A Sales and Marketing ‘Traveller File’

At Owlstone we manufacture complex instrumentation, with lots of parts, and lots of people involved at each step in the process. We have a file of information called a ‘manufacturing traveller’ that starts empty and at the end of the manufacturing line, is full of documentation and test results. At each step in the build process, different members of the team will complete some tests, add the results and pass the traveller on to the next team member. Each has their own responsibility and is trying to answer a specific question, but the integrated output answers a whole set of questions that no one person can answer individually. I think there is a parallel between the manufacture of physical goods and the manufacture of sales.

A sales person in the field, tends to have the clearest understanding of the customer problem and value of the solution. However, this doesn’t always make it downstream, with high fidelity, to those who generate marketing collateral. Equally the marketing team will be generating significant insight into the sales messages that work through testing of ad copy, landing pages, email copy , offers etc. A sales and marketing ‘traveller file’ that gets passed between the team, could help connect different parts of the cycle. For a new product or application the infield sales team will be trying to understand the customer pain and decide if there is a ‘rough fit’ with your offering. They are populating the traveller with information on the problem, the value of a solution, hot buttons, prospective customers, client interviews etc. This is the analogous to the exploration stage in the lean start-up methodology. When it comes to validation, other members of the team will start to get involved; the traveller gets passed on to marketing who start to put together some collateral and run test campaigns. The goal is to achieve product-market fit and as a by product the traveller is now populated with solid empirical data – the inklings of working campaigns and channels, persuasion assets that resonate. The collected traveller is to able to answer questions that no one person can answer on their own.

Quick and Dirty Data Appending

With internet based marketing you generally don’t want to ask for too much information on a webform. One common practice is to take the minimum amount of information on the form and use a ‘data appending’ service e.g. Jigsaw, InsideView or LinkedIn to build out the details for a new lead e.g address and company information.

A quick and dirty, but effective technique is to send an email to your lead database on a public holiday – a high percentage of people have their Outlook ‘Out of Office’ switched on, which means you get a return email, often with a lot of useful detailed  information, e.g. job title, direct phone number that can be used to append the lead record in your CRM system.

On average, our customers have one breast and one testicle – the problem of forecasting

The ‘Flaw of Averages‘ is alive and well in sales forecasting in CRM software. I have fallen in love with salesforce.com (big time) and have been playing about with the forecasting module; great, but is doesn’t capture the binary nature of a sales opportunity. If I have an opportunity for $100k and assign a subjective probability of 60% that we will get it, what should we put in a forecast? Expected value = $100k x 60% = $60k – not really cos it either ends up $0 or $100k. Same principle that “The average human has one breast and one testicle” (Des McHale).

When these normalised expected values are used you don’t get a sense of the pipeline variance. For example, if we have 100 opportunities of $1k, each with a likelihood of 50% or, 1 opportunity of $100k with a likelihood of 50%, the expected value is the same, $50K, but I know which of these I would rather include in a forecast.

It’s Monte Carlo time: salesforce.com has a nice integration with Excel to export reports. Here is an example spreadsheet with a bunch of random values for the sale value and subjective likelihood to close

DOWNLOAD THE MONTECARLO FORECAST SPREADSHEET

What does it do? It extracts the pipeline data from salesforce and creates a list of the opportunities, their sales value and probability to close. A macro is then used to simulate 300 ‘possible outcomes’ i.e. in one outcome we may close on the first two opportunities and not the third, in a different outcome we may only close on one opportunity etc. From these simulated outcomes we then can pull out some stats; the expected sales figure, min, max and the variance i.e. possible spread of results.

Lots of eggs in different baskets: if we have 100 opportunities of $1k, each with a likelihood of 50% then it turns out that it is very likely we’ll close on at least $40k worth of business. The corollary is that it is also very unlikely that we’ll close on more than $60k of business. Like death and taxes, you have the inevitability of making a trade between risk and reward. Having a good estimate of the variance is very useful for planning especially if you need to watch cash flow. Also handy to keep in check the ever present cognitive bias of over confidence.

The graph above is a value-at-risk (VAR) chart for the case of 100 opportunities of $1k, each with a likelihood of 50%. The distribution shows that the most likely outcome is around the $50k mark, which is what the expected value should be. Most of the action happens in the $40-$60k region so you can use these as your upper and lower estimates when rolling up into a financial model.

In the example above it is pretty easy to calculate the exact expected return and variance. The Monte Carlo approach becomes useful in the real world case where the individual sales values and likelihood to close are all over the map. I’ve also found it useful because the integration sucks the real data out of salesforce seamlessly. In the example below there is a larger $40k opportunity with a likelihood of close of 70% grouped in with the smaller $1k opportunities; what we now see is a bimodal distribution, where depending on whether we close the big deal we are sitting on the lower or upper distribution – for the electronic engineers reading, there’s a sales ‘band gap’ J

Using this type of forecasting approach integrated with your real world CRM data helps figure out what the pipeline risk is more realistically. The big health warning is that you need good historically data on the likelihood to close, at different parts of your sales cycle and to apply consistently across a sales team, or you’ll end up with GIGO.

Pragmatic Problem Solving

There are many ways to come at a problem ranging from thorough analysis through to use of simple heuristics and rules of thumb. I always like it when people can get to an answer by looking at it slightly differently. I came across this one in Baeyer’s book, Information.

Samuel Morse, of Morse code fame, wanted to develop the most efficient way to code letters so they could be transmitted quickly. The principle of achieving this is pretty obvious; the most efficient code assigns short symbols to common letters, and long symbols to rare ones. He then had to answer the question what is common and what is rare? What is the order of the frequency with which letters appear in English? One way to gather such statistics is to select a text, and count the number of times each letter appears. This method works well for the three or four most common letters but it becomes less reliable for the more uncommon ones, such as Q, X, Z, unless the reference text is very long. Besides, who wants to count letters from a 1000 page book. Morse’s pragmatic solution was a lot quicker; he walked into a newspaper office and counted the number of letters in each compartment of the printers box. Presumably decades of experience had reduced its contents to an efficient compromise between supply and demand. Since he found more Es than any other letter, E is represented by a single dot, followed by T with a dash. X,Y and Z, on the other hand, whose compartments in the type box where relatively empty, drew four symbols each.

It is a fundamental part of the scientific method that all hypotheses and theories must be tested against observations of the natural world, rather than resting solely on a priori reasoning, intuition, or revelation

An Algorithm for Business Success?

It seems that testing is the flavour of the month in business these days. All the presentations I go to talk about A/B split testing and multivariate Taguchi methods. Of course the guiding principle of testing is a good one; but I think it gives some  the misguided notion that business is a purely deterministic process and that persistent testing  provides an algorithm for success (or quick, cheap failure, which is also good). There are some useful parallels between empiricism and its critics.

What am I actually testing? The process seems pretty simple; do A/B tests on your google ads, your landing pages, your email blasts, your automated workflows etc etc. Eke out success one word change at a time. How do you know you are isolating the one thing you want to test? How do you know you are not just locally optimising in totally the wrong place.

The empiricists and positivists thought the only source of knowledge is experience. It is a fundamental part of the scientific method that all hypotheses and theories must be tested against observations of the natural world, rather than resting solely on a priori reasoning, intuition, or revelation. Sounds reasonable. Quine illustrated problems with this view in the “Two Dogmas of Empiricism”. Quine argued for a holistic theory of testing; he thought that you cannot understand a particular thing without looking at its place in a larger whole. Holism about testing says that we cannot test a single hypothesis in isolation; instead we can only test complex networks of claims and assumptions. To test one claim you need to make assumptions about many other things e.g. measurement equipment, data quality etc. So whenever you think you are testing a single idea, what you are really testing is a long, complicated conjunction of statements. If a test has an unexpected result, then something in that conjunction is false, but the failure of the test itself does not tell you where the error is.

Take an example of ‘test the business model over a period of one year’, the background assumptions and  conjunction of interdependencies are legion. Two things can happen; you can say it doesn’t work when there is a simple element, which can be changed easily, in the web of dependencies that is the cause of failure  i.e. you get a false negative. A wrong pricing decision for example. You can also ‘forgive’ a fundamental problem by saying that something else in the chain is the cause i.e. a false positive. For any complex business decision the theory is always underdetermined by the available evidence i.e. there will always be a range of possible alternative theories compatible with the set of evidence. So what good is my test if it doesn’t tell us something definitive?

It didn’t work this time is different from it doesn’t work. People are also very keen with the notion of failing fast and failing cheap. Once again admirable but how do you know when you have failed? Karl Popper thought science progressed by a process of falsification; from the problem of induction you could never say that a general statement was true from a handful of observations but you could say the statement was false if an observation contradicted it. The issue of underdetermination rears its head again; you could never force someone to logical conclude that a theory was false because it may be a background assumption that is at fault. Falsification also struggles with probabilistic statements; take the example of proton decay – some grand unified theories predict that a proton should decay into new X bosons. During the 80′s there were a lot of experiments and they never saw a proton decay. They were able to put a lower limit of the proton half-life of 6.6×10^33 years but were not able  to say that it doesn’t decay. Most people may conclude that it doesn’t decay but the key thing is that they have to make a choice to believe so, it does not follow logically from observation. Doing a split test on a low volume search term feels a bit like waiting for proton decay.

Now take an example like James Dyson – he made 5,126 prototypes of his vacuum cleaner before hitting the big time. Why did he not declare that he had failed quickly and cheaply after the first 10 tries? Often it is difficult to know if you have the admirable quality of persistence or whether you are just a nutter.

Putting things to the test is a good idea but it only really works in a very well bounded context; most of the success stories come from web-based business that have a large enough user base to derive useful conclusions. For the majority of businesses there will be other things that matter a great deal more.  A business has a huge amount of knobs that you can turn, the only problem is that you can’t turn them all independently of each other. Basically I don’t think people should spend a lot of their time obsessing with analytics. Doing things intuitively has served a lot of people well for a very long time. If anyone can figure out how to do an A/B split test on the ‘cut of your jib’ please let me know.

It is a fundamental part of the scientific method that all hypotheses and theories must be tested against observations of the natural world, rather than resting solely on a priori reasoning, intuition, or revelation

Intuitive Bayesian methods for portfolio selection – Part II Bayes and Jeffrey

Bayes’ theorem in its common form describes the way in which one’s beliefs about observing ‘A’ are updated by having observed ‘B’. Bayes’ theorem relates the conditional and marginal probabilities of events A and B, where B has a non-vanishing probability.

Each term in Bayes’ theorem has a conventional name:

P(A) is the prior probability or marginal probability of A. It is “prior” in the sense that it does not take into account any information about B.

P(A|B) is the conditional probability of A, given B. It is also called the posterior probability because it is derived from or depends upon the specified value of B.

P(B|A) is the conditional probability of B given A.

P(B) is the prior or marginal probability of B, and acts as a normalizing constant.

Bayesian belief updating is the model we use for learning. We in effect already use it when we sit in meetings, discussing best options, as we will have individually modified belief over time as we receive new information – the problem is that it is difficult for others to see what evidence corroborates this belief, which opens up the door for our cognitive biases and simple heuristics.

Jeffrey’s Rule

During product selection and development we acquire and learn new information, which allows us to update our belief about how to make future investment. However, we know that some information is of a higher quality e.g. let’s say two people make exactly the same statement; one is a lead customer and the other is a stranger on the street, we know which is of a higher quality with a higher information content. Bayes’ rule relies on learning a definitive new truth to revise our belief. Most new knowledge we acquire during product development cannot be classed as definitively true e.g. one customer may say one thing and another may say something totally different. Jeffrey’ rule allows us to deal with opinion, rumor and weakly supporting evidence.

We can formulate a partition of hypothesis Ho and ~Ho

Ho = We will sell 10 products to customer x this year

We are at a trade show talking to a distributor who tells us he has heard that customer x is currently trialing our competitors products. We will call this new piece of evidence E

E = Customer x is currently trialing our competitors products

Before we had heard this we may have been quite bullish about the prospects of selling to customer x because we have had several meetings where they expressed interest and have been talking about using some demo equipment.

Pr(Ho)=0.8

However if it is true that customer x is currently trialing the competitor products then I figure that is bad news as they need to commit resource to testing and are further down the line with our competitors.

Pr(Ho/E)=0.1

If what I’ve heard is not true then I have no other reason to revise my prior belief

Pr(Ho/~E)=0.8

I represent my belief in light of the new rumor as Pr*, so that Pr*(Ho) stands for my belief in Ho in light of the new information E.

When talking to the distributor he can’t remember who he heard it from but is pretty sure that he is right. I might assign a probability that the information is right to 0.75.

Pr*(E)=0.75    Pr*(~E)=0.25

Jeffrey’s revision of Bayes’ rule is reminiscent of the rule for total probability

Pr*(Ho) =Pr(Ho/E)Pr*(E)+Pr(Ho/~E)Pr*(~E)

Jeffrey tells us to conclude that Pr*(Ho)=0.275. Before we heard the rumor we thought it was quite probable that we would sell to customer x, but things are looking a bit more bleak.

Dashboard representation

We can put together a dashboard that allows a user to start with a prior belief and update using Jeffrey’s rule. Two sliders are used to input Pr(Ho/E) and Pr*(E). The numeric inputs are augmented with descriptive labels.

Examples

If we receive a new piece of information that definitively refutes our hypothesis, but we know the source is completely unreliable then we would have no reason to update our belief e.g. if a stranger in the street says he wouldn’t buy our chemical detection equipment, this has no relevance or impact on my belief that the US Army will.

If we receive a new piece of information that we know is definitely true but is doesn’t add much to support our hypothesis then our posterior belief will be unchanged. For example, two people from one company tell me a piece of information separately. When I hear it from the first person I update my belief accordingly, when I hear it for the second time is gives me no new knowledge even though I believe the source completely.

Potential problems with the application of Jeffrey’s rule

Prior Belief

We can look at what happens if we start out with very different prior beliefs. If we are rationally updating with new evidence and agree on the impact and quality we should eventually converge on a common belief.

Evidence	Pr(Ho/~E)	Pr(Ho/E)	Pr*(E)	Updated
0	1.00	0.16	0.23	0.81
1	0.81	0.11	0.44	0.50
2	0.50	0.64	0.51	0.57
3	0.57	0.90	0.69	0.80
4	0.80	0.16	0.04	0.78
5	0.78	0.74	0.38	0.76
6	0.76	0.62	0.40	0.71

Table 1 Change in belief from a starting belief of 1

Evidence	Pr(Ho/~E)	Pr(Ho/E)	Pr*(E)	Updated
0	0.00	0.16	0.23	0.04
1	0.04	0.11	0.44	0.07
2	0.07	0.64	0.51	0.36
3	0.36	0.90	0.69	0.74
4	0.74	0.16	0.04	0.72
5	0.72	0.74	0.38	0.73
6	0.73	0.62	0.40	0.68

Table 2 change in belief from a starting belief of 0

The tables above and graph below illustrate the sequential application of Jeffrey’s rule. We start with differing prior beliefs and as new evidence arrives we update our belief. The dataset for Pr(Ho/E) and Pr*(E) are randomly generated number between 0 and 1. We can see that after 3-4 pieces of evidence we are starting to converge on a common belief. While not rigorous, inspection of simulated cases supports the idea that beliefs will converge irrespective of the staring belief.

Applying the principle of insufficient reason to prior belief

What happens if we start with no evidence at all for a hypothesis? We may be inclined to say that there is nothing to choose between the alternatives, true or false, so they should be treated as equally probable- this is the principle of insufficient reason or the principle of indifference. However we can look at a simple example; I state a hypothesis, “your car is red”. Initially without any evidence it doesn’t seem that the partition “your car is red” and “your car is not red” would have an equal probability.

In most business examples I can think of it is usually more likely for a specific hypothesis to be false; “this product will be successful” vs “this product will fail”. There are usually many more ways to fail than to be successful. We may be happy to assign a personal probability to the prior belief as opposed to assuming indifference. However this may allow certain hypothesis an ‘easy ride’ without forcing us to find evidence to corroborate or falsify. I prefer to operate the maxim ‘guilty until proven innocent’; assume the hypothesis is false until proven otherwise. This forces me to find evidence so I can justify my belief position – just because I think it is obvious that something is true doesn’t mean that others do. If I already have a high prior belief it should be easy for me to find the supporting evidence. This also means that I will be operating conservatively in the early stages as my belief is ‘dragged down’ by the memory of initial belief up to the point of convergence.

Order of discovery

It would also seem intuitively obvious that the order in which we uncover new evidence should make no difference to our eventual beliefs. We have generated 20 discrete pieces of evidence and updated belief at each stage. We have then reordered the evidence (re-sampling without replacement) and calculate the new belief trajectory. Interestingly we can have marked differences in belief at the end of the process. The results are presented without further discussion, but this may pose a significant problem in the application of this belief updating methodology.

The above re-sampling example assumes that we would actually assign the same ‘marginal belief change’ irrespective of the order of discovery. This may not be a valid assumption and we can look at an example from history. In 1818 Siméon Poisson deduced from Augustin Fresnel’s theory the necessity of a bright spot at the centre of the shadow of a circular opaque obstacle. With his counterintuitive result Poisson hoped to disprove the wave theory; however Dominique Arago experimentally verified the prediction and today the demonstration goes by the name “Poisson’s (or Arago’s) spot.” Since the spot occurs within the geometrical shadow, no particle theory of light could account for it, and its discovery in fact provided weighty evidence for the wave nature of light, much to Poisson’s chagrin. If I believed in the corpuscular theory of light I would be extremely surprised to see a Poisson spot. However once I have seen it and adjusted my belief accordingly, seeing it again would only have a very small impact on my belief; the new experiment contains very little information. This is the same as saying that the marginal belief change for a particular piece of evidence depends on my current belief and the history of how I arrived here. It doesn’t therefore seem valid to resample, as we deal with marginal change in belief, not absolute values as new evidence arrives.

Intuitive Bayesian methods for portfolio selection – Part I Background

Introduction

Disruptive platform technologies usually have a broad base of application. During early stage development, before there is a developed market, the selection of a particular product is usually a ‘high risk, low data’ decision. There are a large number of unknowns, both the known unknowns and the unknown unknowns; we seek the resolve these over time. In this type of situation it is difficult to make the initial portfolio selection decision and to effectively monitor the resolution of uncertainty, and determine the ultimate ‘chance of success’ for the product.

Problems in portfolio selection and project monitoring

The portfolio selection process, even when highly structured, often reduces to persuasion by advocates and champions. When a lot of data is being presented it is easy to forget ‘how we arrived’ at a particular position, assigning a higher importance to things that we heard recently (or long ago, depending on how your mind works). Soaring rhetoric can outweigh sober analysis and dispassionate appraisal of risk. It can be difficult to judge the ‘quality’ of a piece of information, which may find itself as a lynchpin in an argument to take a particular course of action. With a lot of unknowns it can be difficult to formulate go/no-go metrics and not relax the criteria when you get to the decision point.

Cognitive biases

The field of behavior economics examines some of the less rational beliefs of Homo economicus. Work by Tversky and Kahneman illustrate cases of overconfidence in our abilities, the desire to go with the herd and a propensity for rolling rationalization. Here is a list of cognitive biases that you can easily imagine arise in portfolio selection processes.

Objectives

1. Develop a simple methodology and toolset that allows us to :-
   
2. Reduce complex business decisions to specific and testable hypothesis, which can be definitively refuted.
   
3. Systematically revise our ‘belief’ in a hypothesis as we receive new information.
   
4. Integrate new information of many types and forms, of varying degrees of ‘quality’.
   
5. Maintain a history of how we arrived at a particular belief to provide an ‘audit trial’ or ‘memory’ to support future decisions and actions.
   
6. Integrate and logically connect hypothesis to create a ‘belief network’ that supports complex decision making.
   
7. Avoid cognitive biases and increase objectivity
   

Logic and Probability

There are three main modes of argument, deduction, induction and abduction (inference to best explanation IBE). Inductive logic analyses risky arguments using probability ideas. There are however different interpretations of what ‘a probability is’.

Frequentists talk about probabilities only when dealing with experiments that are random and well-defined. The probability of a random event denotes the relative frequency of occurrence of an experiment’s outcome, when repeating the experiment. Frequentists consider probability to be the relative frequency “in the long run” of outcomes.

Bayesians, however, assign probabilities to any statement whatsoever, even when no random process is involved. Probability, for a Bayesian, is a way to represent an individual’s degree of belief in a statement, given the evidence.

Logical Probability is thought of as a logical relation between a hypothesis and the evidence for it. J.M. Keynes and Rudolf Carnap both favored a logical theory of probability. Personal probabilities are a private matter, they are up to the individual and anything goes so long as be basic rules of coherency are obeyed. Logical probability maintains that there are uniquely correct, uniquely rational judgments of the probability of a hypothesis in the light of evidence.

For the purposes of decision making in a business context there are very few cases where a Frequentists approach can be used. We tend to use the Bayesian notion of probability where belief allows us to make investment decisions.

It is plausible to connect personal degrees of belief and personal betting rates

You would not pay more than $1 to win $2 on the flip of a coin. If you have some domain specific business knowledge that allows you to exploit an opportunity, your betting rate would be markedly different from someone without that knowledge. During product development as uncertainty is resolved our beliefs are updated and we revise the level of investment we are willing to make. People have always used this ‘managerial flexibility’ and there is now a move to formalize this type of ‘real option’ thinking in investment and portfolio selection.

Verificationism and Falsifiability

There are two common problems in portfolio decision making, how do we extrapolate experience to the future? And how can we provide definitive go/no-go criteria when we do not know the problem well? The former is the problem of induction, and is the question of whether inductive reasoning leads to truth. That is, what is the justification for presupposing that a sequence of events in the future will occur as it always has in the past (for example, that the laws of physics will hold as they have always been observed to hold). If we cannot assume uniformity of nature for physical laws we definitely cannot do so in a business context where we know that the landscape changes very quickly.

Often a go/no-go criteria is framed in a way that allows it to get out of jail down the line. A criteria such as, “show interest from a customer” is quite broad. If in a month’s time if we hear a statement “Fred and Jeff seem quite interested”, this adds practically no new useful knowledge upon which to base a decision – “A difference that makes no difference is no difference”. It also allows us to introduce an ad hoc revisions to ‘pass’ the criteria. If we set criteria such as “one sale made by the end of the quarter”, then we have something that is definitively testable. This is a criterion that puts itself at risk, which can be refuted or falsified – falsification adds new knowledge as it allows us to eliminate options and make definite investment decisions i.e. don’t invest. Falsifiability was put forward as solution to the problem of induction by Karl Popper.

This is related to the Logical Positivist view of the verifiability theory of meaning: the meaning of a sentence consists in its method of verification. In other words, if a sentence or statement has no possible method of verification, it has no meaning. It is pointless to make a go/no-go goal such as, “demonstrate our value proposition and facilitate end to end knowledge transfer”, as there is no possible way to test this and it therefore falls into the category of a nonsensical statement (also known as bullshit bingo).

Game of Life Encryption

I have posted a previous model of Conway’s Game of Life that runs in Excel – click here to view. I’ve just been reading Daniel Dennet’s book, Freedom Evolves, which uses examples of cellular automata to help describe emergent behaviour. There are some interesting ideas and I’ve been having a play with the Game of Life. For any given point in time the future state is completely determined given the cells that are currently alive and the transition rules of the game. I’ve seeded life with a set of random cells and watch life run over a hundred generations; what is clear is that given the end state there are different ways in which we could have arrived there. In other words I can’t just reverse time and the transition rules to get back to the original state.

One way functions, i.e. easy in one direction and hard in the other, are used to encrypt messages (multiplication vs factoring). We could use the initial state of automata and transition rules to encrypt messages in a computationally inexpensive manner.

Monty Hall -Again

If you ever want to get people of a mathematical bent shouting at each other you should try to get them to agree the solution to the Month Hall problem.

“Suppose you’re on a game show, and you’re given the choice of three doors: Behind one door is a car; behind the others, goats. You pick a door, say No. 1, and the host, who knows what’s behind the doors, opens another door, say No. 3, which has a goat. He then says to you, “Do you want to pick door No. 2?” Is it to your advantage to switch your choice?”

There are lots of ways to approach the problem; I’ve just heard a new one from Hans Christian von Baeyer’s book, Information, which should convince any die hard “it makes no difference if you stick or switch” people.

“Imagine there are not three but, but a thousand curtains, and one car. Initially you pick, say, number 815 with a resigned shrug – realising that your chances of success are one in a thousand. The host (who knows precisely where the car is) now opens 998 empty cubicles. Not the one you have picked and not cubicle number 137. Now he asks politely: ‘Do you want to stick with your first guess, curtain number 815, or switch to curtain number 137?’”. What should you do? By changing the degree it makes it a lot more intuitive.

 

Dan Dennett TED Lecture on Memes