How Machine Learning Can Deliver Contextual Insights for Effective Brand Experiences

While terms like “digital transformation” are firmly entrenched in the zeitgeist of the marketing world, its true expression as a means of taking prospects further down the sales funnel from awareness to consideration is far more nebulous. The ability for marketers to gather and use data to create meaningful, personalized brand experiences has traditionally been at the expense of context.

In truth, context is the all-important subtext to understanding current perceptions and experiences with any brand. Context is king because it is the framework that enables digital transformations in marketing, gathering the right data at the right time, laying the groundwork for meaningful brand experiences.

Delivering context to brand encounters on a micro level ups the ante on gathering data, but more importantly on crunching that data to reveal opportunities. Here is where AI comes in to provide marketers with the ability to do the heavy lifting of interpreting data that can deliver those connective brand experiences on an individual level. The true benefits of big data and machine learning to marketers in terms of context targeting is in its infancy.

According to the 2017 Real-Time Personalization Survey by Evergage, 33 percent of surveyed marketers use AI to deliver personalized web experiences while 63 percent are aware of its ability to increase conversion rates. That should come as no surprise as more marketers place greater importance on the use of first-party data to target audiences through delivery of quality ads regardless of served impression quantity.

The Role of AI in Paving the Way for Effective Brand Experiences

AI, or more specifically machine learning, can filter and refine large data volumes to analyze and predict customer moods and taste in the context of time and place. By developing a profile of how they interact with a brand or specific message, it becomes possible to develop accurate profiles that marketers can use to adjust and optimize campaigns to engage an individual in the place and manner they prefer to motivate action.

Within data sets, anything that informs motivations is part of context, so customer feedback and digital interactions hold the key to developing meaningful brand experiences. The goal is to use AI to match customer interest and behavior profiles in response to brand campaigns from first party data. This enables brands to learn from the specific context of daily customer interactions for more refined and targeted buyer seller relationship that is the foundation of delivering effective personalized messages at scale.

We sit in awe of Google’s and Amazon’s abilities to use online algorithms to determine purchase intent and buyer moods, but similar tools are readily available to all of us today. AI can provide context to the rich data that can stimulate and capture consumer interest for lead generation. These existing platforms incorporate machine learning (a subset of AI) and natural language processing (NLP) to deliver relevant content to consumers.

The Many AI Paths to Brand Experience

NLP uses real-time semantic analysis for contextual word comprehension and processing in a similar fashion to the human brain. This puts sophisticated technologies that can learn over time into the hands of marketers so that they can make real time decisions regarding ad placement for specific, and actually valuable, target audience reach. Context provides the relevance that enables machine learning to go beyond simple keyword classifications. Consequently, marketers can detect traffic and behavioral trends that are relevant to guiding ad alignment and more effective customer service.

With the rise in omnichannel marketing strategies to harness the explosion of consumer data now available, AI will play a key role in the integration of several different messaging platforms to create a unified user experience that already includes the use of chatbots and virtual assistants. Marketers will move away from focusing on each channel separately and, with the help of AI, start to assess their marketing campaigns as a whole.

These AI and machine learning-based tools are capable of processing and analyzing big data from the varied sources where businesses gather it in ways that even a large team of marketers could not with unlimited time. As a support to these marketers, machine learning algorithms reveal hidden insights within the data that otherwise would be overlooked in data gathered from thousands or even millions of encounters.

Of course, it will continue to require the experience of human marketing expertise to shape that analysis into meaningful brand experiences. While the evolution and use of AI in marketing is still in its early stages, it is clear that marketers looking to create those unique brand experience will need to begin thinking about how AI can and will affect the business. That means starting to explore its possibilities now as to how they can use machine learning and semantic technologies to deliver more relevant campaigns today and tomorrow.

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