May 17, 2023 | Analytics, Management
The ‘Long tail’ is a graphic recognition of the Pareto principle, the 80/20 rule. It holds true in every situation I have ever seen. Rarely exactly 80/20, but always somewhere in the region.
We tend to accept it as a reflection of revenue and profit: ‘20% of our customers generate 80% of our revenue’.
Often, we manage our businesses, particularly the sales effort, as if this is the only place the principle works.
It applies equally to transaction costs, long term potential, management attention, geography, product class, customer type, and many other useful to know indicators.
Take for example, those customers that in 5 years’ time will be amongst your most profitable. Chances are, they are currently hiding somewhere in your long tail, denied the focus and assistance they might value that will assist them to grow in importance, simply because they are not seen. I call them ‘Strategically Important Customers.’ Unimportant now by most measures, but critically important in the long term.
Ignore these customers at your peril.
So, how do you find them?
- They meet the parameters of your ‘ideal customer.’
- They have a problem to which you have or are developing the ideal solution.
- Your share of their ‘wallet‘ is low when they meet other ‘ideal customer’ parameters.
Conversely, set your sales team to dig them out of your competitor’s long tail, deliver value to them, and convert.
An equally important task is to identify those customers who cost more to service than their current or potential profitability. The best thing you can do is send them to your competitor, so they can be saddled with the usually hidden transaction costs and low margins.
The profit and Loss statement is, or can be, a remarkably efficient way of capturing the information required to focus resources in the most optimised manner, dictated by your strategy. A P&L by customer, product, geography, market, and any other driver can be generated using readily available and relatively simple tools. The challenge is in overcoming the institutional definitions of how the data for the statements is collected, collated, and presented.
For example, what is an overhead, and how is it allocated?
In a factory, is the cost of supervisory staff allocated to individual product lines based on the actual costs, some rough ‘standard’ cost, or not allocated at all? Are those costs seen as overhead? Is the total overhead spread across total production by some magical formula devised by the accountants, or treated as a cost centre and managed proactively? What about those directly on the production line? Are their costs allocated in proportion to production volumes, customer offtake, or some mythical ‘absorption’ rate?
Take the time to ‘slice and dice’ your Profit and Loss statement. After having tackled the greater challenge of having the costs as they are actually incurred reflected in the customer P&L statement, you will be in a great position to take decisions that will have a significant impact on your overall profitability.
Feb 1, 2023 | Analytics, Governance, Strategy
Mark Zuckerburg has a lot to answer for, disrupting as he has the lives of my children. However, he is also very smart and rich, so being annoying must have something going for it.
When pitching the $5 billion Facebook float in 2012, Zuckerburg wrote to prospective shareholders via the prospectus, a letter that outlined his vision of what Facebook had become, and would continue to be.
This is to my mind the crucial paragraph, buried in the body of the letter.
“The Hacker Way is an approach to building that involves continuous improvement and iteration. Hackers believe that something can always be better, and that nothing is ever complete. They just have to go fix it — often in the face of people who say it’s impossible or are content with the status quo”
It now seems he has taken that perspective of his obsession to the world of virtual reality. He has invested billions of shareholder funds in his personal vision, triggering a loss of billions from the market value of Facebook, now Meta. He does not seem to care, but many other shareholders do. They must be getting very annoyed about now, the value of their shares dropping 70% from its peak 15 months ago.
At some point, businesses must develop stable, repeatable processes that just gets the mundane stuff done.
Facebook did that with remarkable efficiency for a long time, creating a river of cash. However, ‘hacking’ has taken hold.
Hacking to improve mundane processes should be part of the culture, so long as the experimentation is part of a managed process. The alternative to that discipline is chaos.
Mixing the cultures that accommodate the disciplined repeatable processes that get the bills paid, and the sometimes chaotic, creative environment of “hacking” is a function of the leadership of the enterprise.
Management needs to be “Loose” to accommodate the creativity and experimentation necessary for process improvement, while being “tight” to enable the learning that comes from experimentation to be incorporated into standard procedures when they prove to be an improvement.
Loose/tight management, is the environment in which “Hacking” Kaizen, or whatever you choose to call it thrives.
‘The Zuk’ has imposed his single minded obsession with hacking on the culturally poisonous monolith he created, because he can. If his VR vision becomes a reality, Meta share price will not only recover, but break all records. I do not expect that at any time soon, particularly if as rumoured, Apple comes out with their version. Meta now faces a governance challenge that could be a real game-changer.
Addendum February 4, 2023.
This article from the Statista website details the progression of losses Meta has booked on Zuks metaverse bet. $US13.7 Billion in 2022, on an increasing trend. While the share price has dropped dramatically, if you look at the PE ratios before and after the drop, it seems to me that the price is settling back to where an old fashioned investor, one who expected a return from dividends rather than capital growth on the basis of a never ending share price increase, might expect it to be. The same comment can be applied to many other digital pletform stock price drops over the last year or so. Fundamentals kicking in??
Addendum 2 February 5, 2023.
They are coming thick and fast!. I read this ‘Wired’ article by the brilliant Cory Doctorow this morning. It explicitly defines the life cycle of social platforms, something we all ‘sort of’ knew but dismissed in favour of the value for early adopters, progressively locking in users, at the same time they squeezed the algorithms to generate ad revenue. Doctorow calls it ‘Enshittification’, a lovely word. Towards the end of the article is a quote from a very young Zuckerberg ”I don’t know why tney trust me, Dumb fucks’. Here is the news Zuk, we don’t!!
Nov 23, 2022 | Analytics, Strategy
Strategy is a bit like economics, go to 5 so called strategists, and you will get 6 opinions.
This is terminally annoying to our accounting and engineering friends who thrive on certainty. However, it is perfectly OK, as we are dealing with the future, and that rarely turns out to be what we think it should be.
The challenge is a Bayesian one.
Over time becoming incrementally less wrong.
Good strategy enables the pace of that Bayesian improvement to be accelerated, sometimes by a geometric proportion.
Strategy generation is a process, it is about creating the future. It has not happened yet, so cannot be ‘proved’ in any definitive manner, until you have the outcomes to count. By that time, it is too late to do anything but adjust and learn for the next time. This does not imply wholesale change, which only emerges from poor strategy in the first place. By contrast, good strategy enables subtle adjustments to be made over time while the direction holds firm.
This makes strategy generation a series of choices powered by an assessment of the relative odds of varying outcomes emerging.
Over the years I have whinged about the mediocre quality of many marketing people I have come across, intellectual dwarfs that fall into ‘marketing’ because they failed to make the grade at something useful.
It is ironic then that almost without exception, the best marketers I have worked with, and for, have found themselves in marketing after becoming tired of the restrictions placed on more externally disciplined professions: accountants (which is where I originated) lawyers, scientists, and medicine.
The combination of the automatic discipline of the scientific method with the creative thinking based on quality data required in marketing and strategy is a potent combination indeed.
Header cartoon: courtesy, again, of Dilbert and his mate Scott Adams.
Oct 19, 2022 | Analytics, Operations
No matter your businesses size, digital capability has become a driver of commercial sustainability over the last decade.
It has become a clear case of digitise or die.
This does not mean you have to go from an analogue starting point to fully digitised in one step, that is unrealistic. However, failure to start the digitisation journey will eventually be the undoing of your business.
There are a number of logical steps you can take that will build capability quickly, without massive investment, although some investment, particularly of management commitment, is necessary. However, like any investment, you should expect a return.
If you are starting the journey, the following is one set of the steps you might expect to mount, not necessarily in this order, but this is a common pattern I have seen.
Step 1. Assemble a clear picture of the currently available data. Mostly this will be ad hoc, and manually collected. Machinery purchased over the last few years will have the facility to capture data that is often unused, or under-utilised. This might simply require some connection between the data logger in the gear to your server, or better still, to a cloud application.
Step 2. Build a common system for the assembly of data that will enable it to be analysed in a consistent manner. Many factors have differing sets of ‘data languages’ based on legacy practices, and short-term convenience. Creating a common data language is important, and the best tool for doing this are to map all the processes in the factory, and break them into what is in lean parlance, ‘value streams’. The languages can then be tailored to make sense to all who meet them.
Step 3. Invest in further data capture. In the early stages, this is often a case of retro fitting devices onto existing machinery and downloading it all into a common data base. Depending on your operations this can be as simple as excel. There are many available low level options that are of a modular design, so that as capability grows, the modules can be implemented progressively.
Step 4. Invest in the capability to analyse the data and turn it into actionable insights. It is at this point that people become invaluable to the system. Any digital system can only respond to inputs in the way they have been instructed. They are no good at assessing the inputs for which there has been no or little precedent, you need people for those vital tasks.
Step 5. progressively implement data generation and analysis to inform operations. Use the feedback to constantly improve the quality of the data and the analysis that is used to manage and improve operations.
Step 6. Rinse and repeat. Digitisation is not a task with a completion date, it is a journey without an end.
As I headed towards the ‘publish’ button, a notification of a new program by the Victorian government popped into my inbox. The ‘Digital jobs for Manufacturing‘ program will fund training of employees of eligible Victorian manufacturers in a 12 week part time course run by Victorian universities. Have a look.
Header credit: Tom Gauld who takes an ironic, but widely felt frustration felt by SME’s at digitisation at www.tomgauld.com
Oct 14, 2022 | Analytics, Innovation
We set out to measure things, to give us a sense of achievement, to allocate priorities, and simply to keep score as we proceed. It is an engineering perspective.
We do not have any way of objectively measuring how we feel, but how we feel is what drives our behaviour.
This would not matter if we all perceived the world objectively, but we don’t. We observe the world through the complex frosted window of our experience, context, opinions, and did we get our coffee this morning.
So, how do we dig away at this problem, and it is a problem, simply because we use objective means to make subjective decisions, and it sucks.
We ask better questions, and we ask those questions from as wide a variety of perspectives as we can. We must ask those better questions, and experiment, test stuff, be allowed to fail, as in the outliers you will find the unexpected. However, you must also expect the unexpected, you just cannot predict where it will be.
Bees, amazing insects that they are, have the process nailed. They have a behavioural characteristic scientists call the ‘Waggle dance’, which is a communication medium that leads others to the source of nectar. Bees must find nectar, that is the job on which their lives rely.
Depending on variables like the weather, location, season, and others, a percentage of bees, 10 – 20% ignore the waggle dance, and go off in a different direction. When they find a new source, they start the ‘waggle dance’ to attract other bees to that new source, thus keeping the hive healthy and well fed.
This is experimentation, sacrificing a small part of the current returns to build for the future.
As we consider how to best allocate our available resources, we can learn from bees.
Bees in blogs
Oct 4, 2022 | Analytics, Management, Operations
Too often KPI’s are all about the result, rather than the drivers that will deliver the result. When you are measuring just the outcomes, you have missed the opportunity to improve and optimise the actions that will lead to change the outcome being delivered.
Take for example the Cash Conversion Cycle (CCC) time. This measure is a fundamental tool in the improvement toolkit.
By improving the rate at which the cash outlaid to generate a customer service or product is turned back into cash by the payment of invoices, you reduce the amount of working capital required to keep the doors open.
The real benefit is to be found in the active management of the drivers of the CCC. The days taken to complete the cycle is just tracking the result of a set of actions that take place elsewhere, Manage the inputs, and the days will reduce.
For example, detailed examination of the debtors ledger will tell you which customers are slow to pay, thereby decreasing the speed of the cycle. It then becomes an item of potential improvement. Perhaps the salesperson responsible is not diligent enough, perhaps your collection processes are not explicit, you lack follow up, or clarity on your terms. In the end, perhaps you can decide that that a specific customer should be handed over to one of your competitors, weakening their cash position.
The same analysis becomes second nature around the inventory numbers: raw materials, WIP and finished goods. Improving those numbers without hurting the levels of customer service can dramatically improve the productivity of the investments made in the enterprise. As an added benefit, customers will thank you and give you more business.
As with anything, the absence of the information that details the drivers of an outcome, makes it hard to make improvements.
Another example. Sales personnel are often compensated by commissions on their total sales. If you want your salespeople to be out hunting for the next customer, rather than glad-handing existing ones, paying a commission on all sales is a poor strategy. It discourages the more time consuming and riskier task of finding and converting new customers. Existing customers in most cases can be professionally managed by an internal customer service function. The better use of commissions might be to encourage business development.
When you spend time identifying and managing the drivers of outcomes, the dollars will follow.
