This year will be remembered as the year artificial intelligence (AI) and machine learning (ML) finally took flight, delivering consumer-centric products that wowed millions of people. Generative AI, including DALL E and ChatGPT, showed what many people already knew: AI and ML will transform the way we connect and communicate, especially online.
This has profound implications, especially for startups looking to quickly figure out how to optimize and improve customer engagement in the wake of a global pandemic that changed the way consumers buy products.
As startups navigate a uniquely disruptive season that also includes inflationary pressures, changing economic uncertainty, and other factors, they will need to innovate to remain competitive. AI and ML may be able to make that a reality.
Hyper-personalization is at the forefront of these efforts. TO McKinsey The analysis found that 71 percent of consumers expect brands to provide personalized experiences, and three-quarters are frustrated when they don't deliver. For example, only about half of retailers say they have the digital tools to provide a compelling customer experience.
As the industry advances, consumer-facing innovators can better emphasize personalized experiences and connections by integrating AI and ML tools to engage their customers.
The data that matters most
Hyperpersonalization is based on customer data, a ubiquitous resource in today's digital environment. While excessive or useless customer data can clog content pipelines, the right information can drive hyper-personalization at scale. This includes providing critical information on:
- Shopping behaviour. When brands understand buying behaviors, they can provide iterative content that builds on past scenarios to drive sales.
- Purchase intent. While buyer intent only loosely correlates with buying patterns, this metric can provide context to customer trends and expectations.
- Surveys This direct data and measure customer sentiment, product impact and service effectiveness, giving companies an inside look at their service and opportunities.
- Participation in digital channels. Although the metrics of the social media can mislead businesses, other digital channel metrics such as time spent, resource downloads, and frequency of visits can provide clear direction.
Fundamentally, this data couldn't be collected, aggregated, and applied to the customer experience before, but advances in AI and ML are finally making it possible.
How AI and ML make a difference
AI and ML can be used to analyze customer data and make predictions about what they might want next from the brand experience. By feeding large amounts of data into an ML model, it can be trained to recognize patterns and relationships within the data that may not be immediately apparent to humans. After that, the model uses the patterns to make predictions about future customer behavior.
Enterprising brands can use these predictions to create targeted marketing campaigns, personalized product recommendations, or personalized in-store experiences that are more likely to enhance the customer experience.
In addition, AI and ML technologies can create a customer profile and a single view of the customer to personalize the customer based on real-time AI-generated content.
For example, a leading oral care brand is using AI algorithms to recommend types of toothbrushes and other related products based on toothpaste preferences, extending its reach into households based on how family members They consume toothpaste.
Similarly, high-volume B2C companies with up-sell and cross-sell opportunities for more complex products, such as financial institutions, are leveraging massive sets of customer data to reach new customers at scale.
Best practices for advancing AI and ML
As technology continues to advance, companies are increasingly turning to these tools to gain valuable insight 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 its capabilities. Best practices include:
- Maintain data integrity. AI algorithms can be inaccurate, so companies using this technology must be vigilant about maintaining data integrity. They must test and monitor production with human supervision. This extends to generative AI technologies, which can automate some processes, but have produced enough false and misleading information to require all brands to carefully review content before it reaches customers.
As founders navigate an unpredictable marketplace and consumers demand more personalized experiences, AI and ML will increasingly become essential tools for innovation. By leveraging data on purchasing behavior, shopper intent, survey responses, and digital engagement, companies can gain a comprehensive understanding of their customers and use AI and ML to predict their needs and preferences.
That is why it is essential that companies prioritize hyper-personalization and stay ahead of the competition by adopting these technologies and following the best practices for their implementation. By doing so, start-ups will be able to innovate at the pace of the customer and deliver the personalized experiences consumers have come to expect light years from the speed previously available.
