Can we harness AI to empower every customer with their very own personal shopper, consultant and guide?
Does everything feel the same in the age of AI? Whether we talk about search results, articles and papers written by AI, chatbots, or image and video searches – many have rightly pointed out a feeling of sameness pervading everything that AI touches. This wasn’t supposed to happen. One of the central tenets of the AI revolution was its potential ability to personalize each result according to individual user needs and preferences. So, why does the reality of AI personalization seem a long way off from delivering on that promise? The answer may be more technical than you think.
Technologies Enabling Personalization and Consistency in Messaging
To understand this lack of personalization, it is important to understand the technologies driving AI results. We enable personalized experiences for users by analyzing their queries, behaviors and patterns. This real-time analysis enables us to deliver more relevant results with each query. One of the core technologies behind AI-driven personalization is Natural Language Processing (NLP), which enhances AI models used in search by interpreting the intent and sentiment behind user inputs. By doing so, NLP allows AI to deliver more personalized and relevant results in real time, helping brands and customers tailor their communication more effectively. Finally, there is cross-channel data synchronization enabled by API integrations. This enables brands to provide consistent messaging across diverse platforms including web, socials, email, text, phone etc.
The Touch of Magic at the Back End
While technologies like NLP and cross-channel data synchronization handle the personalization and consistency users experience on the frontend, the backend is where much of the heavy lifting occurs. AI models rely on real-time data to make intelligent predictions and deliver personalized results Powerful search and analytics engines like Elasticsearch and Apache Solr provide distributed data indexing and real-time retrieval, ensuring that relevant and personalized results can be delivered quickly. For example, Uber uses a custom prediction system built on Elasticsearch, Logstash, and Kibana for accurate, data-driven predictions.
The real-time data processing essential for AI models is powered by frameworks like Apache Kafka and Apache Flink, which handle real-time data streaming. AI relies on these systems to continuously update its models with new information, ensuring that search results and personalized recommendations evolve in real-time. LinkedIn, for instance, processes nearly 7 trillion messages per day through Apache Kafka to maintain its peer-to-peer network messaging, supporting AI-driven personalization at scale.
In addition to this, cloud infrastructure plays a crucial role, with distributed systems and databases like MySQL, Postgres, and MongoDB storing massive amounts of data needed to train and update the AI models. Microservices allow independent scaling of these backend services, and caching solutions like Redis ensure that personalized results are delivered faster. Finally, big data frameworks such as Apache Hadoop and Apache Spark enable large-scale data processing, with companies like Yahoo processing over 600 petabytes of data via Hadoop nodes, showcasing the scale at which these systems operate.
How AI Helps Us Understand Customers Better
All these separate technologies at the front and back-ends come together to form what we recognize as AI-driven solutions. Natural language processing helps companies understand nuances in customer feedback, social media posts, and support chats for more precise insights into customer wants and needs. Recommendations systems employ collaborative filtering and content-based filtering to analyze viewing history, ratings, and user interactions to serve up personalized content suggestions. There is also a degree of predictive analytics involved in almost all digital customer interactions today powered by machine learning models.
The amalgamation of different data streams helps companies understand customer behavior at a granular level that simply wasn’t possible before. With strategically deployed AI systems, companies can now understand the likelihood of the customer making a purchase or the probability of churn, reasons behind an abandoned cart and more. They also have the ability to take strategic steps in near real-time to serve and retain customers better.
Why Companies Fail to Achieve Ideal Outcomes with AI
AI helps companies develop a cohesive outlook on every customer’s past and present behavior that can help them strategize and anticipate future needs. Given that the right implementation of AI can not just enhance customer experience, but unlock a degree of insights and opportunities to personalize each customer’s experience that is not possible with traditional systems, why are many companies falling short of achieving the ideal outcome? Why do many AI implementations look and feel the same and end up causing more frustrations for the customer? Just imagine your latest frustration with a customer service chatbot or an AI search result that simply refuses to provide you with the pertinent option or the exact answer that you are looking for.
This lack of relevance in AI results often stems from two critical issues: insufficient data infrastructure and poor-quality data. On one hand, data infrastructure, such as pipelines and processing frameworks, is essential for feeding AI systems with real-time data. On the other hand, the quality of the data itself, whether it’s clean, unbiased, and representative, determines how effectively AI models can learn and predict. Without both, even the most advanced AI systems struggle to deliver meaningful personalization.
Quality of training data can make or break AI systems. This determines how well your AI algorithm can understand customers. You need exceptionally high-quality, diverse and representative data in order to develop accurate and unbiased models. This means taking care to ensure thorough data cleaning and preprocessing, continuous learning for AI models with fresh and relevant data and careful curation to avoid or at least, minimize bias in AI models. Additionally, careful data curation is essential to minimize bias in AI models and ensure they provide relevant, personalized results.
Core Technical Challenges in Implementing an Omnichannel Strategy
Experts often talk about the importance of having an omnichannel strategy in delivering hyper-personal results and harnessing AI for your business. And yet, precious few talk about the finer points of building a technical infrastructure that can actually deliver on that strategy. To deliver truly personal results, organizations need real-time decision-making capabilities. This must be built atop an infrastructure that can pull and process disparate datastreams for the real-time outputs to make a qualitative difference in user experience.
While many organizations collect vast amounts of data, few are able to leverage the right analytical insights or hyper-personalization in user experience. Organizations running on legacy software technologies lack APIs or real time data processing capabilities essential for hyper-personalization. If your infrastructure is still coping with data silos or fragmentation, gaining a unified view of customer behavior or ensuring data consistency across touchpoints can prove challenging. Companies must invest in modernizing their technology stack and consider upskilling their engineers to build systems capable of AI-driven personalization.
It is also necessary for companies to establish strong data governance frameworks and deploy privacy-preserving technologies early on. Data privacy regulations such as GDPR and CCPA have major impacts on hyper-personalization strategies. The only way to continue to provide personalized experiences for your users is to adhere to these regulations and acquire clear consent for data collection and processing.
A Technical Infrastructure that Delivers
Seamless omnichannel experiences are built on strategic data pipelines and APIs that enable multiple systems to communicate with each other and share data in real time for consistent and updated flow of information across channels. For asynchronous communication, where systems need to exchange information at different times, middleware technologies like Apache Kafka or Rabbitmq can help ensure data consistency and reliability. Many modern software architectures struggle with data consistency, which is where centralized data management platforms can prove invaluable. These platforms ensure even delivery of consistent data across all systems.
With all of that in mind, it is important to remember that machine learning systems can only improve from here. Emerging technologies may provide us with the means to further improve personalization and user engagement. Search results will get more contextual and we will see improved accuracy in not just search, but predictive analytics as well. Hyper-personalization is likely to completely transform how users interact with systems and will enable brands to anticipate customer needs and offer recommendations in a way we haven’t even probably thought of yet. It is important for companies to think ahead and future-proof their technical infrastructure as users are likely to demand expertly tailored user experiences.
Hyper-personalization has the potential to transform customer interactions, but only if companies build the right technical foundation. Is your infrastructure ready to deliver on AI’s promise?
