Category: Mental Models

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Why brand awareness is the most important thing for an organisation now

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Things sell themselves these days.

Whether we’re talking about placing products in front of consumers, or trying to persuade others to adopt a particular strategy in an organisation, the point at which we reach them is crucial.

The traditional approach of a funnel, where we go through defined stages is starting to show its age – because it can’t cope with the idea that consumers may know as much, if not more than product and service providers.

Take recruitment, for example.

For a long time, the only way for a person to understand what it was like working for a company was to ask friends and family who worked there, or apply for a position and spend some time working there.

So, they entered a funnel – experiencing the recruitment process, negotiating salaries, starting work, mixing with their colleagues, understanding the hierarchy and so on.

Now – they have access to much more information on the working experience at a company – especially if it’s a large one.

For example, Glassdoor has 5,021 review of Barclays, 8.596 salaries and 1,920 interviews with employees.

A prospective employee looking there will know more about how the company treats its staff than almost anyone else internally, especially the top management.

The democratisation of information has levelled the playing field in every aspect of organisational interaction.

Most service and product providers understand their products in detail, but spend less time comparing themselves with others than potential consumers.

The consumers therefore are more likely to have a better understanding of the market and trends and the differences between brands, just through the basic research they do before engaging with providers, than the brands do themselves.

This change in the way of how consumers interact with products and services has been called the customer decision journey by McKinsey.

In this model, consumers start with an initial consideration set, a collection of brands that they are aware of and may have been exposed to recently.

They then get information from a wide variety of sources – internet reviews, personal recommendations, traditional media – which all contribute to an active evaluation of their options. At some point, they reach a moment of purchase, where they decide to go with a particular option.

According to the customer decision journey model, this is where the hard work begins.

The postpurchase experience then shapes success or failure.

Many people, once they make a decision, experience a degree of anxiety.

The first thing they then do is to go online and check that they have made the right decision – looking for reassurance from others in the same position.

This works, sometimes, as they get more information, realise that they are with a good brand and are reassured. On the other hand, they may see more information from competitors that show them what the alternatives might be.

The trigger for entering a loyalty loop and making follow on purchases depends then on the quality of what they get and their ongoing assessments of the options open to them.

This continuing change in the way consumers make decisions is changing everything from sales to recruitment in an organisation.

And the starting point – the thing that one must do to even play the game – is to be included in the initial consideration set.

And that means that the potential consumer needs to be aware that a particular organisation exists – it needs to be discoverable.

Which brings us back to the importance of brand awareness: why it matters so much now, and why it will become even more important in the years ahead.

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When will a new app or IT solution benefit our work?

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For many of us, a standard work IT package consisted of a laptop or desktop and a Microsoft Office suite.

That has let us do most things for a number of years, and remains a solid foundation for the kind of day-to-day office work we need to complete.

In most functions, however, the number of tool options are exploding.

Take the graphic below from Scott Brinkler who writes the Chief Marketing Technologist Blog. It shows the marketing technology landscape where we can now choose from 5,381 solutions from 4,891 unique companies – and has grown from around 150 to 5000+ in six years.

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A similar transformation, although less dramatic, is happening across other organisational functions.

What are the things that managers who are tasked with investing in tools for users that need information systems and tool developers trying to develop such tools need to keep in mind – given the inevitable competition they will face?

One approach comes from a paper by Mark T. Dishaw and Diane M. Strong that puts forward an integrated model that tries to explain the likelihood of a tool actually being used.

It combines two existing models, and the new, extended version is more effective than either alone.

First, the Technology Acceptance Model (TAM) suggests that actual tool use depends strongly on an intention to use the tool.

The strength of the intention depends on the user’s attitude towards use, which in turn is a result of his or her perception of how useful the tool might be and how easy it is to use.

The Task-Technology Fit Model (TTF) focuses instead on the ability of technology to support a task – and matches the technology to what the task demands.

This fit depends on the tool functionality and the task characteristics – and suggests that a rational assessment that matches functions to tasks will result in the best choice of solutions.

The TAM depends on perception and attitude while the TTF focuses on rationality and comparison.

In reality, we use both, and the extended model, shown in adapted form in the picture above, is an integrated model that selects from parts of the TAM and TTF and connects them with the hypotheses set out in the paper.

It turns out that this extended model explains more about actual tool use than either model on its own.

How can we use this when selecting and implementing a technology stack in our organisations?

To start with, this model gives us an approach to scope what is required in terms of both technical capability and user-centric capacity.

All too often we select a package based on the sales pitch and technical functionality, forgetting that the value it will add depends on how the people in our organisation use it – and they will default to using systems they find easy and useful.

And the quality of our choice will show up in the statistics of actual tool use.

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How do we search for information in a data world?

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The first thing we often do now when confronted with a question is to search.

We open a browser and go to Google and get going.

The proliferation of smart devices and AI assistants like Alexa and Siri will only intensify this approach – we can no longer hope to know everything because there is just too much out there.

This is well understood when it comes to marketing – experienced internet marketers know that they need to study search histories and trends and design content that addresses the way in which people search for content.

We are starting to see this in documentation and support pages for some companies, where instead of browsing through a list of topics we are encouraged instead to search or ask a question and the system tries to answer it or find relevant information.

It also has ramifications when it comes to training – the traditional classroom based approach to professional development can give employees an overview, but the individual challenges they face in their work are usually addressed through a search.

So, is it useful to have a simple model of the purposeful activity that people go through when searching?

Possibly – and that is what is shown in the picture above.

This is a representation of Marcia J. Bates’ 1989 berrypicking model of searching online.

In this model, the user might start with just one feature of a situation, to create a search query.

For example, this post started with the search query model of information search online in order to investigate current models out there.

That led to a paper which set out a number of models, from bibliographic or directory based approaches to linear models where the researcher moves systematically from a vague understanding to a focused one.

Looking at the variety of sources, the one that stood out, however, was Bates’ berrypicking model, because it matched how we do things now.

So that led to modifying the query to refine and gather more information on the berrypicking model, until a satisfactory completion point meant that the model could be expressed in the form of an human activity model, as in the picture and accompanying explanatory text.

So, why is having such a model in mind useful – and why is it any more useful than simply following a standard marketing approach of following a checklist and looking for Google keywords.

The key reason is that having the model in mind allows us to better organise the learning process associated with the creation and presentation of information online.

We can ask ourselves whether we have selected the key features that matter from the user’s perspective?

We can come up with search queries and match them against search engine data, especially around long tail searches.

We can also compare how our content matches up against other content from a variety of sources and come up with a plan to modify or improve what we are doing.

Finally, we have little control over when a user feels satisfied, but we can aim towards helping them move towards that point with well designed material.

Most organisations will find that they are in the middle of a transition to a user-centric, search based and information rich world.

Focusing on how users search will be essential for how businesses stay in business in this future.

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What’s the difference between a plan and a novel?

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We all make plans – from planning a trip to setting out a marketing strategy for the next five years.

There are many moving parts in a business environment – from internal capabilities, dynamics and politics to external influences like regulation and social transformation.

What differentiates one kind of plan in such a dynamic environment from another?

Is it success – if a plan succeeds is it a good one?

Or is there something about the nature of the plan itself – does a good plan have a particular architecture?

Take marketing on the internet, for example.

Search engine optimisation or SEO is something that many people try and sell – ways to manipulate search engines into ranking sites above others – and it was popular in the early days of the web.

So, is that something that we should do now?

Looking at Google trends – perhaps not. Interest in the term has steadily dropped over time as search engines have become smarter, using semantic analysis and tracking to provide hyper-personalised content.

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SEO these days is more about making it easier for the engines to understand what we do rather than tricking them.

A plan based on taking a shortcut or tricking the system may work for a short while.

It can then form the basis of a story, which can be used to sell the plan to others – but it’s unlikely that the results will be reproducible to the same extent.

And that’s the operative word – what distinguishes a plan from a novel is that the former is designed to produce reproducible results while the latter is a narrative of what once was and what might be.

The picture above is from Peter Checkland and Sue Holwell’s book Information, Systems and Information Systems: Making Sense of the field and sets out the basic architecture of a research process – something that can be generalised to much of business.

We start with a framework of ideas – things we believe about our environment.

Taking the marketing process again as an example – these might include the importance of video, a belief in the process by which concepts go viral and the extent to which elements of the work can be outsourced.

The standard process is to them come up with a strategy, a plan and then apply it to an area of concern A – the sales process or pipeline management.

Directors set targets, managers review progress and the standard process rumbles on. Perhaps they hit targets, perhaps not – that’s not really important.

What’s important is the bits that are missing.

A set of ideas by themselves are just opinions. What makes them useful is setting out, in advance, the methodology that underpins our strategy and plans.

Our objective is to cause a change for the better in the area of concern that we are looking at.

Continuing with the marketing example – we may be able to see and measure the change in a metric like sales – but that is an output of the process.

What’s important is that having the intellectual structure in place – a framework F, methodology M and an application area A – then lets us reflect and learn about the system we have put in place.

It’s the learning that matters – and that’s what helps us adjust and refine our plan and create an approach that produces predictable results.

And this is perhaps the biggest thing we miss when we think about a plan in the narrow sense of having a goal or target and define success as hitting it and failure as missing it.

The journey we take and the lessons we learn are just as important – if not more so.

We often learn more from failure than we do from success.

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Why goals and control are not enough for business and society

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We have been conditioned for a long time to think that setting goals is the way to achieve success.

This may partly be due to the work of Herbert A. Simon, a Nobel prize winning economist who pioneered work in goal-seeking, which spawned fields such as artificial intelligence, decision science and complex systems.

This kind of thinking leads to the unquestioned assumption that the way to make something better is to throw technology at it – a very common theme at present in the energy world.

We think energy markets aren’t working, so the way to make them work is to implement blockchain, AI, machine learning, comparison engines and other types of solutions – which will magically transform it into a clean, lean machine.

Except it doesn’t work that way.

A countering approach comes from the work of Geoffrey Vickers who came up with the notion of appreciative systems.

He argued that ways in which we often thought about the world were inadequate.

The goal-seeking method leads to a narrow reductionist view.

An alternative – the cybernetic view, where there are controllers and actors and one controls the other doesn’t really exist in reality.

Take for example a prison guard and a prisoner. While one is behind bars – both are in prison – and we know how the environment can quickly turn good people bad.

Vicker’s approach is one where life is experienced as a flux of events and ideas – brought out in the picture above from Checkland.

Imagine a loud, raucous party. You arrive, having been invited. You meet a few people, get to know more. Over time, you make friends, have conversations, even throw your own mini-parties in a corner of the room. Then you leave – but the party carries on.

That’s pretty much how life works.

Appreciating the world, or life, then means perceiving it in the first place and making judgements about the things we see.

Those judgements are usually about fact – what we believe is – and value – or what is good.

Given our perceptions and judgement, we can envision what might be and take action.

And we do all this not to meet goals, as a rationalist approach might assume, but to maintain relationships – our place and friendships at the party, if you will.

All this activity results in standards – our expectations of fact and value.

What needs to be seen is that our previous experience results in standards which are then modified in the light of future experience.

At a very basic level, this is what happens when companies become more diverse – the introduction of new thoughts and approaches from a greater range of individuals can change our standards.

A few years ago, no one would have questioned mostly male panels. Now it would be a brave organiser that didn’t have any women up at all.

Why does thinking about any of this matter?

It’s easy to be cowed by what seems like the unstoppable march of technological progress – the bots are going to take our jobs and there will be nothing left for humans to do.

Except to be human – appreciate life as it is and aim for better standards and relationships in business and society.

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What does it mean when an organisation has a social purpose?

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Organisations are changing and the boundaries between them are getting blurry.

Once upon a time it was simple. The government did some things. Profit driven companies did other things. And non-profits picked up the pieces.

The regulatory structures in many countries grew up to support organisations that fell into one of these three categories.

But, that’s not enough for people any more – they want to work with organisations that do more than just make money – that have a social purpose.

But what does that mean exactly?

In this BCG article, Cathy Carlisi and Dolly Meese from Brighthouse define purpose as the why of an organisation, resulting from the intersection of two questions:

  • Who are we?
  • What need do we meet in society?

Does this become a social purpose if we just add the word social to it?

Not according to the Advertising Standards Agency, which ruled that A4E, now known as People Plus, could not describe itself as a social purpose company because its activities made a profit but people could mistake it for a non-profit.

So, while leaders in organisations are trying to make their businesses about more than just money, the system of regulation and oversight is trying to understand what this means and how it should respond.

In the U.S, the concept of a for-benefit organisation is being mooted, one that makes a profit and acts like a normal business, but whose primary purpose is provide social benefits.

The normal way to get this message across is through marketing – by structuring branding and messaging around concepts like “social enterprise” and “sustainable business” according to this article in the Harvard Business Review.

But, the article argues, it can also be achieved through organisational architecture – by creating a set of rules and operating principles that go beyond profit and involve suppliers and customers in decision making and even profit sharing.

A report by the Mission Alignment Working Group of the G8 looked at a new form of organisation called profit-with-purpose businesses – a type of organisation that has the freedom to distribute profits like a traditional business but also commits to prioritise, deliver and report on their social impact.

They also propose a way for these organisations to become formally recognised in law – with a definition, legal framework and operating model.

So… it’s not that easy to understand social purpose – the words make sense, but what does it really mean when an organisation starts to focus on the impact it is making rather than the profit it is taking?

The starting point is getting the internal and external narrative right – the story we tell ourselves and others.

And we can start by answering a few fundamental questions.

Who are we, what need do we meet and why do we exist?

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How to take your company digital

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Amazon is ruining things for many businesses – teaching customers that they can expect to get products and services quickly, have a great user experience, no errors, 24-7 availability and personalised interfaces – and save money and time.

What about everyone else? How should they think about transforming their organisations to stay competitive?

Tunde Olanrewaju and Kate Smaje from McKinsey set out seven traits in this article that they have discovered effective digital enterprises share – and that we can use as a blueprint for our own programmes.

Going digital is less evolution and more reinvention.

We need to set unreasonable goals, make choices about targets and strategies that make people around us nervous about the scope and extent to which things will change.

Someone, somewhere is working on an idea that will make our existing business obsolete, our products expensive or redundant and that will satisfy our customers more.

We need to work on destroying and rebuilding our business before they do.

And the skills we have in the organisation now are not the ones that will take us there.

We need to recruit for skills, not experience.

The capability that built our organisation is unlikely to be the same capability needed to build a new digitized one.

The kinds of people needed – developers, user experience designers, system architects – are likely to be in other fields and need to be recruited.

Most organisations will be better off in the long term with in-house capability because a digital transformation is a core strategic initiative.

Then, talent needs to be protected, perhaps in a Skunk Works.

Lockheed Martin’s Advanced Development Programs are referred to as the Skunk Works, a group given a high degree of autonomy and freed from bureaucracy, and told to get on with new projects.

It’s very hard to stick talent in the middle of an existing organisational structure and expect them to innovate.

The resistance from people used to business as usual is too much, and can slow everything down.

Nothing is sacred – challenge everything

When going through a transformation, every aspect of the business and how it works needs to be questioned.

Do certain processes have to be carried out? Are there things we can stop doing?

A formal way to this is a method called Final Cause Analysis (FCA).

We ask what is this for? over and over again – and focus on the essential elements we discover as a result.

We haven’t got a year – we need to move fast.

These days no one has 12-24 months to put a new system in place.

We’re talking weeks and months to getting working systems that we can test and refine based on customer feedback.

Lean and agile ways of working are taken for granted now.

There are more projects than we can do, so we need to prioritise based on value – follow the money

Our projects will help us increase revenue.

At the same time, and as importantly, they can help us cut costs.

We need to rank our projects based on contribution to the bottom line and then commit to a programme – putting money, resources and management in to get things done.

All of this effort and reinvention is focused on one thing – the customer.

Customers leave because they are unhappy – so successful digital organisations are obsessed with the customer and their experience (in a healthy way).

Digitization is not a choice – it’s just what we now have to do to stay in business.

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Why I sold my crypto holdings

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Like many other people, crypto currencies weren’t really something I looked at seriously until the staggering rise in their valuations in 2017.

In December and January, the price of Ethereum went from under $500 to over $1,250, more than doubling in two months.

The entire world got very excited.

Everyone seemed to be looking at these currencies and talking about buying it.

Was crypto something I should get into as well?

Buying a crypto currency is not like buying a stock or an index fund.

With a stock, we are taking an ownership stake in a company, with underlying cash flows and the possibility of growth and more income over time.

As an owner, we share in the growth (or not) of the business – and it makes sense to buy good businesses and hold them over time.

An index fund that covers a market is taking a position that an entire economy will grow and we will share in that.

For example, a S&P tracker will simply follow the performance of the largest companies, and they will probably be worth more in 20 years than now.

Cryptos are a pure trading play

Currencies don’t work like that – they have no intrinsic value.

They are worth what someone else is willing to pay for them in another currency.

That means we need to understand how buyers and sellers in that market work.

I needed a trading method

In many situations, we only need to be good at the buy side.

We’re going to buy and hold for the long term – and as an investment method that works quite well for things like buying stocks or making decisions on commodities like electricity and gas that we actually need for our homes and businesses.

In trading, however, we need to be good at the sell side as well.

More than good actually.

Lets say that we are 70% right at picking when to buy. That seems good, right?

If we are also 70% right at picking when to sell, then for any trade that involves a buy and sell, our probability of success is 0.7 x 0.7 = 49%.

That means we have a less than 50% chance of being right over time on both elements.

That’s where a system comes in, so my first step was to code one up to use for crypto currencies and I decided to apply point and figure charting (P&F).

A P&F system is designed for long term traders that want to understand how buyers and sellers are operating and make decisions on taking positions in that market.

The picture above is a snapshot of my P&F chart for Ethereum.

A position with actual money makes it more real

It’s easy to talk about what we would do, but to really get a feel for how we will act in a trading situation, it’s necessary to put down real money.

After the fall in value in February 2018, I entered the market with a very small position on the first reversal – marked as BUY on the chart.

I bought at around $725, and set a stop-loss at $650 – the red line.

The price went up a bit, down a bit, up a bit and then started to come off consistently during late February and into March.

When it crashed through my stop – I sold.

There isn’t much point having a trading system if you don’t stick to it

The point is that my sell wasn’t due to market conditions or timing or emotional decision making.

At the time I bought, I had a sell in mind, both for the upside and the downside.

This was a trading play, not an investment one – so when it went against me, the only rational thing to do was cut my losses and stop playing.

The next thing to answer is – when should I enter the market again and have another go?

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What is emergence and how can we make it happen?

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We’ve all seen a flock of birds wheeling and swooping as if it were a single, giant organism.

The same thing happens with shoals of fish, or even people trying to leave a train station at rush hour.

Why and how does this happen, and what does it mean for us?

The term emergence is used to describe complex phenomena or behaviour that emerges from the interaction of simpler elements – often in a way that can’t be predicted from the features of the simpler element.

We can simulate flocking behaviour by setting up a system that follows three rules:

  1. Don’t crowd neighbours
  2. Move in the average direction of where neighbours are moving
  3. Move in the average direction of where neighbours are

These three rules result in a swarm – see here for example.

In organisations, emergence can happen in two ways.

In a hierarchy, the rules are set by those in charge.

People are given jobs, roles and responsibilities. In most organisations now, they have latitude and discretion in how they do their roles but have rules to follow.

Take the flocking rules, for example, and recast them for a job role. This might say:

  1. Avoid doing the same work as someone else – create your own niche.
  2. Try and make sure what you do is aligned with the vision and mission of the organisation.
  3. Do work that feeds into and works with what others in the organisation are doing.

If company had a number of people who organised their work in line with these rules it’s very likely that they will do some very interesting things.

It’s that balance between individuals and the collective that creates the conditions for innovation and creativity to emerge.

It’s also why micromanagement doesn’t work.

We need freedom and control – too much of either results in very simple or chaotic behaviour, neither of which are useful.

The second way in which emergence happens is through markets

Take Ebay, for example.

By creating a platform where people can exchange things, they created a thriving ecosystem of buyers and sellers.

Products from bicycles to floor mats flow through the system, in bursts of transactions that spill out into the real world – triggering a flow of packages in white vans that then creates emergent behaviour in the flow of traffic.

On a macro-level, the most successful economies are those that let markets form – allowing people to freely exchange goods and services.

We are surrounded by emergence – and what it reminds us is that we cannot control everything.

The best stuff happens when find the space between simplicity and chaos.

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Do you have the skills needed for modern work?

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Hedge funds could look very different in a few years.

The Financial Times published an article about the rise of DIY algorithmic traders – people who develop automated investment strategies.

These people don’t work for hedge funds or banks on Wall Street.

Instead, they are mathematicians, progammers, physicists and data experts who are using their skills and cheap, powerful computers to tackel investing.

And this is happening everywhere we look.

Online sales are hyper-competitive, and the companies with an algorithmic edge can squeeze out more profits from their platforms.

Recommendation engines are key to keeping users interested, as algorithms work out personalised offers.

The energy business is fuelled by data – from meters recording generation output to those working out who has consumed it and what their bill should be.

In a world of abundant, cheap money, projects have to work on razor thin margins.

Getting the numbers wrong, over time, will mean that the project makes negative returns.

So, who is going to succeed in this new world?

Drew Conway came up with the Data Science Venn Diagram to explore the key skills needed in the world of Data Science – the field that will most likely underpin modern work.

In adapted form, the key is having three sets of skills.

Hacking skills are an entry requirement – being able to deal with and clean text and numeric data is part of every project.

Excel won’t hack it anymore – we’re going to have to use better tools to deal with more and messier data.

Then we need some maths and stats knowledge.

Knowing how to draw and interpret charts and understand the relationships between sets of numbers makes the difference between guessing and having a theory.

And a scientific approach is based on having hypotheses and running experiments.

Finally – many people think they can simply waltz into a new field and take it over.

Domain knowledge often makes the difference between success and failure in a field – it’s very hard for someone to build a tool to solve a problem that they have never experienced themselves.

That’s why we get lots of tools that look pretty, but end up doing little.

Curious people with good tools are what we need for modern work.