Let’s set aside for the moment numbers two and three above, and look at the question of how much more you have to spend.
What if you had the ability to say to the customer: “Let’s not talk, right now, about ‘MORE.’ Instead, let’s look at our upcoming programs, at a simulation of how we’re going to achieve a mutually beneficial result” –- the win-win to which we’ve all been giving lip service. “How are we going to reallocate our money in order to accomplish our mutual goals?”
Now, what have you just accomplished? You have sidestepped getting caught up in: “Well, I’ve got 10 percent more for you.” You have avoided an argument about whether that number is or isn’t enough. Instead, you’re now saying, “We’re going to do this, this and this, and here’s the payoff.” You’re able to simulate each of those events, one at a time. You’re able to roll them all up, showing the customer what and how they will generate “X” for him for the year.
That is, as long as there is a reasonable level of confidence that the models are correct and that they use good data from a mutually acceptable source, you’re well into discussing “What” rather than “How much?”
That’s a big improvement over what so often happens today –– which is that as soon as you say: “I don’t have more to spend,” an argument begins or the meeting ends.
A second advantage is operational efficiencies.
One company we know actually has some algorithms running in connection with its trade promotion management solution. Now, this happens to be a German company, with some rather demanding system performance standards. What they tell us is: “We are now predicting, with a relatively high degree of certainty, what our outcomes will be. We’re finding we’re able to drive operational efficiencies in the supply chain, as well as understand our spend, because our predictive models are so accurate.”
That’s despite things like weather patterns and other variables.
We know of other cases where companies are telling us: “It’s surprising how good these solutions are.” Do they say: “This is the absolute price point?” Maybe. Or maybe they make a suggestion, one to try and then modify.
In short, while the model may not be perfect, it does remove much of the uncertainty around the decision-making process, and user companies maintain it is driving efficiency.
Now, before Alex comes through with another right hook and knocks me out, I have to concede it’s not absolute. What I’m hoping the modeling process will do –– and I think the German user company would agree –– is narrow down your options. That is, where you might think you have six choices, the model will show you there are really only two good options. Then, yes, it will take some human “gray matter” to figure out which of the two is likeliest to produce the greatest return.
For example, maybe you’re talking with a retailer who has never been open to certain kinds of merchandising activities. You can come in and say, “This year, we’re going with longer sections, more segregated promotions, and here’s what that will do for you…”
ALEX RING: I’m glad you’re talking, Dale, about two choices instead of definitive answers. Think again about that little gauge in the car that tells you how many miles to empty. We’ve all experienced this: We let the gas run down, and the gauge tells us we have 45 miles to empty. Then, a minute later: Oh, no! — now it says 34 miles! Another minute later, it says 64! It’s bouncing around! What’s going on?
Well, the reading is only an estimate. Depending on how you drive, the system continuously recalculates. That is, even with a sophisticated computer chip doing the math, you don’t get a precise answer. Why? Because your driving behavior changes –- or you hit a bump, and the float in the gauge changes position.
So we have to be careful not to have unrealistic expectations. The fact is, too often we’re looking for an “Easy” button. We just want to hit that button, and have the system answer our question: “Do I do a TPR or a BOGO?” “Do I promote this week
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