Why Consumption Pricing Makes Forecasting Harder with Devavrat Shah

[MUSIC] Welcome to the Revenue Builders Podcast with John McMahon and John Kaplan. This podcast is brought to you by Force Management. Today, let's revisit a segment for my episode with DevShop. Dev is an MIT professor, the director of their statistics and data center, and the co-founder and CEO of Iki Guy Labs. He knows what he's talking about. In this segment, we talk about using AI to help with consumption pricing models and forecasting. What are some of the ideas, understandings, challenges you think are presented right now with consumption pricing and what could AI help with in the future on that? Fantastic. So I think there are two parts. One is about consumption pricing as a concept. And again, I must confess here is that my expertise there are limited. But let me contextualize in the context of AI itself because lots of modern AI pricing is around this word called token-based API calls very much along the lines of consumption. And that's where one is trying to think through. How does one think about AI as a consumption based product rather than AI as a traditional, I'd say, value based product or traditional SaaS pricing, which is licensing, proceed, etc., etc., right? Now, I think it's a great framework because one way I like to think about, we like to think about, in my role as a leader of Kampdisi Ikkikaya, is that we think of data as a massive data queue. That's how world of data warehouses, world of databases have been thinking about it. I'll check different axes you have, tables and all of that. So you can massive queue for us to check all time cities, different types of data, how much time in history, what is the future forecast? And each of the forecast and data point is like prediction query and all that. That provides a volume of work you are doing, volume of compute you are doing. That's how sort of, let's say, entire cloud as a compute has been sort of structure. And what that has done well in my mind successfully is allowed people to understand what is it that they're getting into? They can sort of actually just compute. There is a very honest exchange that's there. Now, as that honest exchange happens in terms of overall consumption, it's relatively easier for people to forecast what their future revenues would look like in that consumption model from sitting from a CFO's outpace. However, if you are selling those deals as an enterprise sales, as a sales rep in a small region, in a small location or small part, small channel, then forecasting those things becomes hard because here is a, you know, you are forecasting on one hand, take a smooth water. On the other hand, you're forecasting when umbrella is purchased. It's like a two very different type of forecasting problems. And this shows up in many, many different places. Think of manufacturing. I'm manufacturing, let's say, a material, okay? That material I need to manufacture because when my customers come, every customer comes and places one ton of order, once every quarter or so. There are a few customers choppy orders, but that collectively forms into 27 tons of production, this quarter and 28 tons of order next quarter, but who I don't know. So the mechanics are exactly like that. Easy to forecast overall volume, very, very difficult to forecast where and how, but they are all related. And this is where AI can really help. Because if you look at these things in isolation, it will be very hard, but if we start looking at, let's say, sales reps as a cohorts, now we can learn from them each other. Okay? So here are the one type of sales rep. Here is another type of sales rep. Here is one type of channel. There's another type of channels. And now we can actually start correcting in addition to data that you have in MetPec or whatnot. So I think there is a huge opportunity to actually empower organizations to work, how to say this, with more trust rather than more finger pointing if I may call it, with the help of AI. And I think that would be a good thing for everyone in work. Johnny, what are your thoughts on this? I know a lot of questions. Well, I mean, it's a lot. Yeah, AI could definitely help. Because there's a lot of parameters that go involved. There's a historical analysis of consumption by her customer. There's seasonality overall. There's seasonality of each individual customer and their business. There's the rep forecasts. There's new bookings that are occurring. There's the timing of the new bookings. So there's so many different factors that go in. And to Dev's point, AI could help by the aggregation of all of that data in order to give you, as you said before, almost the answer predicting what the answer is going to be, which wears right now, that's pretty hard task to do. I was going to say, I almost feel like it's not possible without AI to effectively forecast a consumption business. I don't know how you would do that. Yeah, I would absolutely agree with this. Thanks for listening to today's episode. If you enjoyed the content, please subscribe. Rate and review the show to help us reach more people. This show is brought to you by Force Management, where we help companies improve sales performance, executing the growth strategy at the point of sale. Check out Forcemanagement.com for more information.