Incremental Revenue Forecasting: Turning $2 Million Into $6.9 Million

We are doing a lot of counting at LiftLab, and one of the lists that we make is, there are two ways to understand diminishing returns. We do that through models, and we do that through diminishing return experiments. When we have understood all these diminishing returns across the media plan, there are really three use cases to enable marketers. The first one is, use this diminishing returns understanding to know, for the spend that I put in market last week, how did it perform. Everyone wants to answer that question, it’s human nature. The second one is, if I spend more money, how much more revenue would that drive, right, as I move along a curve, can I actually see that. And the third one is, move me around all these curves on your own. We think of that as optimization, and we’re using that to drive a forecast. So we’re going to look at all three use cases: how did I perform, what would happen if I spent more, and you tell me how much I can spend and tell me what that would do in terms of forecasting my incremental revenue.

I’m excited to share with you the forecasting portion of LiftLab. It’s one of the use cases of, when we understand diminishing returns, what can we use it for, and it answers a couple of the questions that are asked over and over again by marketing teams. For a given budget, what’s the most amount of incremental revenue that I can drive, and then the opposite: with a certain revenue target, how much budget do I need to support that. Let’s go inside LiftLab and take a look.

We’re in the analyze section of LiftLab, and we’re welcomed by the ROI dashboard, and you can see that in one of our other videos. We’re going to go straight into forecasting and click on a new analysis, and these are the two questions that we just shared. The one that is used most commonly by LiftLab customers is the budget constraint question. We’re going to call this budget constraint demo. It could be on all of our products, or potentially on one of our product categories; we’re going to go ahead and use all of them today. The budget constraint, we’re going to have other constraints that we’re going to see in just a moment, but that’s the area that we’re solving for, and it’s going to maximize our revenue. How much revenue can we drive with a specific amount of marketing spend, and we’re going to go do this for some point in the future. Let’s do it for later in 2024, and maybe we’re doing it for a whole quarter. We can use the most recent data that’s loaded, or more commonly we would say the same period from the prior year. It’s going to bring back our media plan, what we spent in that training period, and how much revenue it drove. We get to say which of the channels we’re going to optimize; I might take out a channel, maybe one that’s planned way in advance. And what’s nice about this is it’s going to go ahead and run a default scenario, so we’re going to see, all things equal, looking at 2023, how should we deploy that capital in 2024. The changes it’s going to make: if there are areas where we overspent, let’s free that budget up, and if there are areas where we underspent, let’s bring budget to that area, that opportunity.

What we’re presented with here is, we have a pretty good score that this ran; it ran well, and we don’t have any concerns about it. It turns out that there were some sections, with the default constraints, that were maybe overspent in the prior year, so we could actually increase our ROAS and increase our profit by spending slightly less. Here we’re going to move from a ROAS of 3.3 to a ROAS closer to 3.5, and doing that with less budget. Let’s see the specific breakdown here. We have channels that are going to be recipients, in green, they’re getting more budget at the spend, and we have channels that are going to be receiving less budget. I like to call this sometimes, take from the poor, or poor performing, and give to the rich, those that are performing better. The headline here is, with less budget, a decrease in budget about $96,000, we’re going to forego some revenue, about $45,000. Why would we do that, why would that math work out? Well, this revenue is our top line, but we have a profit margin, and here the profit margin is set to 50%, so if we were to forgo that much contribution to profit minus the savings, we’re going to increase profitability of the firm.

Inside, you can see here where we had this increase of 3.7%. Where did we do that? The podcast, it turns out, could see increases in spend. We were underspent here, at $2,800 and $2,700, we were slightly overspent in the third month, and so that’s going to be corrected. We’re going to spend more where we had left money on the table, and we’re going to spend less, so we’re even reallocating within the channel, and here we get a net new budget for podcast of $419, which can drive $1,700. I said that these were the default constraints; we could rerun this now. What if, for those $2,800 months on podcast, what if we were to actually give it even more budget? We could do that specifically for just one channel, like the podcast channel or the non-brand channel, or we can do it for all of them. Let’s increase this to 40%, apply the selections, and rerun the scenario. Now we have some of the channels, instead of stopping at 50%, increases are willing to go closer to the 40% that we allowed it. Now some are not making it all the way to 40%, it depends on what the economics are, and now we see quite a different solution. Instead of decreasing the budget by closer to $100,000, we’re only decreasing it overall total by $22,000. But even with less budget, by reallocating to stronger performing channels and reallocating within channels to the strongest performing months, if we had underspent in a month, like correcting that, we’re able to drive another $122,000 in revenue.

This is a really good story: we’re working with nearly the same budget, actually slightly less budget, and we’re getting much more yield out of it. This is what we mean by optimizing. This new forecast is saying, with my new budget of $2 million, I should be able to drive almost $7 million, $6.9 million, and we can see, month by month, how we’re going to deploy that capital and what our new target budgets are going to be. We’d like to ask prospects, do you think that you’re 90, 95, or 99% optimized, and I always get a smile from those prospects. No, we don’t think we’re 99% optimized, and that’s to be expected. What that translates to is, you will see similar phenomena in your media plan; we don’t think that you’re fully optimized, so you’re very likely, just like in this example, to find places where you should be spending less and places where you should be spending more. That’s what we should all be getting excited about, is redeploying the capital and seeing these increases in revenue with almost the exact same resources.