What’s missing in the market of one strategy?


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The traditional customer segmentation approach to marketing is dying. A majority of consumers expect to receive personalized experiences, which means companies need to have a lot of data on those consumers. A decade or so ago, the only way to parse this data was to segment it into personas.

At the time, personas were a great way to understand different audience segments – what single men in their 20s bought from your company was often different than married women in their 40s. But there were limits and costs to how many personas you could create and target based on human bandwidth. Whether it was two or 12, eventually you had to aggregate similar data points into single personas. For example, even though two different dads living in San Diego with kids under 10 seem to fit the same persona, they may want to be interacted with differently and buy different products.

Artificial intelligence (AI), machine learning (ML), and large language models (LLMs) have fundamentally changed that. LLMs understand and derive meaning from language. With these capabilities, LLMs can generate new text that appears human-like and assist with various tasks like writing and conversation, code generation, validation of outputs, and information retrieval and parsing from unstructured documents. All of these applications further enhance the user experience by bringing a more natural and effortless interaction with technology resulting in efficient outcomes and positive business impact.

These AI-driven conversational agents can comprehend and respond to intricate user queries, making them ideal tools for providing customer support and improving the customer experience in a number of ways:

  • Asking and receiving questions in any language, removing language barriers, and enabling customer service for a global customer base.
  • Simultaneously handling a high volume of customer inquiries to provide quick and accurate responses.
  • Expediting training for new customer service representatives and employees.
  • Providing customer support anytime and in any time zone.
  • Reducing customer waiting times and handling a growing number of customer interactions without compromising response quality.

One-to-one journeys.

Additionally, LLMs changed the macro persona model of personalization across every application almost overnight. Today, you can quickly create content specific to a single consumer based on their individual interests and preferences which wasn’t possible before. Keeping with the marketing example, LLMs give marketers the ability to talk to a consumer directly and even account for behavior changes. As an individual’s interests or preferences change over time, LLMs can generate hyper-personalized content that takes those changes into account.

The cost to provide this one-to-one journey for an individual consumer is decreasing with the integration of LLMs. The data is there, and it can be adjusted ad infinitum. Businesses can generate five, 10, or even 100 different scenarios based on existing data. But which is the right one?

Quality over quantity.

When it comes to the market of one, LLMs can sort through millions of data points in seconds to put together potential recommendations and guidance for an individual’s journey. But are those recommendations even any good? When the individual follows one route, how does the LLM know which next step is best, rather than just what works?

This is where we need to bring ML back to the table in order to maximize the benefit of generative AI (genAI) tools like LLMs. Many organizations have side-stepped ML in order to implement LLMs more quickly, but one cannot thrive without the other. ML will follow the recommendations laid out by LLMs to see what the consumer chooses, and then it actively learns and adapts to that choice. In the era of LLMs, using proprietary data to filter LLM recommendations differentiates a company and builds moats.

Data security as a priority.

Businesses can consider moving ML models or LLMs between public and private clouds and securely integrate company and product data to enable a “market of one” strategy while ensuring proprietary data remains secure. For example, a bank or financial institution might leverage a publicly available LLM for general marketing outreach, and a completely separate LLM and hosting structure for detecting possible fraud events.

Choosing the right security posture and LLM implementation is dependent on the use case. Experts in genAI and LLMs can help mitigate compliance and security risks and ensure the communication platform leverages the best of public information with unique and proprietary customer data to enhance the personalization of the market of one strategy even further.

This specialization is how we grow from providing adequate recommendations to providing great recommendations.

Broader applications.

Again, retail marketing is just one example of genAI applications that can provide a truly individualized journey. From healthcare to banking and financial services, there is room for genAI to be customized for the consumer and provide not just what they need, but what they want, how and when they want it.

This mindset is still somewhat limited. GenAI isn’t just for consumers who need products or services. It can also be used internally in business applications to truly revolutionize how employees learn and grow. What is the opportunity in your organization to think of your employees not just by department, but in macro personas segmented into individualized preferences, areas of interest, and future growth opportunities? How would that impact your bottom line when onboarding and training employees exactly as they prefer?

Like any consumer application, businesses must be careful to avoid quantity over quality. When there are a dozen paths to take for onboarding, which one is actually good for the employee? The answer lies in genAI – the combination of LLMs and ML that don’t just produce, but refine and learn. That is the key to seeing both your employees and your business grow through AI.

What’s next?

There is a risk of ignoring LLMs as they help the business understand how to better interact in a highly targeted manner with current and prospective customers. Start leveraging them now. Security concerns are understandable and enterprise-level security should be a key consideration. Well-managed LLMs integrate securely with a company’s data. This allows an organization to protect its proprietary data to leverage the best of LLMs and the best of the company’s data.

Once in place, use your proprietary data with ML to filter LLM responses and give the right recommendation to the right person at the right time. ML is key here and will involve a lot of work to optimize and drive outcomes based on the LLM. Don’t miss an opportunity to get to your customers faster than the competition and with better, more personalized messages.

Morgan Llewellyn
Morgan Llewellyn, Stellar's chief strategy officer, envisions and implements data and AI solutions for government, healthcare, SaaS, IoT, retail, and manufacturing clients. An expert in diagnosing customer challenges and innovating unique solutions that are durable and sustainable, Llewellyn’s notable achievements include a SaaS product of the year award and a Brandon Hall AI Innovation of the Year award. He has held positions as the chief data scientist at Employ Inc., the chief operating officer of a large Midwest consulting firm, and founder of the AI-consulting company Predictive Partner.


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