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Original article

Slides from SaaS Metrics Palooza 2024: The Impact of AI on SaaS Metrics

The Impact of AI on SaaS Metrics Kellogg r1.3b.pdf

• The concept of ARR is already challenged by monthly-varying pricing, e.g., usage-based pricing.
• AI will exacerbate that problem, bringing new forms of value-based pricing, e.g, unit-of-work or outcome-based pricing.
• There are two schools of thought on dealing with this: (1) split the ARR baby into baseline and variable, then analyze the baseline as if nothing has changed, and (2) spend is truth, where we substitute trailing spend for ARR. I’m in the second camp.
• AI will, gasp, require us to think about cost, something we don’t really like to do in the software business and something we’ve historically been able to kind of ignore.
• All the heavy lifting is going to move to the pricing model.

In short, to know ARR we used to read contracts. In the future, we’re going to read invoices, instead.

For internal reporting we will do a lot of pricing model analysis and examination of the base/variable split. But for external reporting, the big six SaaS metrics all depend on ARR and going forward that won’t change. We’ll use some proxy for ARR, as many quietly do already today.

Like a duck, nothing will change much on the surface, but they’ll be a lot of activity underneath. And the metrics won’t mean quite the same thing as they once did. For example, ARR and NRR will become less forward looking and work less well as leading indicators.

Key takeaway

• For entrepreneurs: The integration of AI into SaaS models necessitates a shift in pricing strategies, emphasizing value-based and outcome-driven pricing, which can significantly impact revenue forecasting and customer acquisition strategies.
• For investors: The traditional ARR metric is becoming less reliable due to AI-driven variability in pricing, making it crucial to focus on alternative metrics and understand the cost implications of AI integration in SaaS businesses.

Summary

The article "The Impact of AI on SaaS Metrics" by Dave Kellogg discusses how AI is transforming SaaS pricing models and challenging traditional metrics like Annual Recurring Revenue (ARR). AI introduces new forms of value-based pricing, such as unit-of-work or outcome-based pricing, which complicate the use of ARR. Two approaches are proposed to deal with this change: splitting ARR into baseline and variable components or using trailing spend as a proxy for ARR. Additionally, AI requires considering costs, which were previously often ignored in the software business. The heavy lifting in SaaS metrics will shift to pricing models, making internal pricing analysis crucial while external reporting will continue to rely on proxies for ARR.

Insights

• ARR Challenges: Monthly-varying pricing, especially usage-based pricing, already challenges the concept of ARR. AI exacerbates this problem by introducing new pricing models.