Welcome to the forefront of conversational AI as we explore the fascinating world of AI chatbots in our dedicated blog series. Discover the latest advancements, applications, and strategies that propel the evolution of chatbot technology. From enhancing customer interactions to streamlining business processes, these articles delve into the innovative ways artificial intelligence is shaping the landscape of automated conversational agents. Whether you’re a business owner, developer, or simply intrigued by the future of interactive technology, join us on this journey to unravel the transformative power and endless possibilities of AI chatbots.
Latest
AI
Amazon
Apps
Biotech & Health
Climate
Cloud Computing
Commerce
Crypto
Enterprise
EVs
Fintech
Fundraising
Gadgets
Gaming
Google
Government & Policy
Hardware
Instagram
Layoffs
Media & Entertainment
Meta
Microsoft
Privacy
Robotics
Security
Social
Space
Startups
TikTok
Transportation
Venture
Staff
Events
Startup Battlefield
StrictlyVC
Newsletters
Podcasts
Videos
Partner Content
TechCrunch Brand Studio
Crunchboard
Contact Us
In many ways, this year will come to be remembered as the one when artificial intelligence (AI) and machine learning (ML) finally broke through the hype, delivering consumer-focused products that amazed millions of people. Generative AI, including DALL·E and ChatGPT, manifested what many people already knew: AI and ML will transform the way we connect and communicate, especially online.
This has profound repercussions, especially for startup companies looking to quickly find how to optimize and enhance customer engagement following a global pandemic that changed how consumers purchase products.
As startups navigate a uniquely disruptive season that also includes inflationary pressures, shifting economic uncertainty, and other factors, they will need to innovate to remain competitive. AI and ML may finally be capable of making that a reality.
Hyper-personalization is at the forefront of these efforts. A McKinsey & Company analysis found that 71 percent of consumers expect brands to provide personalized experiences, and three-quarters are frustrated when they don’t deliver. Currently, for example, only about half of retailers say they have the digital tools to provide a compelling customer experience.
As the industry moves ahead, consumer-facing innovators can better emphasize personalized experiences and connections by integrating AI and ML tools to engage their customers at scale.
The data that matters most
Hyper-personalization is predicated on customer data, a ubiquitous resource in today’s digital-first environment. While excessive or unhelpful customer data can clog content pipelines, the right information can power hyper-personalization at scale. This includes providing critical insights into:
Critically, this first-party data couldn’t previously be collected, aggregated, and applied to the customer experience, but AI and ML breakthroughs are finally making that possible.
AI and ML can be used to analyze customer data and make predictions about what they may want next from the brand experience. By feeding large amounts of data into an ML model, companies can train it to recognize patterns and relationships within the data that may not be immediately apparent to humans. The model then uses these patterns to make predictions about future customer behavior.
Entrepreneurial brands can use these predictions to create targeted marketing campaigns, personalized product recommendations, or tailored in-store experiences that are more likely to resonate with the customer.
Moreover, AI and ML technologies can build a customer profile and a single view of the customer to be personalized accordingly with AI-generated content in real time.
For example, a prominent oral care brand is using AI algorithms to recommend toothbrush types and other related products based on toothpaste preferences, expanding their reach into households based on how family members consume toothpaste.
Similarly, high-volume B2C companies with upselling and cross-selling opportunities for more complex products, like financial institutions, are leveraging massive customer data sets to reach new customers at scale.
As technology continues to advance, businesses are increasingly turning to these tools to gain valuable insights into customer behavior and preferences. However, it is important to approach the implementation of AI and ML with a strategic mindset and a clear understanding of their capabilities. Best practices include:
As founders navigate an unpredictable market and consumers demand more personalized experiences, AI and ML will increasingly become essential tools for innovation. By leveraging data on purchase behavior, buyer intent, survey responses, and digital engagement, businesses can gain a comprehensive understanding of their customers and use AI and ML to predict their needs and preferences.
That’s why it’s crucial for companies to prioritize hyper-personalization and stay ahead of the competition by embracing these technologies and following best practices for their implementation. By doing so, startups will be able to innovate at the pace of the customer and deliver the tailored experiences that consumers expect at light years ahead of the speed available before.
Topics
Tickets are live at the lowest rates of the year. Save up to $680 on your pass now.
Meet investors. Discover your next portfolio company. Hear from 250+ tech leaders, dive into 200+ sessions, and explore 300+ startups building what’s next. Don’t miss these one-time savings.
The first signs of burnout are coming from the people who embrace AI the most
MrBeast’s company buys Gen Z-focused fintech app Step
YouTube TV introduces cheaper bundles, including a $65/month sports package
Discord to roll out age verification next month
From Svedka to Anthropic, brands make bold plays with AI in Super Bowl ads
The backlash over OpenAI’s decision to retire GPT-4o shows how dangerous AI companions can be
© 2025 TechCrunch Media LLC.