Revenue Builders

Using AI in Consumption Pricing Models

Jan 5, 2025

Artificial intelligence is transforming consumption-based pricing models, enabling more accurate revenue forecasting. Experts discuss the challenges of predicting localized demand while balancing macro trends. AI empowers sales teams by analyzing data as a predictive framework, fostering trust within organizations. The conversation highlights how businesses can leverage AI to aggregate complex data and improve decision-making, offering a competitive edge in the market.

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Quick takeaways

AI revolutionizes consumption pricing models by enabling accurate forecasting through the analysis of diverse customer behaviors and historical data patterns.

Implementing AI in revenue prediction fosters trust among sales teams by transforming reactive decision-making into a proactive strategy, enhancing overall performance.

Deep dives

The Role of AI in Consumption Pricing Models

AI can transform the way consumption pricing models are structured, moving away from traditional value-based pricing or standard licensing practices. By conceptualizing AI as a consumption-based product, it allows for a clearer understanding of the data and computing resources involved. This framework presents an honest exchange where businesses can better forecast their future revenues based on their consumption patterns. However, in enterprise sales, forecasting becomes more complex due to variable customer behaviors, making AI's role essential in managing this unpredictability.

Harnessing AI for Enhanced Consumption Pricing Forecasts

6min

In this short segment of the Revenue Builders Podcast, John McMahon and John Kaplan are joined by Devavret Shah, an MIT professor, data expert, and CEO of Ikigai Labs, to explore how AI is revolutionizing consumption-based pricing models and forecasting. Devavret delves into the challenges of predicting demand, the role of AI in empowering sales teams and CFOs, and how artificial intelligence can enhance trust and accuracy in revenue prediction. This engaging discussion also highlights how businesses can leverage AI to enable seamless decision-making and gain a competitive edge.

KEY TAKEAWAYS

[00:00:28] Understanding Consumption-Based Pricing Models: Consumption models focus on token-based API calls, offering a modern alternative to traditional SaaS pricing.
[00:01:58] Data as a Predictive Framework: Viewing data as a massive queue allows organizations to forecast compute volumes and future revenues with more accuracy.
[00:03:23] Challenges in Forecasting: Smooth macro-forecasting often clashes with micro-forecasting, such as predicting localized or choppy demand.
[00:04:14]AI's Role in Cohort Analysis: Leveraging AI to analyze sales reps and channels as cohorts improves prediction accuracy and fosters trust within organizations.
[00:05:16] Aggregating Complex Parameters: AI simplifies the aggregation of historical, seasonal, and booking data to deliver actionable insights for consumption forecasting.

QUOTES

[00:01:58] "Each forecasted data point is like a prediction query—it shows the volume of compute you're doing."
[00:03:23] "It's like forecasting smooth water versus forecasting when umbrellas are purchased—two very different problems."
[00:04:36] "AI helps organizations work with more trust, rather than more finger-pointing."
[00:05:39] "I almost feel like it's not possible without AI to effectively forecast the consumption business."

Listen to the full conversation with Devavret Shah through the link below.

https://revenue-builders.simplecast.com/episodes/cutting-through-the-noise-understanding-ai-through-history-and-practical-application

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