SUMMARY
Customer-centric engagement demands enterprises to know each one of their customers, predict their needs and act on an individual basis
Human decisions and actions cannot achieve the desired scale and speed of customer-centric marketing in the digital age
AI need to be autonomous and explainable for marketers to trust, adopt and use quickly
In today’s competitive world, B2C enterprises irrespective of the industry where they belong, focus on building long-lasting and mutually rewarding relationships with their customers. This explains the shift in customer engagement strategies and actions from being product-driven to more customer relationship-driven in recent times.
Customer-centricity helps enterprises to not only understand customers deeper but also to predict and meet their needs at any point in time. It is a proven fact that B2C enterprises who have successfully moved to customer-centric engagement are enjoying a significant competitive advantage and an uplift in sales and revenue.
Steve Jobs certainly proved why getting closer to customers will help in telling them what they need before they even realize it. But it is not easy for enterprises to achieve this across their millions of customers.
Rapid advancements in AI and ML technologies will allow enterprises to effect this true customer-centricity at a scale never achieved before.
Here are the three key Analytics and AI trends that will play a critical role in transforming customer-centric engagement by 2025.
Autonomous Marketing
As AI learns and evolves, we can expect the AI to do the whole marketing process from data analysis through decisioning, execution and optimization without any manual interventions. This will let marketers to focus on setting up strategies, creative planning and defining the boundaries for machines to operate.
With machines taking over marketing actions, enterprises will never miss an opportunity to engage with customers and generate incremental value in an optimal way. This is made possible by precise decisions in the moment of relevance eliminating human bias.
Explainable AI
For autonomous marketing to succeed, humans need to trust the decisions and actions taken by the machines. And this holds true for marketers as well. AI cannot be a black box, its decisions and actions need to be explained and understood by marketers for its adoption to increase.
Explainable marketing AI will emerge that is focused on establishing this trust factor with its beneficiaries and users by clearly explaining the reasoning and rationale behind its decisions and actions. Machines can state the decisioning logic and predict the output of its actions beforehand to marketers.
And once marketers are convinced, they will trust AI more to decide and act on its own not only in that demonstrated context but also in such similar business contexts.
Responsible AI
Seamless access and explicit consent to process customer data is absolutely essential for enterprises to developing long-lasting and trustworthy customer relationships. Responsible AI will emerge that will diligently put in place stringent customer data ownership and privacy enforcement mechanisms. There cannot be any ambiguity in taking the permission of customers to use their data.
Even the better-known brands and larger new age enterprises have burnt their fingers badly in this aspect. Just like humans, machines also need to explicitly mention what data is being requested and also the intended purpose of using the data. The practice of AI being sensitive and responsible to honour the privacy of customer data will mature beyond just data aggregation and anonymization or even data localization.
There will be more sophisticated algorithms in place to respect customer privacy, for example, training machines to analyze shared data in a virtual way without actually sharing or exchanging data.
Summing It Up
Advancements in AI in the next 5 years will be greatly influenced by its foreseen applications for managing customer relationships. However to fulfil this promise, AI has to evolve and we are seeing vendors and research community focusing on perfecting AI, already in laboratories, to be more autonomous, explainable and responsible.
