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Entity Language Models: Monetizing Language Models – Part 2

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We must move beyond just taming…to monetizing Language Models!

In part 1 of this series on Small Language Models (“Use Case Language Models: Taming the LLM Beast – Part 1”), I explored the business and operational value of Use Case-specific Small Language Models (Use Case Language Models).

Use case language models are trained or adapted to excel in solving specific, cross-domain problems by integrating and analyzing pertinent data from various data sources to deliver comprehensive and actionable responses, such as improving customer retention, increasing sales revenue, or optimizing inventory management.

In part 2, I want to move beyond just taming language models (with Use Case language models) into monetizing language models (with Business Entity language models).  I want to show how we can build Entity language models on a use case-by-use case basis (instead of the very expensive, overly complex, “big bang” Large Language Model approach) and unleash the economic benefits of entity language models.  Let me explain further.

Economics of Entity Language Models

Business Entities are the physical entities (human and device/equipment) around which organizations seek to uncover or quantify analytic insights (predictive propensities).

Business entities are the main actors or objects involved in a business problem or initiative. They can be human, such as customers, employees, patients, doctors, students, and professors, or devices/equipment, such as cars, trains, airplanes, compressors, motors, and chillers. Business entities have attributes, behaviors, tendencies, inclinations, and relationships around which we can create analytic scores to measure and predict behavioral and performance propensities. These analytic scores are then used to optimize the decisions and actions supporting the organization’s critical use cases.

We can expand upon the concept of business entities by creating business entity-centric small language models (entity language models) to support the use of business entities to optimize the organization’s critical use cases.

Entity language models are language models trained on a specific business entity. They can capture the characteristics, needs, and propensities of the entity and produce natural language outputs that are relevant and useful in the assessment and analysis of those business entities.

Some of the benefits of an Entity language model are:

  • It can improve the relevance and quality of natural language outputs by focusing on the specific characteristics and needs of the business entity.
  • It can reduce the cost and complexity of developing and maintaining language models by reusing and sharing data and knowledge across different use cases and domains that involve the same business entity.
  • It can enhance the value and impact of analytics by generating natural language outputs associated with the characteristics and propensities of the entity.
  • Finally, we can build Entity language models on a use case-by-use case basis and avoid the “Big Bang” approach of Large Language Models (LLMs).

For example, if we want to build a Customer Entity language model, we could start with a “Customer Retention” use case as the foundation for our Customer Entity language model. We would then build out the Customer Entity language model use case-by-use case with additional customer-centric use cases such as Customer Cross-sell, Customer satisfaction, Customer Lifetime Value, Customer Referrals, etc. (see Table 1).

Data SetCustomer RetentionCustomer Cross/Up-SellCustomer SatisfactionCustomer Lifetime ValueCustomer Referrals
Sales transactions (orders)✔️✔️✔️✔️✔️
Customer support✔️✔️✔️✔️✔️
Customer demographics✔️✔️✔️✔️✔️
Product returns✔️✔️✔️✔️✔️
Payments✔️✔️✔️✔️✔️
Social Media comments✔️✔️✔️✔️✔️
Marketing campaigns✔️
Salesforce notes✔️✔️✔️
Product specifications✔️✔️

Table 1:  Customer Business Entity Language Model

The data sets that we have already analyzed and integrated into the Customer Entity language model to support the customer retention use case are now available for other customer-centric use cases at little or no marginal cost. We would simply add additional data sources (e.g., marketing campaigns, sales force notes, product specifications, local economics) to the Customer Entity language model as they are needed to support additional customer use cases.

The power of entity language models is that they can support multiple use cases at little to no marginal cost, changing the economics of AI application development and value appreciation.

But wait, there is more! An Entity language model can also be blended with other entity language models to address more complex use cases.

Cross-entity Use Cases

Entity language models can be blended with other entity language models to address more complex, cross-domain business and operational challenges and opportunities.

Integrating or blending entity language models can have significant benefits, including:

  • Reduce the cost of developing and maintaining multiple language models for different use cases and domains by reusing and sharing data and knowledge across them.
  • Increase revenues and profits by creating more comprehensive solutions that address the business needs more effectively and efficiently.
  • Enhance organizations’ competitive advantage and innovation by enabling business users to tackle more challenging opportunities that require cross-domain expertise and creativity.

Examples of cross-entity language model use cases include:

  • Marketing campaign effectiveness: This application blends the campaign and customer entities to generate an analytic score for each customer that measures the campaign’s effectiveness. The score drives recommendations for the campaign design, execution, and evaluation.
  • Medical treatment effectiveness: This application blends the patient and medical treatment entities to generate an analytic score for each patient that measures the treatment’s effectiveness. The score drives recommendations for the treatment selection, delivery, and outcome.
  • Maintenance effectiveness: This application blends the device and technician entities to generate an analytic score for each device or technician that measures the maintenance’s effectiveness. The score drives recommendations for maintenance scheduling, execution, and evaluation.

Financial Services Example

Let’s say that we want to explore the use of Entity Language models in the financial services industry.  Let’s start with some entity language models for the financial services industry:

  • Account entity: This entity represents a customer’s account with a financial institution. The account entity can have attributes such as account type, balance, transactions, fees, interest rate, rewards, etc. The account entity can be used to optimize use cases such as account management, fraud detection, customer service, etc.
  • Customer entity: This entity represents a customer of a financial institution. The customer entity can have attributes such as demographics, income, assets, liabilities, credit score, preferences, behavior, etc. The customer entity can be used to optimize use cases such as customer segmentation, customer retention, customer acquisition, customer up/cross-sell, etc.
  • Product entity: This entity represents a financial product or service a financial institution offers. The product entity can have attributes such as product name, category, features, benefits, costs, risks, eligibility criteria, etc. The product entity can be used to optimize use cases such as product development, product recommendation, product pricing, product performance analysis, etc.

Now, let’s explore some financial services use cases that require blending different entity language models, such as:

  • Loan approval blends customer and product entities to generate an analytic score that measures the likelihood of loan repayment and default. The analytic score can then be used to drive recommendations that optimize the loan approval decision and the loan terms.
  • Insurance claim processing blends customer and product entities to generate an analytic score that measures the validity and severity of the claim. The analytic score can then be used to drive recommendations that optimize the claim processing speed and accuracy.
  • Investment portfolio optimization blends customer and product entities to generate an analytic score that measures each investment product’s expected return and volatility. The analytic score can then be used to drive recommendations that optimize the portfolio allocation and rebalancing.
  • Credit card fraud detection blends account and customer entities to generate an analytic score that measures the likelihood of fraud for each transaction. The analytic score can then be used to drive recommendations that optimize fraud detection and prevention actions, such as alerting the customer, blocking the transaction, or issuing a new card.
  • Customer churn prediction blends customer and account entities to generate an analytic score that measures the churn propensity for each customer. The analytic score can then be used to drive recommendations that optimize customer retention and loyalty strategies, such as offering incentives, discounts, or personalized services.
  • Financial risk management blends product and account entities to generate an analytic score that measures the risk level of each product or account. The analytic score can then be used to drive recommendations that optimize the risk management actions, such as adjusting the capital requirements, diversifying the portfolio, or hedging the positions.

The potential of cross-entity language models to support more complex business and operational use cases is only limited by your creativity (and an understanding of your organization’s key use cases).

Summary:

Maybe what excites me most about entity language models is their role in my “Thinking Like a Data Scientist” (TLADS) methodology. Step 3 of the TLADS methodology – Understand Business Entities” – identifies and assesses the most important entities for addressing your targeted business initiative (Figure 3).

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Figure 3: Thinking Like a Data Scientist Methodology

We can expand TLADS Step 3 to include the design, development, and management of entity language models that help our organization become more effective at leveraging data and analytics to power our business and operational models.

“I love it when a plan comes together.” – Hannibal Smith, The A-Team