Solutions Review’s Expert Insights Series is a collection of contributed articles written by industry experts in enterprise software categories. In this feature, IKASI co-founder and CEO Anthony Chong offers a comparison between business intelligence vs. personalization.
Over the last 20 years, business intelligence (BI) software has become ubiquitous in enterprises, as it provides great ways for organizations to visualize their data, track key performance indicators, and find compelling insights. However, this technology is becoming increasingly outdated, as more enterprises are moving away from traditional BI tools in favor of those that offer high levels of personalization.
The major drawback of BI was that it promised to improve business performance through insights, which are broad conclusions or takeaways garnered from aggregating data and understanding the so-called “average” customer. Like early online clickbait ads promised to reveal “one simple trick,” these insights were supposed to provide key information that could determine whether a customer was profitable or not. For instance, BI software might produce an insight that tells you if a customer buys three products in a row, then they’re your customer for life. Or, if customers under 40 years old spend twice as much as customers over the age of 40. Unfortunately, this approach was incapable of describing what was really going on.
Table of Contents
Business Intelligence vs. Personalization
Why Business Insights Alone Are Ill-equipped
The insights produced by BI tools (and generated from a single variable) are incapable of recognizing the complexity of individuals, making them functionally useless for a business. Not every person will behave the exact same way and not every customer will be described by these generalized statements. There’s usually a more complex story, and many more caveats needed to describe what’s really going on.
Worse, drawing sweeping insights from averages of data can lead organizations to conclusions that are the opposite of reality. BI software was, and is, not equipped to handle the messy realities of customer behavior.
Enter the Age of AI-powered “Hyper Personalization”
Instead of using BI tools to analyze customers in groups based on one particular bucket or antidote, modern artificial intelligence (AI) solutions enable organizations to draw conclusions based on each individual person.
These AI-driven approaches allow organizations to take into account a multitude of variables to create a personalized analysis and a more accurate reading of that customer’s individual behavior. AI provides the ability to collect multidimensional data on each customer, and understand individuals’ unique stories at the scale of hundreds of thousands of customers. Instead of flattening all that data to glean broad insights, businesses are conducting personalized analyses for individuals and are capitalizing on the insights all the way down to their unique behavioral data. Looking at what they’ve interacted with, for example, organizations can see how often a person is coming into a store or engaging with a brand. Rather than trying to pattern-match by comparing their behavior with that of other customers, they can be much more responsive to the individual and other changes in their specific behaviors.
By looking at each individual as the fundamental unit of analysis, businesses are drawing conclusions from deeply personalized experiences, and can individualize customer journeys. As opposed to earlier BI offerings, AI is unveiling an unprecedented level of understanding that helps to allocate internal resources and budgets more efficiently, because decision making is based on that person’s preferences and behaviors. Rather than relying on single-variable-based insights on a broad group, businesses and customers now understand that to truly appeal to customers, they need to undergo a mentality shift, and focus on the multifaceted variables that contribute to the specific individual’s behavior.