# How to Price Your Products and Services

#### Transcript

Alrighty guys. So in this video, we are going to explain price. Okay. So this is a really important concept to understand we're going to walk through this training and and give you a lens to use when you are determining a price for something. Okay. So I'm going to jump into the pricing methods. Okay. And the pricing explain. So let's talk about price. Okay. So the price is the amount of money the customer is willing to pay for the transformation, obviously. Okay. Now it is a function of the value created. Okay. So the value of the transformation. So how much, how much value, what is the, what is the problem you're solving for that person? Remember the man on that Island who is starving and wants to go see his family, that guy's willing to pay whatever he has to, to get to that Island too.

So it's the, it's a function of the value of the transformation. The amount of certainty the customer can attribute to the outcome of that transformation. Okay. This is really important. So if it's not that certain, well, they're not going to pay a lot. They're not going to pay a lot, right. The competitors in the markets. So if the competitors are offering we'll get to, what's called the expected value. The competitors will determine your price and also the ability of the customer to afford the solution. So if the guy on the Island didn't have any money, he didn't have two nickles. Well, he's not gonna be able to pay anything. So you have to be, you have to be aware of the customer's ability to afford the solution. Okay. So it can be kind of summed up in the price as it is a function of the transformation, the certainty of that transformation happening that competitors in the market and the ability of the customer to afford the solution.

Okay. So let's first explain what the expected value is. So if you go back to school, if you remember your statistics, you might have learned the expected value formula. Okay. So the expected value accounts for all possible outcomes and the respect of values, a and P and probabilities. Okay. So where N is the number of possible outcomes. Okay. Expected value. So E X is the expected value of all the outcomes. X I, as the as the outcome value of the event. I and P of X is a probability of X I happening. So that's just like a, that's kind of math language, but let's give you a real world example. Okay. And this is actually not very hard to understand. If you don't remember your probability, I'm going to give you a quick crash course in this expected value stuff. Okay. And we're going to be using an example of a, a roulette wheel.

Okay. So let's let's pretend we have a roulette wheel. Okay. So have you ever went to the casino and you played this roulette, you will notice that there are a 38. So if we have a roulette wheel, there are 38 possible slots the ball can fall into. Okay. So there's 38 possible slots. Okay. So those are the possible outcomes. Okay. And if it lands on, let's say you're playing with a, a dollar. Okay. You're playing with \$1 and if it lands on a black, so you're betting on black. Okay. The outcome is \$2. Okay. But if it lands on red, okay. The outcome is zero. Okay. Cause you're betting on a you're betting a dollar, so you're gonna lose your dollar. And if it lands on green, then you're going to lose your dollar. Okay. So, and out of the 38 slots, there are two greens.

Okay. And that leaves 38. So 36 over two, that leaves 18. Woops, that leaves 18 black slots and 18 to red slots and two green slots. Okay. So let's calculate the expected value of this roulette wheel and why you probably shouldn't be going to the roulette table because you're going to give your money over to the casino and why casinos make so much freaking money. Okay. So we have let's calculate this expected value. Okay. So we're going to take, we're going to calculate, calculate the expected value of this situation, where X is the outcome value and P of X is the probability of that outcome happening. And then we're going to sum them up to determine the expected value. So what we're going to do okay. Is, so let's just put this formula here is the sum of X times P X I. Okay.

So what we're going to do is we are going to take the first outcome. So that let's say this is the first outcome. It lands on black, right? There's only three outcomes that can happen. And unless the ball falls off the table, there's only three outcomes that can happen. So it lands on black, in which case the outcome value is \$2. Okay. And the probability of that happening is, well, there's 18 slots, black slots, and there's 38 slots on the table. So the probability of that happening is 18 over 38. Okay. So that's the first outcome. The second outcome that can happen is lands on red. Okay. So we're going to add, because we're summing up these probabilities here the second outcome is red and in that case, we're going to actually lose our money. So the outcome value is zero. And the probability of that happening is the same as black.

So it's 18 over 38. Okay. And then we have green two. So there's two green slots. And if we land on green, we're actually going to make zero. Okay. And this is where the casino makes their money. If the casino didn't have these green slots, then they wouldn't make their money. So if it lands on green the probability of Atlanta on green is two slots out of 38. Okay. So if we did the math here, the zero times, anything, it just cancels out. Right? So the probability of this happening is two times 18 divided by 38. So let's take a look at that. So two times 18, over 38 95, we'll call it 95 cents. Okay. 95 cents. Okay. So if you take a look at this, if we play the roulette table with the dollar, the expected value is 95 cents. Okay. So if we play this game a thousand times, K with a dollar, we're going to walk away with \$950.

That's what the expected value. The probability tells us that we are going to walk away with 95 \$950. So every time we roll the dice here, or sorry, we spin the wheel, we lose 5 cents. So this is where the casino makes their money. Okay. So every time you spin that wheel and play the game, you're actually, if you're playing with the dollar, you're losing 5 cents over time. Okay. So that's essentially what the expected value is. And this is how to think this is how decision makers think of making decisions. They're trying to account for the outcome value. Okay. And the probability of that outcome happening. Okay. And so all humans, they have this baked into their head through evolution. They've been very good at determining and simulating outcomes at assigning probabilities to those outcomes, whether they're doing it consciously or unconsciously, they're really good at this.

This is actually how they make decisions. Okay. So that's basically what the expected value is. So how does this apply to pricing our products? Okay. So the expected value is used to justify the investment. Okay. So if the expected value is greater than the cost of the investment, then a rational buyer will decide to close. Okay. So I'll give you an example, economics, economic buyers love the way of justifying the purchase using the expected value because it shows the shows, the buyer that you did your homework, and you understand you understand the situation sometimes even better than they understand the situation. And if you can't clearly map out the ROI with a few customers to determine the actual ROI and the impact of your solution, you can use discovery calls to figure this out. So, first of all, let's, let's just get into the expected value.

Okay. And how this applies to your business. And we're going to give an example of our expected value. Okay. So let's pretend we have a customer who is already at 10,000 monthly recurring revenue. Okay. And we are going to be calculating the expected value of them working with us. So this is the outcome value. So we could say the, one of the outcomes, if you're already at 10,000 monthly recurring revenue, the the increase in monthly recurring revenue over a 12 month period could be one of the outcomes could be a hundred thousand dollars. So if they're already at 10 K, then they can get to 110 K. And that's the, the increase in monthly recurring revenue. Okay. So we're going to calculate that expected of that outcome value. Okay. And not only in our case, if a company is able to increase their monthly recurring revenue by a hundred thousand dollars, well, because the way that investors are valuing SAS businesses, it, they often value it at the SAS businesses at anywhere from four to 10 X annual recurring revenue.

Okay. So this is the equity Delta that is created. So if we create a hundred thousand dollars a month of recurring revenue and investors are using a six X multiple on the annual occurring revenue, then the equity Delta is seven point \$2 million. Okay. So based on the statistics, we can assign a probability of that outcome happening. And so in our historical data, we can say that 30% of the customers who are at 10 K will add an additional hundred K. Okay. So we have this data and it's, it's a, it's pretty solid. Another 40% of customers will add an additional 30 K monthly recurring revenue. Okay. 10% of customers will add an additional 10,000. Okay. And then 10% of customers will actually not make any progress. They run out of money, they give up something happens and that outcome is negative. So there we're actually accounting for the negative.

Remember we have, instead of spending the roulette wheel, instead of having three outcomes, there's actually multiple outcomes that can happen. And we're just quantifying those outcomes. Okay. And then there's a 3% chance that they're going to add an extra 250,000 a month. Okay. Because we have customers who've done that. And really there's a 0% chance that they're gonna do an extra 500,000 a month. We haven't had that happen. So there's, it's not, it's not very likely that that's going to happen inside of 12 months. So what we're doing is we're taking these, these values, these outcome values. Remember it's the, it's this value right here. Okay. And we're assigning the probability. Okay. And then we're coming up with the expected value here. Okay. So we're basically just multiplying the outcome times of probability and we're coming up with that expected value. And then we are summing up those summing up those those products here and we're coming up with an overall expected value.

Okay. So that overall expected value can be compared to the cost of investing. Okay. So if we come up, if you work with us, this is the over, if you're already at 10,000 monthly recurring revenue, this is the expected value of working with us. Now we have to compare that to the cost. So if the cost is significantly if it, if it's greater than this expected value, then it doesn't make sense to do it. So like, if the expected value is 3.6 million, but it costs 4 million we'll, then it doesn't make sense to do it. If it only costs 300,000 to do that. So you spent 300,000 and you make 3.6 million. Well, all of a sudden that makes a ton of sense. So instead of playing this silly roulette game, where every time you spend the the wheel, you're losing a 5 cents, if you're playing with a dollar, well this roulette table is a little bit different.

This favors the customer a little differently. Okay. So this instead of losing money, every time they spend let's say they cost them 345,000 cars to get to that to get to that level of of scale. It's going to be about three staff members, and it's going to cost me about \$300,000. If they want to add an extra a hundred thousand to their monthly recurring revenue they're going to be spending 300,000. They're going to be making 3.6. So that's an actual, that's 10 X. Okay. So they spend 300,000, then they get a 10 X return. Okay. So that's an example of calculating the expected value. And if customers understand all the outcomes, so the job of the entrepreneur is in the sales person is to map out all the outcomes, assign probability to them, and then come up with the expected value and then compare the expected value to the cost and see if that makes sense to the customer.

Okay. That is really the key. You can, you can be the worst salesperson in the world, but have a really tight, expected value calculator. And all of your assumptions are backed up. Okay. So essentially your assumptions need to be backed up with third party data or, or first or firsthand data. Like if you did a survey with your customers or you did, you figured out the probabilities on your own, then then you're still going to be able to close. Okay. And you can calculate an ROI. So let's go back to our documents. So economic, economic buyers love this way of justifying a purchase decision, because it shows that you did the work of mapping out the business case for them. Okay. And it also shows that you understand the business just as well as they do, if not better. And if you can't clearly map out an ROI, you're going to have to interview your customers.

Okay. You're gonna have to interview your customers and also do some some research to determine that expected value. Okay. And again, as long as these assumptions are backed up by credible data, you can lean on them to form an economic case and justify your expected value. Okay. So with B2B, like high ticket things where, you know, you're in the order of millions of dollars this is, this becomes really important with lower ticket things and consumer products. This is still really important because humans make decisions the same way, but explaining it this way, it might be a little bit overkill, but if you do you're, you're going to be able to close. Okay. So let's give an example. So you are promising to create \$1 million in gross contribution. Okay. And you're assigning a 25% chance that you can do this. Okay.

So if the customer aligns with this outcome value and the probability, then the expected value of that transformation is 250 K. So if you're promising to make someone a million dollars, and you're saying there's a 25% chance that we're going to do that, then the expected value is 250 K if you only charge 25 K okay. Then the customer is experiencing a 10 X return. Okay. That's one 10th of the expected value. Then the customer is guaranteed to buy from you because the customer, it's very tough to get a better than 10 X ROI on something. If they're comparing, if they're doing resource allocation or capital allocation, they're gonna be looking for things with the highest ROI and working with you. It's going to give them a 10 X ROI. So, well, that's going to be pretty darn hard to beat. Okay? So this example shows how price changes with the certainty of the probability of the outcome happening.

Okay. So let's get into some real world examples. So let's pretend John creates a solution for construction companies over 10 million in annual sales. Okay. So that's his niche, construction companies over 10 million annual sales. And if this solution is implemented correctly, it's going to save these construction companies 40 hours per month in administrative time. So that's his promise. That's his transformation. He says, I'm going to save you 40 hours a month in administration time. His customers agree that each hour of administration time is valued at \$40. Okay. Because that's what they pay their admin people. The value created from the solution is then a \$1,600 per month. Okay. Because it's 40 hours save times \$40 per admin and bid an hour. So the yearly value created from this solution is approximated to be \$19,200. Okay. So that's the that's great, right? \$19,200. However, John can just say, Hey, I'm going to create \$19,200.

You need to assign a probability. This assumes though, if he were to say, I am going to save you \$19,200. This assumes that the client has a 100% probability of realizing this value. Okay. However, this is not what happens in real life. This is not what happened. So John ran an analysis using his historical customer data and concluded that only 80% of the clients are realizing this 40 per hour, a 40 hour per month savings. Okay. So he looks at the data and he goes, you know what, we're only 80% of our customers are achieving this. So we're gonna assign a probability to this outcome. And he signs a value of 80% to the outcome of new customers. And so he calculates an expected value of \$15,360, which is just 80% of that 19,200. Okay. Then he chooses to price his product at one 10th, the expected value.

So instead of charging 15,000, he charges 1500 a year. And the customer agrees with his assumptions and buys without thinking. They think they're just like, wow, John, thank you for coming into my life. This is a, this is a fantastic transaction. Okay. So in the example, above the metric, John affected the administrative time was translated to real dollars using valid assumptions collected from his customers, similar to what we did with this. We we have a an increase. So with the metrics that we're affecting, translate it into equity value. Okay. And this didn't even account for actual like cash like sales. This is just accounted for equity value. So the the outcome of the outcome value of the transformation was calculated using these assumptions. And the probability was found using real historical customer data and assigned to the outcome, which yielded the expected value.

Okay. So all we're doing is we're calculating what, how much value what's the value of that transformation? What's the probability of that happening? And then we're going to try to charge one 10th of that expected value. Okay. So if you can present a cost expected value ratio of one over 10, the customer will buy. Since it's very difficult to find anything that can compete with a 10 X return. Okay. So the price is also affected by the competitors in the market. So again, if we go back up to this here, that the transformation, the certainty, which is like the value created in the certainty, which is the probability value. Now we also have to account for it, the competitors in the market. Okay. So do, where are we here?

Where are we? Here we go. The price is also affected by the competitors in the market. So let's give an example. So let's pretend John creates this product. It creates a \$15,000 three, 360 15,360 unexpected value. And he's pricing at 1500. However, a new competitor, her name is Mary. She enters the market with a new technology that allows her to offer the same transformation and 80% certainty value. Okay. So basically the same expected value, but instead of charging 1500, she only charges 800. Okay. So the customer will choose Mary solution over John's since the expected values are the same, but the price to achieve those outcomes are different for the customer. And the difference between Mary's and John solution is two X. So obviously if the expected value is going to be the same and the price is cheaper, they're going to choose Mary solution over John's.

Okay. Now let's imagine that Mary's track record and customer results. This is where customer results and track record get really, really important. Let's say that the track record and the customer results are worse than John's. Okay. So pretend that Mary based on our tracker, cause the customer only can assign a 25% certainty. They're like, Mary, I like you, but your track record is just not as good as John's. And it's only, you know, John has an 80% chance and you know what? You might only have a 25% chance of pulling this thing off. Okay. So the customer assigns a \$4,800 expected value, which is 25, 25% times the transformation promise. Okay. And since that expect, since John's expected, value is greater than that. It's 25. It's 80% of that. Well, the customer's likely to go with John's more expensive solutions since the ROI was ROI is greater than Mary's.

Okay. So this example illustrates the relationship between price expected value and competition knows that the customer just cares about the expected value. You can charge a higher price if the expected value is greater than a competitors, meaning the transformation value is high. The probability of the transformation happening is higher than the competitors. Okay? Now this assumes that the customer can afford it. This is another big assumption, or this is another big factor in your price, the ability to afford the solution. So in some markets, the customer literally can't afford, even, even though it's a higher expected value. And it actually makes sense to go take out a loan to, to to to buy, okay. In some cases, the customer literally can't afford it. So you're bound by the customer's ability to pay. Okay. Does that make sense? So you're going to have to play with the price, if you, if you're the best in the market and the competitors, you're the best you have, you offer the best expected value and maybe you're charging one 10th, expected value.

Well, sometimes your customers literally can't afford it. And in which case you're going to have to move down in price. And I don't even like playing there. Like I don't like selling to customers who are bound by the price. There's usually two worlds that you can plan. There's people who can can't afford things and people who really can't and in every market, those there, those two groups of people. And if you can, you want to stick to the folks who can afford your your solution. Okay. So you want to keep that in mind as your pricing as well. So if you, if they can't afford it, you might have to come down and price. Okay. So the expected value, here's another point, the expected value increases over time with continuous improvements of the mechanism. Okay. So the following example shows this price evolution.

So let's pretend that Mike ch he's an, he's an entrepreneur. He chooses financial advisors as the niche, and he makes a promise to them. He says, all right, we're going to add an extra seven high net worth clients per year. Okay. So let's go through another example. He calculates the gross contribution per year of a client of, for financial advisor to be \$20,000. So every, every client that the that the financial advisor advisor brings in is worth 20,000 in contributions of 20,000 and the financial advisors pocket. Okay. So the outcome value is then determined to be \$140,000 per year. So if Mike can fulfill on this promise, he's going to make the advisor 140,000 a year. Okay. However, Mike let's, he's just, he's just starting. He doesn't have a product yet, and he hasn't validated his mechanism. He just has a promise, but he hasn't been able to fulfill the promise.

So this means that the probability of Mike actually pulling off this transformation and fulfilling his promise is relatively low. So Mike, being the entrepreneur, he approaches a few beta customers with the transformation. He goes, Hey, I'm going to get you seven new clients and the beta customers, the early adopters, the one who, the ones who are more risk tolerant, they are like, all right, I'll bite. But I'm only gonna assign a 10% probability that you can do this. I'm not stupid Mike, but I do, you know, I, I think there's about a 10% chance that you can do this. So I'm going to sign a 10% probability to this. And the expected value is then determined to be \$14,000. So if Mike can pull this off there's about a 10% shot, he's going to do it. And it's \$140,000 worth to me. Well, that's about \$14,000, the expected value.

So Mike goes, all right, I'm going to charge five grand to pilot this thing. And the beta customer goes, alright, sure. \$5,000 is less than 14. Okay. I might be able to get a two X ROI on this thing, Mike, I'm not expecting much again, I'm going to give you a 10% shot, so let's give it a shot. So they go ahead and they figure that out and they do business. So after working on a solution for a year, Mike delivers a transformation. Okay. So he's actually working on this thing for a long time. He's actually delivering the transformation. And so the probability of the outcome, the seven clients per year increases. Okay. So Mike approaches another customer. Who's willing to assign a 25% probability to the outcome that Mike can pull off this transformation. Okay. So the new customer goes, Oh, okay, Mike, well, you did a decent job over there.

And we're a little bit different than that guy. So maybe there's a one in four chance that you can pull it off for us. So this makes this, because there's a 25% chance. The probability, this expected value jumps up to 35,000. Okay. So Mike pushes his price up to 10,000 a year. Okay? So that price increase continues as the certainty value increases via the improvements to the solution and documented case studies. So as the, as the product gets better, the solution gets better. And the probability that you can fulfill the promise gets higher. Okay. And customers are willing to assign a higher probability to you pulling this thing off, which means that you can charge a higher price if the customer can't afford it. Okay. So there's a few moving parts here, and that's essentially what's going on. So the example above shows, how price can increase as the solution improves.

And the track record gets more solid. What Mike is actually doing is increasing the certainty value or the probability of the outcome happening, which increases the price. And the price will continue to increase until a competitor comes and offers the expected value for a cheaper price. Okay? So that is essentially pricing explained. If you think about it in this lens, you can't really go wrong. You can you can compete with anybody. If you do your research and you map out this expected value and you really get into the weeds, you'll be able to price your products. And you'll be good. You're going to be able to be super confident in your offer because you're making a promise. You're assigning a probability of that promise happening, and you can be very confident in your price. If you go through this analysis, you might find that you're under priced.

You might even find that you're overpriced. Okay. And so the market we'll we'll we'll tell you where you are. So if you're getting no price resistance, you're probably on a price. If you're getting lots of price resistance, no one's buying, you might be overpriced or your transformation might be off. Okay. All right. So in this section, we are going to give you some methods to calculate the price. Okay. So in the previous in the previous explanation we gave you we explained the expected value. And now we're going to give you a few methods to determine the price. There's a few methods, and I'll give you some some, some guidelines for when to use these methods. So the first method is pretty straightforward. You're just going to charge one 10th, the expected value. Okay. So if you're able to prove an expected value to the customer, okay, then the customer should buy.

If you charge one 10th, the expected value that the customer will jump over a fence to buy. Okay. So how do you want to do this? You want to account for all possible outcomes, using a spreadsheet. So go ahead and create a spreadsheet and map out all the possible outcomes that can happen when you engage with the customer, what are the possible outcomes, including everything crashing and burning. You go out of business. The worst thing happens. You have to assign, you have to, you have to for that outcome happening too. Okay. Then you're going to determine the probability of those outcomes happening using third party data, or firsthand data and assumptions that you can back up to a customer. So you're going to assign probabilities to those outcomes using third party data or your historical customer data. Okay. And you're going to have to, you know, get on, you're going to have to do some research because you're going to have to back up these probabilities.

You're going to have to back up these to the customer and the customer doesn't buy into these. Then they're not going to be influenced by this expected value. Okay. Then you're gonna determine the expected value using the model. Okay. So you're just multiplying the outcome values times the expected value. Okay. Then you're just going to charge one 10th, the expected value. Okay. So that's pretty straightforward. So example, product promises of 50 K per year in value. There's an 80% chance that this will happen. And the expected value is \$40,000, which is 80% times 50 K and your customer will easily pay \$4,000 since the ROI is 10 X. Okay. Example, here's another example. There's a 5% chance of creating at least \$5 million in value in one year and a 50% chance of creating at least a hundred K in one year, the expected value is 250 plus 50.

Okay. And that expected value is \$300,000. Okay. So a customer will easily pay at least \$30,000 to experience that result if they can afford that. Of course. Okay. But if they can, it actually makes sense for them to go to the bank and take out a loan or go and get a credit card and take out some credit to to pay you. Okay. So when is this method? You, so you can use this method when you're pitching larger deals to businesses and you need to justify the pricing. You can use it in your sales script to justify the pricing when you, when you can clearly map out the outcome and the risks. Okay. And when you can make credible assumptions about the impact of your solution and when you're, when you're pitching sophisticated buyers with deep pockets, okay. So this method works when you're selling to people with money, because you know, people with money, they probably have got their money by being smart and making decisions based on this, this formula.

Okay. So if you're trying to use this method with unsophisticated buyers, it's going to be more difficult. Okay. So how to handle competitor's pricing. So if your customer can create the, if your competitor, sorry, it can create the same expected value then you'll need to be priced lower to win the business. However, in most cases you can win with a higher price either by increasing the value or increasing the certainty. Okay. Or both. Okay. Which increases the expected value. Okay. And so to increase your price, you're going to need to either increase the value or increase the level of certainty. Okay. So that's the first method. The second method is the descending pricing method. And this is used when you're testing a new market and you're not completely sure if the customer can afford it. Okay. So when do you use this method?

You're going to, when you're testing a new offer or a new market, and you're getting some price resistance when you're trying to lower turn. So maybe turns too high. Okay. And when you're set, when you're still learning how to sell, and you're looking for small wins to boost confidence. So sometimes you might be on like a little drought and you're going to have to descend in price to boost your confidence and get your mojo going again. Okay. So how to do the descending pricing method? So step number one is you're going to calculate the expected value using method one. Okay. And then you're going to descend using multiple offers until you get the deal. So let's say the expected values, you know, a hundred thousand, you start at 10 K and then you slowly go down until the customer bites. Okay. So what's an example.

Product promises to create \$50,000 in value. There's an 80% chance that this will happen. Okay. The expected value is 40 K. Your customer will easily pay 40 K. However, in this case, your customer unfortunately cannot afford 4,000, but can fucking do 2000. Okay. So you come up with a smaller price and you limit some of the features to get the deal done. Okay. And you're lowering the expected value. If this happens, if this happens multiple times, your pricing will convert at two K because that's what the market will bear. So sometimes you're going to have to do that. But if you, if you're, if you end up descending the price a lot, it's probably because the market can't really handle that price. And you'll start to see this, this point of convergence. Okay. So here's another example. If you're charging \$599 per month for your subscription offer, the customer's experiencing value, but but contacts you 90 days to cancel, maybe because of competitors offering to do the same thing for a cheaper price and customer success drops the price to \$299 per month to test.

If the customer is going to stay on, if this continues to happen and the customer stays on it, the new price point, then the price converges at \$299 per month. Okay. So sometimes you might be overpriced and the customer just can't handle it. So you're gonna have to come down and see where that see where it converges. Okay. So method number three is the trial method. You can, you can use this when you're selling to your beta customers. So when is this method used when you have a brand new product or offer without any case studies or any data when you're entering a new market, a brand new market. Okay. And when your product delivers almost instantaneous value, and you can, you can deliver the value in like 30 days or so. Okay. So what you want to do is you want to give the customer a set period of time to experience the value.

So you might say, Hey, look, I'm going to give you 30 days to try this out. But at the end of this trial, it's going to be this amount of money. So when the period expires, you're going to charge them at the end of the trial, after they get results. And that, that conversation is, is usually really, really easy because if the customer has gotten results from the trial, then they're probably going to continue on if they can afford it. Okay? So you're charging five 99 per month. This is an example, you're charging \$509 per month for your subscription. You offer your first customer 60 day trial. So you can collect the case, study data. At the end of that trial, you request payment. If the customer experience value, then they're going to be happy to pay you a fair price. And if they don't pay, then you're going to have to, you're going to need to fix your product or fix your price.

And you're going to run multiple trials and collect most multiple data points. Okay. So, and then you're going to use these data points to determine your initial pricing going forward. So when you're in the lab and you're testing things, you're just going to pull out a price and you're going to see if the customer stays on. Okay. And then this is the fourth method, that 10% increment method. Okay. So when is this pricing method used when your offer is selling through, and you're not experiencing any price resistance, or when that customer goes really? That's it. That's all you're charging, man. That's crazy. Okay. So what you're going to do, if you're getting no price resistance, you're going to increase your price by 10% for each customer, until you experience at 10% resistance or sorry, resistance by 10% of your customers. So you want 10% of your customers to be like, nah, it's too expensive.

Okay. But you want 90% of your customers to be like, okay. Yeah, that makes sense. Okay. So here's an example. You're charging \$299 per month for your subscription. Offer. Customers are happy and you're not experiencing any price resistance. So you increase the price to three 29 and the three 99 and four 99. And at the four 99, you experienced some resistance, but your customers are still happy. And so you set your you set your new price at four 99 per month. Okay. So it could take you 20 to 30 customers in the same niche to dial in this price. Okay. And remember to be scientific with your decisions. So don't operate using emotions. Don't, you know, so if someone says it's too expensive, don't go throw the baby out with the bath water and change your pricing for everybody, make your decisions based on data and make sure you have enough data points.

Okay. So look at the data, listen to your customers. If someone is turning out or complaining, try to determine the cause effect relationship. Okay. Sometimes it has nothing to do with price. They simply don't need the product anymore. They their circumstances change or they suddenly got broke or something like that happens. Okay. If you're selling to the Lord, that's why I don't like signing the lower end of the market. I'll let you in a little secret. I don't like selling the lower end of the market because they're so like volatile. So if you're not an expert salesperson, yet, there's a good chance that you will corrupt your pricing data. So it does take sales skills to get this down. So you're gonna have to practice, especially if you're new to sales or you're new to this type of selling, you're going to have to practice.

So don't be frustrated, just keep on trucking, keep going through this method, keep doing your sales calls. And this is why we teach you sales in this program. So as you gather more experience and more confidence with sales, there's a good chance that you're going to be able to increase your price. Okay. And remember that your price is not set in stone. It's a function of the vehicle and your sales and marketing skills and the competitors. Okay. So if your transformation improves, you're able to provide more value. If your sales skills get better, your marketing skills get better. I think competitors, the competitive landscape changes, then that will, those will all impact price. So don't get hung up on price, pick a price, run with it and iterate accordingly. So don't get hung up just especially in the beginning. You're going to go to your foundational copy spreadsheet and you're just going to come up with a price.