Stephan’s Blog

Stephan’s Blog

Looking for project funding?

Looking for project funding?

Make sure you have wrangled the numbers…

Make sure you have wrangled the numbers…

10 April 2017 | 6 minutes read

Share this:

If you don’t know your numbers inside and out, and cannot back them up with credible data, the best pitch in the world will (probably) not be enough to get you the funding that you need. Here’s how to wrangle the numbers you need into believable financial models that wins funding.

How big is the market? Is it growing and, if so, how fast? How much of that market can we reach? At what cost? What is the potential of one sales channel versus another? Do we know the expected lifetime value of a customer?

Providing credible answers to questions such as these is a key success factor in developing commercial concepts and getting them funded. And yet, the overwhelming majority of businesses however don’t have any real answers, at least not credible answers. In response, funders typically view development projections with a large dose of suspicion.

Imagine that, after developing an exciting commercial concept and surviving the initial pitch, you get invited to present your idea to the senior funding committee at a major merchant bank. One of the bank’s representatives asks something like: “I see that you project that the SuperWidget market is worth approximately $2 billion, but I see no clear rationale for it. What do you base that figure on?”1 It’s a perfectly reasonable question. Yet most people choke as they realise, in a flash of horror, that their figures simply do not stand up to scrutiny.

Let’s assume that that the banker simply overlooked your carefully prepared market analysis and financial model. You are ready.

“Great question!”, you retort without missing a beat.

“As you know, SuperWidgets are used by industrial customers such as mines, plastics manufacturers, metal extruders, etcetera. It is a very important type of widget, but really represents a small sub-segment of a much larger market. Finding reliable secondary data on it was therefore nearly impossible, and we had to conduct our own research.

Firstly, we constructed a database of potential SuperWidget customers, and then surveyed that database to determine usage volumes. When speaking to SCM~ and Purchasing Managers, we asked them, among other things, how many SuperWidgets they used for each year over the past five years, how much they project that they will need in the current and following years, and how long average order lead times were, which gives us some indication of supply availability and competitor capacity, since we understand SuperWidget manufacturing dynamics. We also asked whether there were any supplier disqualifications for technical or quality issues, and what those were.

Independent of that exercise, we also spoke to site manager, front-line supervisors, and engineers. We asked them about many things, but germane to this to this discussion is that we checked their reported consumption levels against the data from SCM and Purchasing. We also called around to competitors—speaking to sales reps and sales managers, for instance—to get a sense of their production capacity and market demand.

Armed with this information, as well as what we could glean from a few secondary datasets, such as StatsSA and a hand full of international resources, we constructed a predictive economic model of the SuperWidgets category. It is probably the single most comprehensive view of the category to date, and uses advanced stochastic methods to simulate SuperWidget market dynamics. With it, we can predict with a 97% level of confidence that the Super Widget market is worth, at a minimum, R2 billion per year. To say that differently, there is a 97% chance that the SuperWidgets category is larger than R2 billion per annum. We also know that its expanding at a rate of at least 8.25%.

This and all other financial and technical data is available in the business plan you received. At the end of my presentation, we can have a careful look at any technical and financial details you may wish to discuss. Now, let me draw your attention to…”

How many applicants can answer such questions with this level of confidence detail? Not many. And it makes a huge difference when asking for millions (or billions) in funding.

How to do it

An incredibly important aspect, obviously, is to KNOW the numbers, inside and out, so that you may answer such questions with conviction, and move back to your pitch as quickly as possible.

However, the preparation of your financial models is just as important. Remember that they can and will be scrutinised by funders at leisure, so you had better make sure that your figures are solid.

I think that the best use of your time is in two areas: budgeting and economic modelling. Accurate and detailed budgeting and economic modelling provide a real sense that the management team is experienced, and knows the business that they are getting in to. It is a marker of credibility.

Developing kickass budgets and economic models start with an above-average commitment to research. If you are planning a new medical facility, for instance, it is very useful to know things like the following when developing a budget:

  1. what types of clinical business units (CBUs) the facility will house (such as theatres, surgical wards, medical wards, high-care units, paediatrics units, etcetera);
  2. the categories and distribution of cases typically served by each CBU (for a general surgical ward, this might include everything from appendectomies to knee replacements);
  3. every single product and service (down to the line items) that would typically be sold per case;
  4. typical sales price and cost price of product and service;
  5. …and the many other details that go into a hospital’s budget.

You also need market data, such as related to market size and growth rate, health insurance, demographics, community health statistics, birth rates, competitive dynamics, market needs, industry drivers, and so on.

If you have enough data on any of these points, it is often possible to fit a distribution curve to that data that accurately describes it, which in turn allows you to develop a stochastic simulation in which each data point is varied at random, millions of times, along its curve. As each result is computed into the model, you get a simulation that can predict outcomes with a much greater level of certainty than was possible before, and determine the likely impact of decisions and events on the business.

Need help putting together your own economic models? Get in touch.

  1. Ideally, such a question will never come up, since the business plan/prospectus/presentation deals with that information effectively. And therein lies an important lesson: you must make every piece of content work as hard as possible to head off technical or financial details from encroaching on your well-crafted pitch. [return]