Do You REALLY Know Your Business Model?

Do You REALLY Know Your Business Model?

Clarity is a repeated theme of mine and The Disruption!, whether in regards strategy or how you make money. Listening to Josh Kaufman discuss his “Five Parts of Every Business” and the need to define your business model while presenting this information clearly magnified the point.

 

What Are The “5 Parts of Every Business”?

Kaufman says in every business model there are “5 Parts of Every Business,” each of which flows into the next:

  1. Value Creation: A venture that doesn’t create value for others is a hobby.
  2. Marketing: A venture that doesn’t attract attention is a flop.
  3. Sales: A venture that doesn’t sell the value it creates is a non-profit.
  4. Value Delivery: A venture that doesn’t deliver what it promises is a scam.
  5. Finance: A venture that doesn’t bring in enough money to keep operating will inevitably close.

 

Value Creation

Kaufman defines Value Creation as “Discovering what people need or want, then creating it.”

Most customers don’t know what they need or want. As has been pointed out many times, people wanted a faster horse, not an automobile. However, whatever they want, in reality, they are just seeking a solution to a problem. Therefore, the critical issue is determining “What problem you are trying to solve?” Or, as Clayton Christensen said, “What is the job the customer is hiring you or your product to do?”

Defining this is often hard, as many companies don’t know what job their clients are seeking them or their products to provide. I have discussed this before. However, as the adage says, “people aren’t buying drills, they are buying holes.” This is a vital part of your business model.

So, working with your team to determine “the job to be done” and your “Core Customer” is well worth the effort because you can better describe what you do, and all your employees will better know what you do and how what they do impacts it.

 

Marketing

Kaufman’s definition is “Marketing is defined as attracting attention and building demand for what you have created.”

In today’s digital world, with Google, Facebook, Linked In, and Instagram, marketing separating yourself from the masses is hard, especially if people don’t understand the product and service. Therefore, by focusing on the job to be done or the problem you are solving, it easier to stand out among the crowd.

Also, as you identify what the “job to be done” is, you can better identify your Core Customer. Remember a Core Customer is:

  • An actual person with needs and wants. If you sell B2B your core customer is still a person because you have to convince a person to buy.
  • Who buys for the optimal profit.
  • Who pays on time, is loyal, and refers others.
  • Has a unique online identity and behavior; and
  • A customer who exists amongst your clients today.

Build Direct started as a company supplying contractors. However, it soon realized that while contractors were a key customer component, they were not the company’s Core Customer; instead, Build Direct’s core customers were young female DIYers interested in the products and education. Build Direct focused its marketing according to that recognition and started providing much educational content for young female DIYers. This specific marketing drove much better brand recognition and engagement.

Also, South Shore Furniture in Canada identified their core customer as “Sarah.” Sarah is so vital that there is a mannequin of Sarah in all meeting rooms, so no one forgets whom they are seeking to serve.

Besides, marketing to the correct demographic is easier and more fruitful if you know your Core Customer. Without this information, the marketing section of your business model is just hope, not a strategy!

 

Sales

Kaufman defines sales as “Turning prospective customers into paying customers.”

However, as Jeffrey Gitomer, put it “People don’t like to be sold, but they love to buy.” So the key is how do you move prospects into customers? Businesses have to earn their prospects’ trust and help them understand why it is worth paying for the offer. Another way of looking at this is, “What is your brand promise?”

Companies need to know what their brand promise is. For example, Starbucks is “Love your beverage or let us know and we will always make it right.” Some organizations may have supporting brand promises to prove more definition of the brand promise. Your brand promise must be measurable, because as Peter Drucker said, “What gets measured gets managed.” So if it is measurable and measured, the organization can ensure that it meets its brand promise, which provides more assurance to the prospect. Finally, with a clearly defined brand promise that is measurable, the organization ends up saying “No” more than “Yes” to opportunities and ideas since they will damage the brand promise.

Since no one wants to be taken advantage of, Sales is about educating the prospect to identify what is essential to convince them you can deliver on your promise. A clearly stated brand promise that is measured and quantified increases the ability to persuade the prospect to purchase from you. It amazes me how many business models don’t have a brand promise.

 

Value Delivery

Here Kaufman defines Value Delivery as “Giving your customers what you’ve promised and ensured that they’re satisfied.” With this, I have no issues. Anyone who doesn’t deliver what they promised is effectively a “scam artist.”

To ensure you that make the customer satisfied, you have to exceed the customers’ expectations. A popular way to determine customer satisfaction is through Net Promoter Score scores which we see more and more (if you are looking for help with NPS surveys of your customers, contact me). You want more promoters and detractors. However, the NPS score tells you what the customer thinks after experiencing the service or product. Companies need to develop systems that ensure the service or product is exceeding expectations.

A great example is the Ritz Carlton’s policy whereby any Ritz-Carlton employees can spend up to $2,000 per incident, not per year, to rescue a guest experience. This policy ensures that the customer is getting a great experience because it empowers employees to fix problems and provides the customers’ concerns are solved quickly. As David Marquet says, “Move the decision making to where the information is.” That is what Ritz is doing, and it is empowering employees and making customers happy.

Companies that have outsourced many functions to cut costs, so any customer has difficulty reaching the people they need or have to spend five minutes going through a phone tree to contact some is already failing at this.

Ensure your business model tracks customer satisfaction and you have ways to ensure that customers are happy.

 

Finance

Kaufman defines finance as “Bringing in enough money to keep going and make your effort worthwhile.”

As I have pointed out, this is key, and many people don’t realize the situation because of flawed analysis and lousy modeling. However, the key for any organization must be a well-defined “Profit/X.”

Many organizations don’t have a well-defined Profit/X, but there is a lack of discipline that ensures good financial performance without it. Profit/X is some unit of scale, and profit can be gross profit, net profit, EBTIDA, or EBIT. Examples that I have seen are:

  • profit per airplane
  • profit per job
  • profit per customer
  • gross margin per delivery
  • profit per employee

There is no correct Profit/X, just the one that works with your business. One organization that did deliveries chose Gross Margin/Delivery, which focused on reducing the cost of delivery to maximize profit. Once Profit/X is selected, the entire organization must seek to meet or exceed it; thus, everyone needs to understand it and how they drive it. With that focus and discipline, the organization is more likely to meet its financial goals and objectives.

 

Summary

In summary, the organization needs to be able to define its business model by the following:

  • Define the problem its products or services solve or, more precisely, what job they do.
  • Who their Core Customer is so they can market to them effectively?
  • What is their brand promise, and how is it measured?
  • That their customers are satisfied, returning and recommending.
  • That they have identified their Profit/X so that they are profitable.

Doing this work is an excellent exercise for any leadership team to help bring clarity to your organization. If you need assistance doing it, contact me. Good luck, and may your business grow.

 

 

Copyright (c) 2021, Marc A. Borrelli

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The precision versus accuracy concept was first brought home to me back in the depths of time while I was doing Physics “A” Level in high school and calculating measurement errors in experiments. From this, I learned that no matter how precise one’s measurements were, there is an inherent error in the tool used to measure the results. Taking into account that lack of precision provided more accurate results, which is what we sought.

When I got involved in finance during my time in business school, the measurement tool was the currency, and so precision was deemed to be the smallest currency unit, i.e., a cent. This precision was encouraged by the beautiful computing power of Lotus 1-2-3 and then Excel, which allowed you to do forecasts that were precise to the cent going out years based on a few assumptions. While the precision was there, accuracy was foregone as we built huge models on a few simplistic assumptions.

However, upon leaving business school and entering the real world, my illusions were quickly dashed. After my first week at work, I presented my boss with a ten-year forecasting model that showed profit and loss statements, balance sheets, cash flow statements, and of course, a discounted cash flow valuation. He looked at it and asked how confident I was in my forecasting. As I defended it, he looked at me and asked in a sort of Bayesian way, “Would you bet your bonus on the company making the revenue numbers you have for this year?” (we were a quarter of the way into the year).

“No!” was my quick reply.

“In that case, how can you defend ten years of numbers which all build off that initial number?” he asked. This has lived with me ever since that we quickly get sucked into the precision of our models, but the numbers are so inaccurate.

During my career, I have to see precise projections for sales, projects, costs being bandied around in companies by everyone, including the CFO. Yet few, if any, would bet a modest amount, let alone their bonuses, on those numbers being the final results. Thus we end up with the corporate situation where everyone knows the numbers are wrong but cannot admit it.

However, when working with these same companies and trying to get them to accept the concept of ranges in forecasts and probability distributions, I am regarded as though I had just stated that the earth was flat and supported by elephants. One CFO of a public company looked at me with total distance and asked: “Why would anyone ever need something like that?”.

When I responded that I was seeking more accuracy, albeit with less precision, in financial projections, it was apparent that had I reverted to speaking Fanagalo. Another CFO used precise numbers (down to the dollar when their revenue was over $20 million). None of their forecasts were ever correct, but he could not accept that forecasting to the million was acceptable. I heard that their last results were a huge surprise given the forecasts; however, I was not surprised at all given the forecasting process.

This trade-off of precision in return for greater accuracy continually is rejected by many of the financial professionals that I meet. I have wondered if the cause of this focus on precision encouraged by the accounting profession makes it hard for these professionals to let it go.

Going back to my boss’ logic – if you would not bet on a single number being accurate, would you be prepared to bet on a range? For example, the forecast may say that next years’ revenue will be $10 million, but no one will bet their bonus on it when pushed. So would you be willing to be that it will be between $9 and $11 million or $8 and $12 million? When you reach a range that you would be willing to bet on, it is surely the range to use in the forecasts.

Using ranges like this and applying techniques like Monte Carlo simulations results in far higher accuracy in forecasts and an understanding of the risk profile of the results, i.e., there is a 50% probability that revenue will be between $9 million and $10 million or that profits will range between $0 and $1 million. Furthermore, one can see what items are critical to the final results, what is not. The company can focus on those essential items rather than getting distracted by the noise.

It is best to remember Nils Bohr’s quote, “Prediction is very difficult, especially if it’s about the future.” Therefore, to make the predictions more useful, it is best to forego precision in return for greater accuracy.

 

© 2013 Marc Borrelli All Rights Reserved

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Excessive precision.

When I see models with numbers in the millions and show accuracy to the dollar, or even worse, the cent, red flags fly. Just because Excel can calculate to ten plus decimal places, you don’t need to use it. That much precision is a distraction and is usually wrong, especially if it is a forecasting model. In the words of Niels Bohr, “Prediction is very difficult, especially about the future.” Thus models that predict amounts to the dollar a year away are wrong!

Ask yourself what level of accuracy is needed. If you are dealing in tens of millions and show numbers to the thousand, the error level is 0.01%, which is more than enough precision. Another test is, would you be willing to bet on the outcome being right to that level of accuracy, and if not, ask why you are showing it. Remember the adage, “I would rather be 90% right and imprecise than precise and 100% wrong.”

Hardcoded numbers.

Models that have hardcoded numbers in them also raise red flags. Users forget they are there, and they remain forever with no rhyme or reason, leading to wrong results. If things change, it is hard to find them and correct them in all the cells that need changing. Another issue with hard-coded numbers is where a correct model produces #N/A or #DIV0 results due to the values entered, and the model cannot adjust. To fix these, instead of using Excel logic statements, users change the formulas to exclude the offensive input. However, when the correct data is obtained and entered, everyone has forgotten about these changes. As a result, I have seen large companies use fatally flawed models in planning, but no one has realized it. Finally, requiring hardcoded numbers to change a model to get it to produce correct results could mean the model is fatally flawed in design and operation – time to start again.

No tracking of results.

Recently I met a firm that had an Excel model it had been using for all its forecasting for years. I inquired about how accurate it was, and the answer was that the last month had been a huge surprise, but no one had ever tracked its results against actual. If the model’s results are not measured, its effectiveness is unknown, and no improvements are made. Thus one could be relying on something wrong for years and not realize it. No model is perfect, and they are like an iterative process, use them, measure the results, and then adjust them to improve the results.

Understand the logic of the model.

Understand what the model is trying to accomplish. It is often good to diagram out how all the parts will fit together and where the different components will reside in the model. Also, think through all the pieces carefully about how they work and what they do. It is my experience that usually 6 – 10 items contribute to the majority of the values, and so they need the most precision; the rest will not change things much, and we don’t need to focus on those as much. Also, beware of some essential items, i.e., exchange rates.

Another Excel model I recently reviewed had no exchange rate assumptions even though the company had significant European operations – this oversite would lead to incorrect forecasting. Finally, remember all the parts! The complex financial model used business forecasting mentioned above showed P&L, Balance Sheet, and Cash Flow statements; however, the model didn’t distinguish between book and tax depreciation, leading to incorrect cash flow statements well as errors elsewhere.

Layout.

A model should be like a book or an essay – an introduction – the characters – the plot – the conclusion. I have seen everything mixed up, and you cannot follow the logic or flow, which makes it hard to read and understand. Make the model easy for the user to follow and read. Thus, I would recommend:

  • A tab that explains what the model does and describes what each tab does as well.

  • Color Coding. Use color-coding to distinguish between input, calculation, and output areas.

  • Indentation. Use the cell indents for indentation, not another column

  • Colors. Use colors to format types of cells. It makes it very easy to see what are inputs, assumptions, outputs, etc. If all input cells are yellow, then the user can quickly know what to change. Excel provides this through cell formatting.

  • Data Validation. Use lists to control user inputs, which prevents mistakes from accidentally happening.

  • Spell check. Excel provides it, so use it.

  • Make it clean and easy to read.

  • Tabs, Use more tabs with a purpose rather than one colossal tab that is difficult to navigate around. Break tabs down into Assumptions, Working, Results. These tabs make it much easier to follow.

  • Assumptions must feed the model. All assumptions should feed into the appropriate parts of the model – just showing them on an Assumptions page is useless. Remove any cells that contain assumptions that don’t feed into anything or do calculations. Users will change them to no avail.

  • Check cells. Use check cells to ensure that things are correct. If you are building a forecasting model, put in a line showing that the balance sheet balances. Thus if it doesn’t, you can easily see it rather than suddenly realize it when it is too late.

I hope that you find this useful. Good luck, forecasting.

 

© 2013 Marc Borrelli All Rights Reserved

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