Oct 8, 2021 | Analytics, Management, Operations
We are all wary, in fact, usually very reluctant to put prices up, in case we lose customers. We ignore the sage advice of Warren Buffett who knows something about making a bob when he said: ‘If you have to go to a prayer meeting before raising your prices, you have a lousy business’
Increasing prices is a valid concern if two conditions are not met.
- You are undifferentiated in a way customers value
- You are in a commodity market.
There are five strategic drivers of price, the items that should be considered in your strategic thinking that delivers your pricing architecture:
- Your business model
- Price packaging
- Strategic priorities
- Market power
- Behavioural drivers.
Before you consider the actual price you will be charging, you need to have built the pricing architecture that best accommodates the dynamics at play in your market, and the price elasticity of demand.. Any pricing decision has two dimensions:
Strategic: The pricing architecture that is consistent over time, which provides the structure of your price list.
Tactical. Price can be moved around as necessary, while always remaining inside the pricing architecture.
Many just leave price decisions to the end, a grave mistake, as finding the Optimum Price, the one that leaves a minimum ‘on the table’ will have a profound impact on your profitability.
If you produce a simple spreadsheet, such as the one below, you will be able to model how the profit changes at various price assumptions. It is almost always the case, that to a point it is better to put your prices up and take a modest volume loss, than to drop prices hoping that the added volumes will deliver greater profit.
The assumptions in the chart:
- The Price we charge is entirely our decision.
- The volumes we forecast at any price point are the combination of experience, assumptions, and gut feel. They can be very tactical, varying time to time, and customer to customer in some circumstances.
- Cost of goods sold/unit and fixed costs are unchanged at any volume or price.
Developing a simple model is just maths and a range of assumptions, but we use it too infrequently. Our instinct is usually to drop prices in a crisis to preserve market share, rather than thinking about the impact on profit. If you have a gross margin of 40%, for every $1 you drop your price, you have to gather in $2.50 in added revenue to break even.
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Option 1 |
Option 2 |
Option 3 |
Option 4 |
| Price/unit |
$15 |
$18 |
$21 |
$25 |
| Quantity/period |
100 |
85 |
80 |
55 |
| GOGS/unit |
$6.0 |
$6.0 |
$6.0 |
$6.0 |
| Fixed costs/period |
$400 |
$400 |
$400 |
$400 |
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| The profit outcome of the various options can be seen below |
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| Revenue Price X Quantity |
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| Minus Total cost = ((COGS X Quantity) + fixed costs)) |
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| Equals Net profit |
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|
Option 1 |
Option 2 |
Option 3 |
Option 4 |
| Revenue |
$1,500 |
$1,530 |
$1,680 |
$1,375 |
| COGS |
$600 |
$510 |
$480 |
$330 |
| Total cost |
$1,000 |
$910 |
$880 |
$730 |
| Net Profit |
$500 |
$620 |
$800 |
$645 |
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| Breakeven point. |
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| Fixed costs/Unit Gross margin |
44 |
33 |
27 |
21 |
The break-even point also changes. This is one of the most under-rated but simple calculations available to businesses to gauge their financial health.
Whatever you do, there will be some for whom the price is too high and will not therefore buy.
There will be others for whom you are pricing below what they would have been prepared to pay.
Either way, you leave profitability on the table when you pick a single ‘Optimum price’ point.
This is represented by the left-hand graph in the header.
When you can have two price points, you tend to increase the profitability.
I.e., You drop one price below the ‘optimum’ single price, and pick up those ‘cheapskates’.
You have a second option with prices higher to capture those who are willing to pay the higher prices.
The challenge is, how do you effectively fence off the two, so you are not just delivering an extra reward to those prepared to pay the higher price, just to capture the cheapskates?
It is in the ‘Fencing’ that the creative strategic thinking must take place.
This is represented by the right-hand graph in the header.
The obvious example is economy airline seats. Every economy seat is almost identical, yet there are price fences based on time, and ticket flexibility. Book early, cheaper than in peak booking time. Book very late, you might get a very cheap price, or you might miss out altogether. This is in addition to loadings on location: aisle and window, forward and aft. This is also in addition to the fences that exist between economy, business and first class, which has similar demarcation, for time, as well as the premium to be there instead of cattle class.
A final thought. Many SME’s are not selling time, or input costs & materials, they are selling the results of knowledge and experience and the value they can deliver to their clients.
How do you put a price on experience?
We all have trouble with that, at least I do, and most people I have come across do also. There are three basic rules to follow as you consider how to price for a job.
- Price the client rather than the service. This means if you make them a million, shoot for a share of the outcome. This involves a ‘value conversation‘ early on. E.g., If I was able to deliver you added profit of 100k, how much would that be worth to you? This sets a benchmark, from which you can come to an arrangement. Remember, that a client asking you to do something for them is all about removing risk. You cannot offer guarantees with certainty, as there is always risk involved, but a bit of creativity can expose some useful ways to share the risk and reward. To quote Peter Drucker: ‘In business all profit comes from risk‘. Therefore, the answer to how much they are willing to pay would be tempered by the risk and reward to both parties.
A further example: A friend of mine is a hypnotherapist, and often helps smokers become non-smokers. The value conversation around her services should not be about the price/session, but by how many packets of cigarettes it was worth, in which case, success would mean an ROI in a couple of sessions. E.g., How much does a packet of ciggies cost? How many packets a week do you smoke? Quick mental arithmetic… that means that success here will save you $250/ month on cigarettes, and that is before you factor in the health benefits. What a great deal!!!
- Offer options. Where possible, offer more than one option at differing price points. A premium version, and one or more cheaper versions that have had some features removed. Think about the SAAS software options offered on the web. There are differing features listed for various price points, and it is always three.
- Anchor high. Three price options, start with the highest first, it acts as an ‘anchor’. This is opposite to what we do automatically, we tend to price low as it seems that will be more effective at closing, but the opposite is true. Price high, usually they will go in the middle. In addition, it is far easier to price high and give a discount than it is to price low and try to add features at an increased price.
None of this is easy, if it was, everyone would be doing it. However, it can be done with some creative thought, experience, domain knowledge, and good feedback mechanisms to enable ‘fine-tuning’
Note: Please excuse the dodgy graphs in the header. I am a strategic thinker, not a graphic artist! However, despite their dodgy state, I hope they convey the message.
Sep 15, 2021 | Analytics, Category, retail
‘Elasticity’ is something most of us did in economics 101. Why have we not used it more than the evidence of my eyes would suggest?
The price elasticity of demand is usually defined as the relationship between changes in price and the resulting changes in volumes sold.
Elasticity = % change in quantity/ % change in price.
For example, assume you raise the price of a widget from $100 to $120, which causes the volumes sold to go from 1,000 in each period to 900. The price increase is 20%, the volume decrease is 10%. Elasticity is therefore 10/20, or 0.5.
It is the absolute value of the metric that is important, the distance from zero, rather than if it is positive or negative. If the number of widgets sold had been 750 after the price increase, the elasticity would have been 1.25. (25/20) a more elastic response to the price increase than the 10% drop in the example.
It is crucial for marketers to understand the elasticity of their products if they are to optimise the price/volume relationship, as price is the most sensitive driver of profitability.
The challenge is that there are a whole bunch of psychological and competitive factors that weigh into the equation in a consumers mind, simply not accommodated by the simplistic price/volume curve we all saw in that economics 101 class.
You can speculate all you like about price elasticity, but the only way you will know is to evaluate it in the marketplace.
We are currently (September) in the season where there is a glut of avocadoes available. My local Coles store seems to be altering the prices daily, anywhere between 1.00 each to 1.69 each. It is probably that they are partly reflecting the deliveries into their distribution centres, but the data collected at the checkouts will give them a detailed view of the volumes at differing prices, and even the time of day. This data is invaluable market intelligence that can be used to optimise their profitability for the product category.
Given that cost is a lousy starting point upon which to base price, it may be that this Coles is leaving money on the table by reducing the prices below $1.49.
How many less avocadoes would be sold at $1.49 than at $1.10?
Someone in their data analysis system, somewhere, has the data to make this call with close to absolute certainty as it applies to this store.
Theoretical price research, outside of the real purchasing decision making, is at best inaccurate, at worst, misleading. A/B testing used to be a challenge, but increasingly we can use digital tools to interrogate the data that digital capture, in this case the checkout, that has become available to us.
Companies like Amazon with vast amounts of data are so good at it that they know the price elasticity of individuals in particular product categories. They display prices accordingly every time you search, in order to maximise the chance you will buy at the highest price they can charge, based on your history. ‘Dynamic pricing’ is the now common term being used to describe this process.
Once you understand the elasticity of the price/volume profile of your product, you are in a better position to maximise profitability, while delivering value to your customers.
Header cartoon credit: Scott Adams. Not sure the analogy is a great one, but the idea was amusing.
Jul 28, 2021 | Analytics, Management
Many if not most marketers, approach metrics that seek to increase their accountability with about the same enthusiasm they would approach a snake of unknown species in their backyard.
Warily.
The default has become a range of numbers that might look useful, are ‘saleable’ in the corner office, but usually do little to hold marketers genuinely accountable for the outcomes of the decisions they make.
The most common I have seen are:
- Vanity metrics. Typified by ‘likes’ or number of ‘friends’ on Facebook.
- Measuring what is easy to measure instead of measuring what is important, the drivers of outcomes.
- Measuring activity rather than results. This is endemic in publicly funded organisations.
- Measuring for efficiency rather than effectiveness. You can be highly efficient at doing exactly the wrong thing.
- Concentrating on cost rather than the return that the investment generates. This measure, as does the following one, infests organisations of all types.
- Measuring budget compliance.
Charles Goodhart, a professor at the London School of economics proposed what has become known as Goodhart’s law: ‘When a measure becomes a target, it ceases to be a good measure’
The implication is that you need two opposing measures that drive the outcome you are looking for to use as KPI’s.
For example: We all know that the best lead is one we get from a satisfied customer, a referral. Therefore, it is easy to set as an objective the number of referrals given. Unfortunately, this is very easy to ‘game’.
Sales people are able to just extract any old name from customers, to reach the number. Therefore, it follows that the KPI should be referrals that are converted into a sale. Better, that ensures that the referrals given are genuine. However, it is also flawed, by the simple fact that a conversion can happen for a number of reasons, including a below cost deal.
Therefore, the related KPI should be around the margin, or perhaps customer cash flow, something that reflects the profitability of converted referrals. This will ensure that the referrals are in fact worth having.
Developing KPI’s that are held across functions will improve the flow of information and resulting functional performance.
I refer to these as Tandem and Opposing KPI’s. For example:
- Sales people responsible for revenue should also be responsible for margin, but not for setting the prices beyond a proscribed band. Those who set the prices should also have margin as a KPI.
- Operations people responsible for efficient manufacturing should also be responsible for inventory levels and stock turn. This should connect manufacturing to market demand, and ensure some level of collaboration with sales to ensure stock availability.
- Those responsible for management accounting reporting and implementation, should also be responsible for reducing operational transaction costs.
Marketing is often accused of using garbage maths, fancy but meaningless clichés, and often they do. For credibility this must change.
It is not only marketing that overuses garbage metrics. It is just that marketing is an easier target than the accountants and engineers who have some numerical street cred and get away with it more often.
Having a simple set of cross functional metrics that go to the drivers of performance at any level, that are openly displayed, will be a huge step towards performance improvement.
Header cartoon credit: xkcd. https://xkcd.com/2295/
Jul 14, 2021 | Analytics, Management
The ‘Standard Error’ is another of those confusing statistical terms marketers need to understand. It is often confused with, and is as misunderstood as ‘Standard Deviation’. While they are related, and the Standard Deviation calculation is used in the calculation of the Standard Error, they tell entirely different stories.
The standard error calculates how accurate the mean of any sample from a population is likely to be, compared to the true mean of the total population.
An increase in the standard error means that the means of varying samples of data are spread out, so it becomes more unlikely that any mean of a sample will be an accurate reflection of the true population mean. The higher the standard error, the more spread out will be the population around the mean. Conversely, a low standard error indicates a closely distributed data set, and so is more likely to be representative of the population.
To continue the example in the earlier post explaining Standard Deviation. If you were planning to improve Sydney’s terrible road congestion, it would be valuable to know how representative of the total commuting population of Sydney the mean of your trips from Artarmon to the CBD of 30 minutes was.
To do that, you would do a wider study of the whole population, and calculate the mean, and standard deviation. You would then apply the Standard Error formula to calculate the standard error of the Artarmon sample, compared to the mean of the whole Sydney population.
The standard error is the standard deviation divided by the square root of the sample size. It therefore tells you the accuracy of a sample mean by measuring its variability from the known mean of the total sample.
Header illustration courtesy Wikipedia.
PS. I guess the government could have done such a exercise in parking lots, swimming pools, women’s change rooms, and all the rest. Perhaps they do not understand real statistics when disconnected from political statistics?
Jul 12, 2021 | Analytics, Management, Marketing
Marketers often hear the term ‘Standard Deviation’ during research debriefs, and conversations with operational personnel managing quality. Many do not know what the term means, and in what context to use it.
Standard Deviation is a statistical term that measures the variation in a set of values from the mean, or average of that set of values. The greater the standard deviation, the greater will be the spread of the data from its mean. In effect, it gives you a level of confidence in the conclusions drawn from the data.
Those values can be anything, from the time it takes for you to travel to work each day, to the variation in the size or weight of a widget coming off a production line, or indeed, from any individual part of that production line.
Take your pre-covid commute as an example. You live in Artarmon, 10km’s from your Sydney CBD office. The drive can take anything from 15 minutes to 110 minutes. If you recorded the time taken for a period, say 3 months, assuming you worked every weekday, you would have 130 data points of the time it took to make the commute, to and from work. Assume you took an average of those times, and it was 30 minutes. The Standard Deviation calculation ‘translated’ means that in 68.2% of the commutes, your travel time would be within one standard deviation of the mean of 30 minutes, and 95.4% of the commutes would be within two standard deviations of the mean of 30 minutes.
Let us assume the distribution of the data points led to a calculation of one standard deviation being 7 minutes. In other words, 68.2% of the time you would complete the trip between 23 and 37 minutes. That calculation also results in 2 standard deviations being 17 minutes, meaning that 95.4% of the time you made the commute between 47 and 13 minutes.
Those ‘outliers’ falling outside two standard deviations will be unusual situations. You went into work at 2.00am for a conference call overseas, and you got to the office in 10 minutes, and one morning, there was a ‘prang’ on the bridge, and it took over 2 hours to make the journey. These would be the journeys that made up the very unusual data points in the set, out at 3 standard deviations, within which 99.7% of journeys fell, or further.
This might seem a bit quantitative for many marketers, but if you are to be taken seriously in the boardroom, you need to be able to speak ‘Data’ the language of the boardroom. The typical marketing type assurances based on opinion and theory must be at least partly replaced by the quantitative language of the boardroom.
For those looking for a bit more, there are plenty of resources on the web, and there is a SD formula in Excel which leads you through the steps to do the calculation. However, in principle, the calculation has a few steps:
- Calculate the square of the differences between all the data points, and the mean, then add them up.
- Divide that sum by the sample size minus1, which gives you the variance. The variance is a statistical picture of how spread out the data points in the set are.
- Calculate the square root of the variance, to give the Standard deviation.
As a marketer, you do not have to know the formula, but you absolutely must understand what the term ‘standard deviation’ means, and where it is best used. It might be useful to ‘fiddle’ with the formula in Excel.
Header graph from Wikipedia.
May 31, 2021 | Analytics, Marketing, Social Media
‘Social Media Marketing’ has become a substitute in many people’s minds for ‘Marketing’.
It is sensible to have such a strategy, just as it is sensible to have an email marketing strategy, and a telephone marketing strategy, in the appropriate circumstances. However, to treat it as anything more than another tool in the marketer’s toolbox is to completely misunderstand the whole process of marketing.
Following is a reproduction of a note I sent after a long conversation with a potential client who runs a large function venue in a regional area. It all happened pre-covid, but it seems the sentiments were still valid, based on a similar conversation last week.
Thanks for taking the time to talk to me yesterday, you clearly have some challenging issues to be dealt with.
I suspect that the social media “brain-dump” over the phone I delivered yesterday may have been a little unclear, so I thought I would follow up with a few points that have consistently come through over the course of the work I have done in this space.
- To achieve anything at a cost that delivers leverage on your investment, you need a plan.
- A core part of that plan is establishing objectives for your activity, and in social media marketing the real objective should be to generate “leads”. Not sales, leads. Social media will not be effective directly selling a product such as yours. It can, however, be a very potent tool to identify and feed leads into a sales process that can be at least partially automated.
- There will be investment required in the process, particularly the development of the ‘content’ and messages you send, irrespective of the level of automation.
- The starting point to developing the messages as it should always be, is the definition of the value of the product you are selling to the receiver of the communication. This is the point where your mix will be challenging, as the wedding reception product you have will be different to the corporate function product, although held in the same room, just re-arranged, and with differing support services. Similarly, the person to whom you are marketing the wedding product will be different to the one likely to be the buyer of the corporate function. Defining all this is critically important, much more now in the time of social media because of its ability to deliver a specifically targeted series of messages to a well-defined individual potential buyer.
- You can develop metrics that will give you indications of the effectiveness and impact of your activity. However, the problem of attribution is a significant one. Which piece of content, or ‘marketing collateral’ was the driver of the move towards the objective of a sale? Any digital agency that tells you they have that absolutely nailed is dreaming. However, you do now have the opportunity to test all parts of the process in a multitude of ways and optimise over time.
- The nurturing requires a “toolbox” of content, aimed at the individuals inhabiting specific target markets that you are setting out to reach. Some of this content can be challenging to create, but once done, can be used, and re-used, improved, and used again for little cost, providing your investment with considerable leverage. In your case, you do not have to do everything at once, pick a market (like weddings) and create a few pieces of content, such as the “Guide to the big day” I suggested yesterday, together with a few supporting pieces such as photos of decoration options, flower seasonality guides, and checklists of the really little things that make a difference on the day. These will both alleviate the planning headaches of the wedding planner, and make your life easier by neutralising those last minute panics.
- Once you have some of this, you can utilise social media to target the buyer. For example, Facebook and Pinterest will probably work for the bride to be, but LinkedIn may be better for the corporate buyer. In corporate it is rarely the one signing the cheque that does the investigation into venue options. Having such targeted message recipients means you can get some useful measurements of the outcomes of your social media spend, that can be supported by some of the other media options you are already using. I am however, a great believer, based on the results of the years, of being able to create a “conversation” with potential customers via social media, but this is just an automated and ubiquitous version of the opportunities we have always had to communicate, as evidenced by this story which goes back many years that I related in a post back in 2013.
As a last word, it is really difficult to find people who genuinely understand all this stuff and can implement as well. There are many around who will promise the world, and deliver something entirely different, when they deliver anything beyond an invoice. However, if you are curious, and prepared to explore the options, much can be done very effectively, and the outcomes are measurable and cost accountable.
If it costs you $50, or even a couple of hundred dollars to find, nurture and convert a prospect for a wedding reception into a sale, is that a worthwhile investment? I suspect so.
Let me know if I can help you develop and implement a plan that will deliver a return on the investment you have made in a terrific venue. Just do not be seduced by the hyperbolic nonsense sprouted by many self-styled ‘Social media experts’
Header cartoon credit: Hugh McLeod at www.gapingvoid.com
