Monthly Archives: May 2016

The Dragons Perspective

The BBC2 show Dragons’ Den is now in its ninth series, and while the show is obviously edited to entertain (more than anything else), it seems as if the current bunch of contestants has not watched any of the previous shows! Week after week they appear – often repeating the mistakes of previous contestants. This article attempts to shed some light on the key mistakes being made and recommends some key changes required by future entrepreneurs pitching to investors. Obviously, the lessons here will apply regardless of which finance sources you intend to approach.

1. Complete a business plan in advance

The first thing that is evident is that many of the entrepreneurs appearing on the show have a business idea, but have no clue as to whether the idea is commercially viable or not. A business plan forces entrepreneurs to cover all aspects of the business – not just the idea. If a thorough business plan has been produced, entrepreneurs should be able to handle most questions the Dragons throw at them. They are not trying to catch the participants out. They are simply trying to assess the opportunity to determine whether it is a credible investment option for them. Usually the idea is easy to grasp from the presentation – what prospective investors really want to understand is whether there is a demand for the product, what the scale of that demand is, and how these markets can be accessed.

2. Know the basics

The investor is seeking to diversify their portfolio, investing in some high-risk ventures in return for some attractive upside return. The entrepreneur is seeking investment to develop the idea further. If the company is currently trading, you will be required to have clear answers as to the current actual turnover, gross profit and net profit. Any vagueness regarding these rudimentary financials will set alarm bells ringing. Any credible business person will be expected to have a grasp of these numbers, and an investor will need to know them to make an informed decision as to whether to invest.

3. Share data and information truthfully

There is an information asymmetry between the entrepreneurs and the Dragons, i.e. the entrepreneurs have a lot more information to hand than the Dragons. The investors have to rely on the data the entrepreneurs provide when they assess the risk and the likely return. Hence, unless the entrepreneurs provide the information in their presentations, the investors are going to be asking for it. The information that is provided had better be accurate, as the due diligence that follows will examine the data in detail and will need to substantiate the figures provided in the Den. While it is natural to not want to divulge a lot of information, without it the Dragons may be reluctant to invest.

The numbers you need to know

unduhan-37Those familiar with the BBC2 show Dragons’ Den will be all too aware of the following scene. The entrepreneur is tasked with presenting his business plan to a panel of investors (i.e., the Dragons). The business plan pitch is going well, and then one of the dragons asks the simplest of questions,
”What was your turnover last year?” The camera pans in, the participant stutters, eventually he declares that he is ”not sure” and before you know it, he is sent packing. Why do entrepreneurs consistently fail to appreciate how important it is to have financials in hand when pitching an idea? Why do they consistently present a business plan without even a rudimentary knowledge of basic financial concepts, such as turnover or margin? This article highlights some of the financials that any aspiring entrepreneur needs to know before submitting or pitching a business plan to a ‘dragon’ of any hue.

Firstly, let’s consider the context. Investors have a range of investment options available to them. While depositing cash in a bank is low risk, it is not the most exciting option and associated returns are likely to be low. Angel Investors or Venture Capitalists are looking for investment growth opportunities that offer the potential of a greater return; which naturally come with a commensurate increase in the risk. The level of risk is dependent on a number of things; the market risk (whether there is a market opportunity and the extent of it) but also risk relating to the decisions made by the agent (i.e. the entrepreneur).

With debt funding such as a loan, the investment is typically secured on some assets and the repayment schedule will guarantee monthly income streams to repay same. When it comes to equity financing, the risk dynamic increases considerably. Why? Because decision-making is in the hands of the entrepreneur, not the investor. An investor must endeavour to ensure that the incentives of the agent (the entrepreneur) are aligned with his or her own. This ensures that investment is not spent on non-income-generating investments or perks. This is commonly referred to as the ‘principal agent problem’ by economists.

Potential equity investors will also be keen to assess whether the entrepreneur will be a competent business manager. To address these concerns, the investor will be looking to not only understand the product and market opportunity, but also to understand the abilities of the management team tasked with delivering the opportunity. Hence, the entrepreneur needs to be confident, knowledgeable, and trustworthy but also au fait with the underlying financials for the business.

In assessing these risk factors, historic data will play a crucial role in the investors’ decision-making processes. Investors will be trying to assess the existing cash generation capability of the company and also the free cash flows that remain once all other obligations have been met. Hence, someone claiming to not know turnover or net profit figures from past trading probably has something significant to hide. If figures are low, that is fine, provided you can explain why some of the figures were not as you would have wished. If you have not begun trading, the risk profile increases dramatically and as a result you should expect an increase in the equity stakes required by interested investors. The three headline figures to be particularly cognizant of are Turnover/Revenue, Gross Profit and Net Profit. The figures for these provide an indication to the investor as to the level of demand for the good or service and also whether this demand can be met profitably. If you have been trading, you need to have a firm grasp on the P&L figures and also a good explanation for the underlying performance to date.

Once you have covered off the key financials, and the “dragon” is willing to invest, the focus will shift to the following:

  • The value of the entire business.
  • The percentage of the business you are prepared to sell.
  • The value of the share.

Use a multiple of earnings or an assessment of future income streams to estimate the value of the business, and then decide what level of equity you are prepared to offer in return for a cash investment. Most investors are pretty sophisticated when it comes to financing, and hence, you will be at a disadvantage. This is their area of expertise; they are seeking an appropriate risk/return for their investment. Their primary interest will be to assess the ability of the company (including management) to generate free cash flows to enable the business to grow while also returning cash to them. It is recommended that you get some advice with this area before you enter the den. It is important that there are no complex structures in place vis a vis where the value lies, the company structure, or existing shareholders. Dragons do not like surprises- so don’t deliver one, especially right at the end!

Finally, it is worth having a walk away point in mind. If the offers from the dragons do not match the valuation you have placed on the company and the stake on offer, be prepared to walk away. If you get to this stage and have some offers on the table, it is likely that you will be able to secure funding elsewhere at a level closer to your valuation.

Company Machine Learning Ready

In recent years, there has been a staggering surge in interest in intelligent systems as applied to everything from customer support to curing cancer. Simply sprinkling the term “AI” into startup pitch decks seems to increase the likelihood of getting access to funding. The media continuously reports that AI is going to steal our jobs, and the U.S. government seems as worried about the prospect of super-intelligent killer robots as it is about addressing the highest wealth disparity in the country’s history. Comparatively, there has been very little discussion of what artificial intelligence is, and where we should expect it to actually affect business.

When people talk about AI, machine learning, automation, big data, cognitive computing, or deep learning, they’re talking about the ability of machines to learn to fulfill objectives based on data and reasoning. This is tremendously important, and is already changing business in practically every industry. In spite of all the bold claims, there remain several core problems at the heart of Artificial Intelligence where little progress has been made (including learning by analogy, and natural language understanding). Machine learning isn’t magic, and the truth is we have neither the data nor the understanding necessary to build machines that make routine decisions as well as human beings.

That may come as a disappointment to some, and potentially disrupt some very expensive marketing campaigns. But the likelihood of self-directed, super-intelligent computational agents emerging in the foreseeable future is extremely low — so keep it out of the yearly business plan for now. Having said that, an enormous amount can already be achieved with the machinery we have today. And that’s where forward-thinking managers should be focusing.

Over the next five to 10 years, the biggest business gains will likely stem from getting the right information to the right people at the right time. Building upon the business intelligence revolution of the past years, machine learning will turbocharge finding patterns and automate value extraction in many areas. Data will increasingly drive a real-time economy, where resources are marshaled more efficiently, and the production of goods and services becomes on-demand, with lower failure rates and much better predictability. This will mean different things for different industries.

In services, we will not only get better at forecasting demand, but will learn to provide the right product on a hyper-individualized basis (the Netflix approach).

In retail we will see more sophisticated supply chains, a deeper understanding of consumer preferences, and the ability to customize products and purchase experiences both on- and off-line. Retailers will focus on trend creation and preference formation/brand building.

In manufacturing there will be an evolution towards real-time complete system monitoring, an area known as “anomaly detection.” The components will become increasingly connected, allowing for streams of real-time data that machine learning algorithms can use to reveal problems before they happen, optimize the lifetime of components, and reduce the need for human interventions.

In agriculture, data will be used to decide which crops to grow, in what quantities, in what locations, and will render the growing process more efficient year after year. This will create more efficient supply chains, better food, and more sustainable growth with fewer resources.

In short, AI may be a ways off, but machine learning already offers huge potential. So how can managers incorporate it into daily decision-making and longer-term planning? How can a company become ML-ready?

Better Decisions with Less Data

Maria, an executive in financial services, stared at another calendar invite in Outlook that would surely kill three hours of her day. Whenever a tough problem presented itself, her boss’s knee-jerk response was, “Collect more data!” Maria appreciated her boss’s analytical approach, but as the surveys, reports, and stats began to pile up, it was clear that the team was stuck in analysis paralysis. And despite the many meetings, task forces, brainstorming sessions, and workshops created to solve any given issue, the team tended to offer the same solutions — often ones that were recycled from prior problems.

As part of our research for our book, Stop Spending, Start Managing, we asked 83 executives how much they estimated that their companies wasted on relentless analytics on a daily basis. They reported a whopping $7,731 per day — $2,822,117 per year! Yet despite all of the data available, people often struggle to convert it into effective solutions to problems. Instead, they fall prey to what Jim March and his coauthors describe as “garbage can” decision making: a process whereby actors, problems, and possible solutions swirl about in a metaphorical garbage can and people end up agreeing on whatever solution rises to the top. The problem isn’t lack of data inside the garbage can; the vast amount of data means managers struggle to prioritize what’s important. In the end, they end up applying arbitrary data toward new problems, reaching a subpar solution.

To curb garbage-can decision making, managers and their teams should think more carefully about the information they need to solve a problem and think more strategically about how to apply it to their decision making and actions. We recommend the data DIET approach, which provides four steps of intentional thought to help convert data into knowledge and wisdom.

Step 1: Define

When teams and individuals think about a problem, they likely jump right into suggesting possible solutions. It’s the basis of many brainstorming sessions. But while the prospect of problem solving sounds positive, people tend to fixate on familiar approaches rather than stepping back to understand the contours of the problem.

Start with a problem-finding mindset, where you loosen the definitions around the problem and allow people to see it from different angles, thereby exposing hidden assumptions and revealing new questions before the hunt for data begins. With your team, think of critical questions about the problem in order to fully understand its complexity: How do you understand the problem? What are its causes? What assumptions does your team have? Alternately, write about the problem (without proposing solutions) from different perspectives — the customer, the supplier, and the competitor, for example — to see the situation in new ways.

Once you have a better view of the problem, you can move forward with a disciplined data search. Avoid decision-making delays by holding data requests accountable to if-then statements. Ask yourself a simple question: If I collect the data, then how would my decision change? If the data won’t change your decision, you don’t need to track down the additional information.

Step 2: Integrate

Once you’ve defined the problem and the data you need, you must use that information effectively. In the example above, Maria felt frustrated because as the team collected more and more pieces of the jigsaw puzzle, they weren’t investing the same amount of time to see how the pieces fit together. Their subconscious beliefs or assumptions about problems guided their behavior, causing them to follow the same tired routine time and time again: collect data, hold meetings, create strategy moving forward. But this is garbage-can decision making. In order to keep the pieces from coming together in an arbitrary fashion, you need to look at the data differently.

Integration lets you analyze how your problem and data fit together, which then lets you break down your hidden assumptions. With your team, create a KJ diagram(named after author Kawakita Jiro) to sort facts into causal relationships. Write the facts on notecards and then sort them into piles based on observable relationships — for example, an increase in clients after a successful initiative, a drop in sales caused by a delayed project, or any other data points that may indicate correlated items or causal relationships. In doing this, you can create a visual model of the patterns that emerge and make connections in the data.

Help You Write a Winning Business Plan

Writing a business plan is one of the most important things an entrepreneur must do when starting a new business. However, writing a compelling business plan is easier said than done, particularly when time can be so precious. This article outlines the key resources available to UK entrepreneurs when preparing a business plan. Use them for assistance with the writing of the plan, as well as for understanding the implications of certain business decisions you make in the process, such as those regarding corporate structure, sources of funding, and more.

1. Business Plan Pro UK software

Business Plan Pro is the best-selling business plan software available for several reasons. It is easy to use, saves time, and has over 500 sample business plans to get you started. It also provides a structure whereby you can complete a plan in a methodical manner, while enabling you to benefit from a helping hand at every step.

Where: Business Plan Pro is available from
Cost: RRP is only £79.99 for the Complete version and £129.99 for the Premier.

2. Business plan competitions

Numerous business planning competitions are taking place in the UK at any given time. These competitions test a wide range of skills that are often neglected by entrepreneurs. By producing a credible business plan and presenting your case persuasively, you will significantly enhance your ability to secure funding. These business plan competitions are an invaluable resource enabling you to road test your business plan in a safe environment before submitting the plan to potential investors.

The main advantages of submitting a business plan to a competition include:

  • Tap into Increased Support for Entrepreneurs in the UK
  • Obtain Critical Independent Analysis of Your Business Plan
  • Gain Access to Mentors and Networking Opportunities
  • Improve key Transferable Skills, e.g. Presentation Skills
  • Enhance Your Understanding of What Investors Want