, AI can deliver a contact frequency sweet spot for individual customers.
Assumption testing. More and more, CX is becoming a science, dependent upon the repeated testing and evaluation of new ideas for optimization. Assumption testing is central to this research and involves setting up simulations and/or live contact experiments with limited customer samples and then analyzing the results to see which assumptions are strong enough for real-world deployment. However, a lot of traffic is necessary for the results to achieve statistical significance. AI can be a huge help here, running repeated analyses with subtle shifts in variables -- ultimately, delivering confident results.