AI-Powered Analytics Drives Real-Time Personalization
Businesses today can no longer survive simply by meeting the expectations of their customers. They now need to anticipate their desires and deliver in real time, every time. As a result, even a previously proven and comprehensive marketing strategy is often ineffective. Consumers are frequently changing buying habits, curious about new “hot” offerings coupled with constant competitive price commoditization. In parallel, all of this activity is producing a massive amount of detailed data.
Similarly, with the evolution of many enterprise technologies, traditional approaches are now falling short. For instance, not too long ago business intelligence (BI) software was the method most organizations preferred to track key performance indicators and discover previously unseen insights that help drive profits and identify customer preferences. Traditional analytics is the foundation of business intelligence, which involves gathering historical data, performing statistical analysis, and deriving conclusions so the business can make informed decisions. Relying on predefined rules and static models, the approach worked well, but it had its limits. While traditional analytics excelled—and still do—at providing insights into past performance, they can’t predict future trends or suggest the best actions, which can be a significant disadvantage to maintaining loyalty and improving profits.
Given this, these tools are considered outdated, and organizations are adopting modern approaches that support the next level of personalization. To create meaningful connections with consumers that strengthen brand loyalty and increase revenue, organizations are now harnessing the combined power of artificial intelligence (AI) with analytics. This concept represents a quantum leap in data-driven decision making because, unlike traditional analytics, AI can analyze vast amounts of data and millions of concurrent experiments in real time. More importantly, it allows businesses to detect patterns and trends at incredibly granular levels that would be impossible to identify through traditional means. This enables them to proactively respond to emerging market shifts and changes in consumer behavior.
By leveraging AI, organizations can also introduce the concept of prescriptive analytics, which goes beyond traditional descriptive analytics. Unlike descriptive analytics, which presents you with what happened, prescriptive analytics offers recommendations on what to do next—before it happens. This is proving to be a game-changer for businesses, as it provides actionable insights so data-driven decisions can be made with confidence.
The Hyper-Personalization Revolution
Hyper-personalization uses first-party data, machine learning, and AI-driven algorithms to create individualized customer experiences such as suggesting products, creating tailored website messaging, providing customized offers, etc. One of the most compelling use cases for AI is enabling businesses to create things like product recommendations that are not only based on a person’s past purchases but also their current mood, recent behaviors and interests, preferences, and even the weather outside.
This method differs from traditional segmentation techniques that create groups of customers (and offers) based on common themes such as shared likes, dislikes, demographics, and activities. Hyper-personalization drills down to the minute differences that can be used to target customers at an individual level. Achieving this level of communication is becoming increasingly critical, as a study by Gartner found brands risk losing 38 percent of their existing customer base due to poor personalization efforts.
How AI Promotes Continuous Experimentation
Another benefit of AI-infused analytics is that it doesn't stop at predicting customer behavior; it also provides actionable recommendations. Identifying the optimal experience for each unique customer is achieved through continuous experimentation and learning. By thinking of each customer engagement as an experiment, marketers can use AI to measure what works and what does not and move beyond traditional A/B testing, which is sporadic, slow, and in most cases a very manual effort.
The key to successful AI adoption is understanding its potential and aligning it with business goals. It's not just a technological upgrade; it's a paradigm shift that has the power to reshape industries, drive innovation, and better meet the specific needs of that customer. Unlike earlier BI solutions, AI helps to allocate internal resources and budgets more efficiently, because decision making is based on each person’s preferences and behaviors. However, it is imperative to address several critical factors in this process, including these:
Hire the right professionals. Securing the right talent is pivotal to any AI initiative. Employing data scientists and AI specialists is a good start, but organizations should also look for ways to upskill current employees to work with AI systems.
Ensure high levels of data quality. As the backbone of any AI system, the quality of an organization’s data can make or break the underlying effort and any subsequent results. Cleansing legacy data, and adopting rigorous data collection and validation protocols, helps companies ensure that their data is accurate, current, and comprehensive, enabling them to mitigate any biases or inconsistencies.
Select the right infrastructure. Adopting the necessary hardware and software is a given as it creates an environment where AI can thrive. Yet organizations should consider adding cloud platforms and high-speed processing capabilities to help achieve real-time insights and help manage costs.
But remember, AI efforts must be in harmony with business objectives. Companies must define clear goals for their AI initiatives to ensure they align with the larger mission and values of the organization. The benefits of AI are evolving at a rapid rate, so rather than relying on single-variable-based insights on a broad group, businesses need to undergo a mentality shift and focus on the multifaceted variables that contribute to the specific individual’s behavior.
After all, every individual has their own customer journey and preferences. Leveraging AI for hyper-personalization, organizations can better understand individual preferences and incentives in real time so they can engage with customers in a highly personal way, help motivate them earlier in the funnel, and help strengthen both loyalty and profits.
Corne Nagel is lead data scientist at IKASI, an innovative self-learning platform powered by AI. IKASI specializes in hyper-personalizing engagement experiences for business and marketing professionals at the customer level, aiding them in enhancing their net revenue growth.