Feb 7, 2024 | Analytics, Marketing
Every customer segmentation exercise I have ever seen is based on geography, demographics, some combination of behavioural characteristics, or all of the foregoing.
‘Young women, 25-35, single, who live in the Eastern suburbs, earn more than 80k, and eat out a lot’ sort of analysis.
Misses the point.
There are five types of customers in every business I have ever seen
Unhappy. These will often tell you and anyone else they can grab, of their unhappiness. Usually these are users, rather than the ones who make the purchase choice. This means they can be a fantastic source of improvement ideas, but can also consume lot of your time with things that cannot be changed.
Satisfied. When a customer is satisfied, they go away happy and you rarely hear from them. The more time you spend understanding the drivers of their satisfaction, and doubling down on them, the better.
Loyal. This group of people usually quite small will not go anywhere else and will generally pay premium to you in the knowledge that you will not fail them. In effect, it is in effect a risk mitigation strategy for them.
Apostles. Apostle customers these are generally small subsection of your loyal customers and occasionally just a satisfied customer when conditions are right who are prepared to aggressively push your case to others in their various networks. These people are your best salesman and also your cheapest, although there is a cost get him to getting them to the point where they will proselytise on your behalf
Cheapskates. The fifth type, the one you can probably do without, is the one who dips in and out of your product, chasing the cheapest price irrespective of other considerations. It also seems to me from experience, that they are also the ones who complain a lot.
Think about it.
I am prepared to bet there will be nuggets of value hiding in plain sight you can use.
Header credit: My thanks to the exiled Scott Adams, and sidekick, Dilbert.
Jan 17, 2024 | Analytics, Strategy
Imagine. Possibilities.
‘Strategic thinking’ has been overtaken by the ‘quants’.
Those that believe that by generating loads of data, analysing past events, behaviour, and outcomes, you can create a model that will give answers to the key strategic question: How best to deploy limited assets for the best return’?
Aristotle 2,500 years ago observed that in some things the past will always be the same as the future. Think about gravity. We know it will be there tomorrow exactly as it is today.
Your task in this case is to identify and quantify cause and effect.
Aristotle also observed that in other things it is not the case that what happened yesterday will be repeated today. In that case, you must form hypotheses, test them, learn, then rinse and repeat.
In other words, you need to imagine possibilities.
Look at the evolution on the mobile phone for evidence. On January 9, 2007, Steve Jobs officially announced the original iPhone. On January 10, 2007, despite luminaries like Steve Ballmer poking fun at it, all preconceptions about what a mobile phone was, were out the window. The past was not representative of what the future would look like.
The world is a messy place, today rarely looks like yesterday. In that messy place our task is not to look at the past and project onto the future, our task is to imagine possibilities.
Strategy development is all about imagining those possibilities, making choices on what appears to be the best bet, and putting your money down, adjusting as necessary as more information and insight are gathered.
Aristotle did not conceive the OODA loop. He left that to John ’40 second’ Boyd 2,500 years later, but it was inherent in the ‘scientific method’ he articulated, and should be required learning for every decision-maker.
Header is a representation of the ‘Johari Window’, made famous by Donald Rumsfeld
Dec 20, 2023 | Analytics, Marketing
This is an indulgence, but who cares, it is that time of the year.
I do not spend too much time worrying about numbers, this blog is my personal ‘journal’ of the stuff I am thinking about. If others get some benefit from that great, if not, nobody cares.
However, contrary to the above, there are some lessons for me in the numbers, and learning from the past, and improving is what it is all about.
The obvious skew in numbers that arises from posts early in the year having more time to gather readers than those posted later, has been ignored. Most posts see the vast majority of views in the first week or so, so timing should not be a huge influencer. However, there are a few exceptions to that rule.
Number 8 on the list is a post on the business model of supermarkets written in 2014. This has been in the top 10, usually the top 3 every year since. Number 7 is a thought starter on the budgeting process, that annually added job everyone except accountants hate, which was posted in January 2020. Every other post on the list is from 2023.
There are a few common characteristics of the top posts.
- Most promise a silver bullet of some sort in the headline. This may attract readers, but sadly, does not make the meat of the post any better. I can only hope that having been attracted, some might take some value out of the post.
- They are generally shorter than the average. This may reflect the focus and promise of the headline, or alternatively, I just did a better editing job.
- This characteristic is both a surprise and a worry to me. Apart from the two posts from previous years, and number 10 on the list, all have as a header a ‘Dilbert’ cartoon. Perhaps the presence of Dilbert is a strong motivator to readership? There was no intent here, and that correlation (or is it causation?) came as a complete surprise to me.
- Almost half the readers come from the subscription list, which is not big, about 35% from LinkedIn, and the balance from search engines, mostly from Google, but a surprising number from random engines. Readers come 70% from Australia, next biggest is the US, followed by (presumably) taxi drivers in Mumbai looking to emigrate, and a few from places I have to consult an Atlas (remember those) to find.
- Linkedin attracts a varying number on the platform, from a few to in some cases many thousands. The ‘views’ which misleadingly just counts the number of feeds a post has been shown in, bearing no relationship to being read, varies between a few, and many thousands. I only take account of the number of comments and reposts as an indicator of value, with a lesser value on ‘likes’. Linkedin discourages links leading off the platform by sticking offenders in ‘Linkedin gaol’, meaning they squeeze the algorithm so fewer people on the platform have the chance to see it. Suffice to say, I expect my gaol sentence to be ‘life’.
- As I run my eye down the full list, there is an increasing number of posts from previous years, some delivering very regular cadence of readership, years after publication. This is gratifying, and indicates that unlike a newspaper, a useful blog post is not just tomorrow’s fish wrapper. One that does continue to amuse is ‘Public Sector Flatulence’ published in 2013. It can go months without any readers, then suddenly, and suspiciously coincidental to some politicians brain-fart, it generates a bunch of views, and the odd comment.
For those interested, the list from top to number 10 is:
The simple choice marketers must make.
Plans never reflect what happens, so why bother?
The single key to great success.
Enduring culture change demands action.
The easiest and most effective way to build carbon emission compliance.
How to maximise the return from your investment in sales personnel.
5 Key factors to consider when planning your budgeting process.
3 essential pieces of the supermarket business model.
Equity or loans: The entrepreneurs funding dilemma.
The two key building blocks of strategy.
Thanks to all my readers, have a safe and merry Christmas, or whatever it is you celebrate (a valued friend is a Hindu, and Hindu’s traditionally marry on the last Sunday of the month. Guess what he and his wife of 30 years are celebrating)
Note: Given the number of links in the post, Linkedin will send me to their gaol for life, ensuring as few as possible casual lookers get to see the posts. So, please encourage those who might be interested to subscribe on the StrategyAudit site. That way they can continue to have the chance of seeing the outcomes of my addled musings.
Header courtesy of Dilbert, and Scott Adams, again. It just seemed right.
Aug 21, 2023 | Analytics, Management
Volume, flow, and capacity utilisation are drivers of each other, a symbiotic relationship articulated by Wrights Law.
A product with significant volume that is easy to schedule through a factory, and because of those volumes, has ‘well-oiled’ and efficient operational processes, generally delivers profit.
Years ago, I did a product profitability exercise on a range of products that were marketed by the company for which I worked at the time, Dairy Farmers Ltd. The parameters I used were based on the gross contribution to fixed overhead after promotional costs. These I calculated from the standard costing model being used at the time. Also weighted into the calculations were the complexity of the operational scheduling imposed by the products, and the ratio of gross contribution to the calculated capacity of the individual production lines, including downtime measures like machine availability.
The most profitable product in the range in both dollars and percentage was 300gm sour cream in cartons. It was a product that had significant and easily forecast volumes, so raw material procurement was simplified, predictable, the operational processes were ‘well-oiled’, and we had some pricing power in the supermarkets. There was very little wasted capacity or product failure in the manufacturing processes. Wrights law at work, and after a bit of thought, it made absolute sense that it would be the most profitable.
The second most profitable product in the range in percentage terms was a surprise to everyone, including me. A relatively small volume product, ‘Buttermilk’ in 600ml cartons. At the time there was no competitive product, so we had considerable pricing power, the volumes were highly predictable albeit modest, the ingredients simple and always available, and we could run the product immediately after sour cream with just a change in carton size which we could do ‘on the run’, the first few cartons acting as the ‘clean-up’ after the sour cream. There was effectively no downtime, no ‘start-up waste’ product, no added labour, few inventory costs, and no promotional costs. The capacity utilisation of buttermilk was virtually 100% of the capacity allocated by the arithmetic that combined volumes required and theoretical throughput rate and delivered margin.
Sadly for Dairy Farmers profitability, the supermarkets realised there was a profit pool they were not accessing. They introduced house brand products manufactured by a competitor who had idle capacity and took a marginal cost approach to the price at which they were prepared to sell the product to use that capacity. The volumes of both sour cream and buttermilk products fell quite quickly, while the operational costs per unit increased markedly.
Wright’s Law works in reverse as well.
The header is of Theodore Paul Wright 1895 – 1970
Aug 7, 2023 | Analytics, Leadership
We all need to become ‘knowledge workers’ say the pundits, who generally fail to define just what that term means, and how we achieve it.
Most would simply apply some added practical training and education, and bingo, knowledge, but I suspect it is more complicated than that.
Knowledge is way more than just education and training. It is also the wisdom of experience, domain familiarity, networks of people who can be called upon, and a capacity to make connections in non-obvious ways. It is intangible, as individuals, we have no physical stocks of knowledge, although we do now have relatively unlimited access to its sources.
The value of knowledge is also very hard to define, if not impossible, and it is not of much value when it stays in one place. Its value is highly contextual. It is of little obvious use having an expert in genetics when you are struggling with a problem of commercial governance. However, when you dig deep enough, you often find there are lessons to be learnt from other domains that can be applied, and in the process of digging, you learn.
The real value of knowledge is when it flows from one to another, and on to many, then, magically, it grows, evolves, and is put to uses not previously considered, creating even more value.
Therefore, the definition of a knowledge worker should be more like ‘Builds, shares, and leverages data for use beyond their domain’.
Improvements and alternatives encouraged.
Jul 12, 2023 | Analytics, Marketing, Operations
Anyone who reads my stuff on any sort of regular basis will know I have been deeply engaged with the potential impact of AI on all of us, since I stumbled across ChatGPT in early December last year. Of particular interest is the apparent potential for efficiency gains, particularly amongst the SME manufacturers I serve.
On one hand, I have been excited by the potential of AI to generate efficiency and expand the operational scale of SME’s. On the other, scared shitless at the potential for bad actors to sneak into our collective pockets and steal everything.
I need to write to think.
It forces me to sort out the stuff swimming around between my ears, as when I can articulate it sufficiently to write about it in some coherent manner, it leads to some level of understanding.
So, here is my list of the good stuff, followed by the bad, as it relates to the core of my business: strategy and marketing, starting with SME’s and the written word.
The good things AI can do for you.
- Summarising large blocks of copy, even when it seems very messy.
- Brainstorming; ideas, subject lines, complementary ideas, headlines.
- Editing and grammar. (I have been using the editing and ‘speak’ functionality of word for years, it is essential to me, and is AI that we now just treat as part of the furniture)
- Assembling descriptions and fact sheets
- Looking for logical holes in an argument
- Repurposing copy from one platform to another
- Research
- Outline and first draft.
- Translation and transcription
The stuff AI is no good at doing.
- Humour
- Reflecting current news and events
- Factual reliability. (Sometimes, it just makes stuff up)
- Finding a good metaphor
- Being creative. The great irony with creativity is that AI opens a whole new set of what is possible with visual tools, which can then combine with verbal cloning tools to completely alter apparent reality.
- Looking ahead
- Breaking complexity down to ‘first principles’
- Pouring another glass when faced by a blank page and a deadline.
Then there is all the other stuff AI will do, and evolve to do in the very near future, that is not writing. Graphic design, integrating currently separate digital systems (API’s on powerful steroids) identifying trends and holes in huge masses of data. The impact on medical technology is already profound. When the human genome was first successfully mapped in 2000, the cost of that first success was in the tens of billions of dollars. Now you can send away a sample and have it returned with your genome map for a few dollars overnight.
The key it seems, is to be very good at explaining to the tool what it is you want, in the detail a 5-year-old will understand. As the header cartoon illustrates, being human while driving this stuff will rapidly become the differentiator.
Header credit: GapingVoid.com.
