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
Apr 23, 2021 | Analytics, Management
Every plan I have ever seen, business plan, strategic plan, house plan, office layout plan, is made up of a set of assumptions about the future.
To varying degrees these assumptions are based on two sorts of ‘facts’. These so called ‘facts’ are not accurate reflections of reality, but expectations with varying degrees of validity. They seem to fall into two categories:
-
-
- ‘Facts’ about the future, often distorted by perspective, misunderstanding, incomplete information, and a host of other variables.
- ‘Facts’ that are really just an extension of the status quo extended into the future.
These ‘facts’ are then fed into a process of some sort and used to develop a plan in the mistaken belief that the future will look little different to the past.
Therefore, the first responsibility of anyone doing a plan is to find a mechanism to test the key assumptions in the plan, and adjust as necessary.
Failure to test the key assumptions, which are the drivers of the performance of the plan when implemented, is the best way I know to really stuff it up.
Having a plan that does not reflect reality ensures either: that every decision that will be made during implementation is suboptimal, leading to poor outcomes, or that the plan is discarded, and normal chaos resumed.
I am never sure which is the worse outcome.
Apr 16, 2021 | Analytics, Change, Operations
‘Digitisation’ like many other ‘ation’ words has become a cliché, thrown around with no specific meaning that is consistent and generally understood.
It has many parts, ‘Industry 4.0’, IoT, AI, AR, and so on, but what do you have to do to ‘Digitise’? It is way more than upgrading your ERP and CRM systems, it requires wholesale change from the way most businesses have evolved.
Following is a partial list, gleaned from those with whom I work, and the experience that has come from those interactions.
Have a goal. Like any journey, digitisation is nothing without a goal, something to work towards and measure progress against.
Leaders walk the walk. Again, generic advice for any behaviour you want to see in an organisation, it will be absent unless the leadership displays it. An enterprise that aspires to ‘digitise’ when the leadership stubbornly refuses to digitise themselves, will not see much progress down the ranks.
Recognise digital is a culture, not a set of tools. Tweaking current business models and tool sets will not be enough, there needs to be a change in the way the enterprise engages with the world and manages itself.
Customers first. Success has always come to those who put customers first, but it has never been as apparent and such a source of competitive advantage as it is now. When a customer can actually see you putting them first, or not, they are able to make quick choices. They will either become your extended marketing team, or if not happy with you, potentially do a lot of damage.
Do not adapt, adopt. Adding bits on, making a hybrid, as you would when you extend your house, will not work. You must design the digital experience inside and out from the ground up with the objective as the guiding light.
Employee power. We are talking about harnessing the intellectual power and motivation of stakeholders, and particularly employees in this exercise, without whom, it is no better than window dressing. Empowering employees is a core part of the culture change required; they go hand in hand.
Collaboration and co-creation. Progress is increasingly being achieved by ecosystems, rather than enterprises on their own. Figuring out how you collaborate to compete is necessary.
Kill the legacy. Legacy systems only hold you back, you must be prepared to move them on as you would an old piece of equipment in the factory. Often legacy systems work well, you are comfortable with them, but they no longer offer the key ingredient to digitisation, the ability to communicate with other systems and deliver useable, leverageable data.
Make it measurable. As in any project, being able to measure progress towards the goal, ensures resources are allocated appropriately, and that accountabilities are clear is essential to progress.
None of this is easy. Anyone who tells you it is has never done it. It is however essential, and like everything that is new, it pays to take small steps first, gain some confidence, understand better the costs and benefits, find some skilled help, and keep moving forward.
Header cartoon credit: Dilbert once again delivers enduring wisdom.
