Why AI Contextualization Is Key for Modern Customer Experiences

Modernizing beyond customer personalization to reach effective contextualization is one of the ultimate aspirations for brands. It’s an acute challenge many hope to crack by tapping into AI capabilities—but aren’t exactly sure how. The race to tailor customer communications and customize product recommendations has largely stopped short of delivering true contextualization: the ability to understand what a customer wants immediately at a particular moment.

The voice, virtual assistant, and chatbot ecosystems are particularly ripe for leveraging AI to solve this contextualization challenge. Those getting it right quickly can reap the benefits of competitive differentiation. Here’s what to know about enabling AI to shape your voice and virtual assistant brand experiences around customer contextualization (while eliminating one of the biggest roadblocks—AI bias).

Low-Code Lets Any Engineer Harness AI

A low-code development strategy has the power to remove many of the barriers brands encounter when implementing voice and virtualization assistants capable of AI-powered contextualization. A traditional strategy requires trained experts able to hard-code AI-enabled applications. However, there are only 300,000 AI engineers and 60,000 data scientist engineers worldwide; competition and costs for that talent are steep.

Low-code offers a more practical approach for accelerating development productivity to build AI engines, introduce contextualization, iteratively refine its accuracy, and scale voice and virtual assistant applications. These key advantages bring contextualization within reach for many organizations that would otherwise find assembling the necessary resources much more difficult.

A Case in Point: Ulta Beauty’s Context-Aware Chatbot

The largest beauty retailer in the U.S., Ulta Beauty, set out to create a 24/7 customer service chatbot able to recognize complex intents within customer communication and respond with full contextual awareness. The desired customer experience would offer a night-and-day difference versus simple keyword-centric chatbots, which have limited intent recognition and cannot recognize context. Using low-code, the retailer rapidly prototyped to build an AI engine and training dataset, as well as connectivity with its internal order and inventory management systems and customer service representative queue. Ulta then iterated the chatbot using natural language processing until it could recognize 110 customer intents and respond accurately to recognized customer issues. The chatbot combines contextualization with personalization: For instance, responses to a customer interested in makeup products will consider previous skin tone/color detection and further personal data when delivering contextualized responses.

Today, the retailer has built greater brand trust via the thousands of excellent customer experiences its context-aware chatbot delivers on a daily basis. Ulta continues to improve upon the chatbot using its iterative low-code development process.

Voice 2.0 Is Coming to Put Brands in Command of Powerful Contextual Voice AI

Voice assistants present another opportune playing field for brands to present contextualization-backed customer experiences powered by AI. In the Voice 1.0 era, brands have been able to communicate with their customers using only the voices of the big three assistant platforms: Amazon’s Alexa, Apple’s Siri, and Google Assistant. Brands have also needed to conform to those platforms’ guidelines, and they’ve been unable to access platform data essential to building personalized and contextualized experiences.

Voice 2.0 represents a sea change: Brands will be able to take direct control of the infrastructure they use to deliver voice experiences, giving them total latitude in how they shape those interactions. Voice marketing platforms such as Instreamatic are leading the Voice 2.0 shift, enabling brand-customer conversations at every brand touchpoint. Backed by contextualization, Voice 2.0 lets brands speak in voices all their own, and understand customers as they voice their needs across customer support and feedback channels, interactive voice advertising, and more.

As a medium, Voice 2.0 will power an evolution in customer comfort with and expectations around brand voice communication. Customers can speak three times faster than they can type, and communicate much more naturally when doing so. Unlocking this richer and more direct feedback will tell brands all they need to know to more quickly and accurately optimize brand experiences. Expect many brands to jump at this opportunity, harnessing Voice 2.0 and contextual voice AI to deliver customer satisfaction and earn loyalty like never before.

Human Bias Must Not Hinder AI Efforts Around Contextualization

Per Gartner, human bias will yield false results within 85 percent of AI projects through the end of the decade. Overcoming this bias is critical. Brands’ AI-driven voice, virtual assistant, and chatbot initiatives cannot fully harness contextualization and accurately serve customers without effectively purging training data and algorithms of bias.

There are several types of bias that can affect AI systems, including developers’ unconscious cognitive biases—which can lead to incomplete data and flawed AI conclusions. Another potential hiccup is deployment bias, which can cause poor results even when working with bias-free AI systems. Aggregation bias occurs when the accumulated effects of many biased and mistaken AI project decisions hinder effective contextualization.

AI-powered assistants and chatbots represent the voice of their brand, so if they display an intrinsic bias to a customer, it can result in significant brand damage and the need for major revisions to an expensive AI project. To prevent these outcomes, brands should implement effective anti-bias measures such as AI frameworks, toolkits, processes, and policies. AI frameworks provide automated checks and balances that help remove bias, while toolkits can detect and eliminate biases present in machine learning models and pipelines. Brands should also have processes and policies in place to regularly assess data for specific bias metrics and enforce best practices for bias detection and removal through governance controls.

Contextualizing Voice, Virtual Assistant, and Chatbot AI

Brands that can achieve contextualization have the power to build relationships and connect with customers naturally on a human level even while using voice, virtual assistant, and chatbot AI to enable 24/7 access to brand communication at every possible brand touchpoint. By leveraging strategies such as AI-enablement speed of low-code, AI-powered Voice 2.0 infrastructure, and anti-bias measures within AI processes, brands can successfully offer understanding voices that will transform customer experiences going forward.

Shomron Jacob is an engineering manager focused on applied machine learning and AI at Iterate.ai. Jacob began his career as a software engineer but soon found himself learning ML/AI, and switched his professional direction to follow it. He lives in Silicon Valley.