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3 minutes reading time (606 words)

AI Treasure House - What is Latent Effects Modeling

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Let’s take a geek’s-eye look around the AI treasure house.  Oh, here’s a cool tool - Latent Effects Modeling.  Latent variables, as opposed to observable variables, are variables that are not directly observed but are rather inferred (through a mathematical model) from other variables that are observed (directly measured).  In short, Latent Effects Modeling takes what you can see and directly measure and infers (predicts) the variables you can’t see or measure. Stuff like trends for example.

How Latent Effects Modeling Could Help a Business

Imagine a box ⎯ the top, bottom, and each of four sides are datasets, all of them interacting with each other at full gallop.

Now imagine a company in the fishing industry – Learnington Inc. In this case, one dataset is about the weather, another is about Learnington’s fishing-based revenues, others are about how big ships are traveling through their fishing grounds and their effects on various fisheries – and therefore revenues, or not … toss your own variables and push the button to start the pattern matching and factoring in the variables.

Complex? Yes, but the model crunches away … and comes up with trends and predictions that reveal the unseen trends and variables and how they may play out if the data remains the same.

Latent Effect Modeling is in play as we speak, generating some of the most widely used predictive models of the COVID-19 spread, as well as looking for trends and patterns in marketing data.

People are using it for all sorts of predictive analytics modeling:

  1. Which of several digital campaigns is most likely to produce positive long-term memory of your brand?
  2. Which presidential candidate produces the least revulsion/disgust in voters?
  3. What range of prices is most likely to produce the greatest sales without losing you money?
  4. Which of several email messages is most likely to produce the action you wanted from consumers (e.g., try a new product)?

Don't Just Use Ai to Reduce Costs.  Use it to Increase Revenues

In his “disruptive” book, The Innovators Dilemma, Harvard Business School Professor Clayton Christensen talks about “Jobs to be done”.  The Jobs that most companies have been asking AI to do thus far have been to either reduce costs or measure efficiency – with the by-product being increased profit.

While these less-advanced organizations focus their AI initiatives on cost reduction, more-advanced companies are seeing revenue increases from AI, indicating a shift to more strategic — and customer-centric — AI deployments. It’s time to think of AI as a way to directly increase revenue.

Two quick real-world examples of AI techniques, like Latent Effects Modeling, are making their way into the marketing and healthcare worlds. 

  1. Utilizing that mound of data that is created every time you launch a campaign to ultimately predict trends in brand loyalty, eCommerce transactions, and lead generation. Some companies are seeing increases in these types of transactions by using Latent Effects Modeling to understand the trends & patterns in their data and customer experiences. Trends that no human analysis alone could find.
  1. The current COVID 19 pandemic is probably the best example of data and patterns that are crucial to understand – but have several crucial variables. The data and the datasets are so big that looking for trends or patterns and factoring in different variables becomes humanly impossible. Christopher Murray at the University of Washington's Institute of Health Metrics Evaluation is using Latent Effects Modeling to help the world understand the impacts of many trends and scenarios. https://covid19.healthdata.org/projections 

The best way to really get your mind wrapped around Latent Effects Modeling and other AI power tools is to consult with those of us who are using them

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