As enterprises seek to differentiate themselves with a uniquely personalized customer engagement model, human-to-human connection – or at least the appearance of humanization – has become a growing priority. From Facebook—which encourages users to “check in” with the company after a tragedy or disaster in their area—to Amazon, whose curation algorithm displays seemingly hand-picked products for each online customer, companies are coming to an important realization: for maximum impact, a customer engagement model must consider each individual’s personal, human journey, not just a generic user’s most likely path. And they are turning to technology to bridge that gap.
The concept of humanized connections powered by technology is, to many, paradoxical in and of itself. Many people correctly relate machine learning (ML) with advanced computing tools such as Artificial Intelligence (AI), but share the common misconception that digital ML/AI tools are the antithesis of “humanized” or “personal.” ML technology, in actuality, plays a very meaningful role in establishing personal customer connections and enhancing customer engagement at scale.
Why? Consider this: leveraging humans to connect with customers on a true 1:1 level would require enterprises to hire a dedicated marketer for each of their customers—an impossible prospect. But for organizations dealing with hundreds of thousands, millions or tens of milions of customers, ML can easily make every interaction feel like a 1:1 human connection, providing faster and more targeted personalization for every customer than would ever be possible through sheer manpower alone.
In fact, when it comes to improving customer engagement by humanizing interactions, many of today’s most meaningful customer connections don’t rely on humans at all—they are a carefully crafted product of Machine Learning.
ML is an algorithmic technique that empowers computer systems to intelligently uncover correlations and patterns in data sets. A program enhanced with ML is able to “learn” from data as it changes, drawing new insights and conclusions from the data set without being specifically programmed to do so.
When enterprises integrate ML capabilities into their customer engagement platform, they can begin to derive continuous intelligence about customer preferences with “in-the-moment” context. ML is able to simultaneously process what has happened (i.e., the customer’s transaction history) with what is currently happening (i.e., the customer walking through a store) and use that data to predict what will happen next. This intelligence, which informs increasingly accurate decisions over time, can be utilized to orchestrate the customer journey and enable a wide range of scalable, hyper-personalized interactions, such as:
These intelligent interactions demonstrate to each customer that the enterprise understands exactly who they are, is aware of their unique needs, and will do all that they can to assist them—in real time. In fact, new data-driven developments in ML such as Natural Language Processing (NLP) allow computer systems to understand and communicate with customers in terms that replicate real human interactions, further supporting the business’ ability to make authentic customer connections without human resource constraints.
With the predictive intelligence of an ML-powered customer engagement model, enterprises across industries and sectors can unleash and scale more humanized interactions to improve customer engagement, satisfaction, and loyalty.
As demonstrated in the examples above, customer data is the foundation of any effective exercise in ML personalization. It is this rich customer data—a combination of transaction history, real-time channels and signals, and even current environmental variables—that provides the needed context for an ML solution to deploy a successfully humanized interaction in a personalized customer engagement model.
ZineOne’s Customer Engagement Hub leverages ML to optimize stream processing and event correlation, always absorbing and adapting to customers’ unique preferences to deliver increasingly relevant, humanized engagement at scale. Learn more about how it works.