6 steps for using scenarios in strategic planning (info graphic)

I have been doing a lot of work with scenarios lately, and so I compiled an info graphic outlining the key steps to using them for strategic planning.

The 6 steps are:

1. Scenarios are plausible stories about how the future might unfold.
2. Use a PESTEL analysis to identify uncertainties in your future.
3. Build an Impact/Uncertainty matrix to identify scenario drivers.
4. Create a 2X2 matrix of the highest impact/highest uncertainty drivers.
5. Forecast your business plan within each scenario to identify problems and opportunities.
6. Evaluate your strategic options against each scenario for robustness.

I hope you enjoy the info graphic below. Please let me know what you think in the comments below the post.

The problem with FinTech

I think I have discovered the problem with FinTech.

The problem is that FinTech exists.

By that, I mean that it exists as a category. Because as soon as it exists as a category, it implies that there is an alternative approach.

Financial Services has been a technology business at least since I first got involved in it in the early 1990s. One of my first vacation jobs, as a student, was loading data tapes on those old 'reel-to-reel' drives in the basement of a large insurance company. My first full-time job was distributing software to be used by financial advisers. Almost every financial services job and engagement I've had since then has had technology in it to at least some extent.

I've heard financial services executives say "we don't want to become a technology company", as well as "you can't win the technology" game. Well, perhaps you have to become a technology company in the sense that, for example, Amazon is both a retailer and a technology company? And perhaps the only way to win is to win the technology game?

Clearly, there is a category of FinTech startups which may disrupt the incumbents, either as competitors or as suppliers, but are they not just financial services startups and innovators?

I think it is time we stopped talking about FinTech as a sub-sector, and about FinTech versus non-Fintech, and just started talking about good use and management of technology in financial services, and bad use and management of technology in financial services.

What do you think?

Rethinking Big Data and Personalisation

I recently came across the claim that Data has become the basis for competitive advantage. Like the author of that article, that claim got me wondering if it was true (spoiler: yes, I think it is) and if so, what we should do about it.

I think that to understand the import of this claim, we have to cast our minds way back into history.

In the agrarian age, the basis for competition was land. Feudal lords warred with each other to expand their territories and profited by taxing the local populations and forcing them into military service (in order to gain control of yet greater territories). In the industrial age, the basis for competition was resources. Nations and industrialists fought to control resources like gold, oil, coal and steel, and competed in terms of engineering ingenuity and patent protection.

But now in the information age, the basis for competition is information. Nations and businesses compete by gathering and processing information. Physical warfare and theft are being replaced by cyber-warfare, misinformation campaigns, hacking and identity theft. Of course, the agrarian, and industrial models continue in parallel in the background, but they are of relatively less importance as time goes by.

But what does this mean for businesses and competition?

Big Data and Machine Learning

The first implication is for big data and machine learning. In the early days of the information age, data described industrial concepts like stock units, accounts and transactions. These could be described in relatively simple data constructs, and the volume of data was relatively low. The explosion of new devices of greater variety (think of the Internet of Things) means we're now collecting much greater volumes of data describing a much greater variety of real-world phenomena.

This so-called 'big data' is not only more voluminous but also less structured. This is where machine learning comes in. Big data is too vast and complex for humans to analyse and understand, so we're turning to computer algorithms to do it for us. Knowledge and understanding are being externalised: as humans, we see the results of machine learning, but we don't necessarily understand the thinking behind it.

The combination of big data and machine learning allow us to develop rich pictures of people and other real-world phenomena.

(See also: More data usually beats better algorithms)

Personalisation

Personalisation, in the sense of user experience design, is being presented as something new. But in some senses it is anything but that: in the 'good old days' local craftspeople and traders serving local communities knew their customers, often personally. We lost that personal connection in business, when, in pursuit of the benefits of scale, we centralised customer engagement into call centres. Customers typically speak to a different agent each time they call, and the information available to the agent is very transactional. Things got even worse when, in pursuit of even greater benefits of scale, we moved everything online, and customer interactions were further simplified down to menu choices and button clicks.

Big data and machine learning offer an opportunity to re-introduce personalisation at scale. I am not suggesting we'll have computer systems that remember to ask me how my children are. But personalisation can be used to ensure the content, promotions and user experience I am exposed to is tailored to my specific requirements and circumstances.

A good way to explain personalisation is to offer examples of what it is not:

1. Every time I log on to my bank I presented with the same information about ringfencing, security and identity theft. It does not matter how frequently I see it, or what I do about it, the information is always the same.
2. My investment manager sends me a fortnightly newsletter full of insights which have nothing to do with my approach to investments. I consistently ignore them, but still, they keep coming.

As a result, I have become 'blind' to messages from my bank and investment manager - I simply don't bother to look at them. It is annoying for me, and a wasted opportunity for them.

Skills

Of course, big data and machine learning mean that organisations need employees and leaders with different skillsets. Instead of (or rather in addition to) engineers and accountants, organisations need data scientists and machine learning experts.

Of course, in these new fields, standards of competence are not yet clearly defined, and demand for skilled resources outstrips supply.

Impacts on Trust

The recent Facebook / Cambridge Analytica scandal has highlighted a number of problems in this new world:

1. Facebook allowed Cambridge Analytica to gain access to users' private data as a result of 'backdoors' in their algorithms. Facebook supposedly knew about these backdoors but had not prioritised fixing them.
2. Cambridge Analytica then tried to use that data for nefarious purposes. (I have heard quite a lot of doubt expressed as to whether they actually had any impact on the US presidential elections or the UK Brexit referendum.)
3. Facebook was not open and honest about the breach. It took an investigative journalist and a whistleblower to bring it out. Under the European GDPR, which comes into effect on 25 May 2018, Facebook would have been obliged to let every impacted user know, or face substantial fines. As it was Facebook broke customers' trust, but not the law.
4. Once people started to become aware of the issue, they started looking at exactly what data Facebook had stored about them, and they were surprised by just how voluminous and detailed it was. People have subsequently had similar revelations with Google. This is causing people to think more carefully about the value exchange in which they trade their personal data for free services.

My personal view is that people will continue to be happy to trade their data for services, but only if:

1. The data is being used in a way that they perceive to be of benefit to them. For many people, personalisation of advertising does not yet seem like a benefit, but that could change. And, of course, there are many other more direct benefits that could be delivered.
   AND
2. They trust the companies to look after their personal data and keep it safe.

The rise of the Personal Information Managers

A whole new category of services is arising in response to the challenges outlined above: the Personal Information Manager (PIM). A PIM acts as a single source of truth for individuals' data and gives them control over which other organisations can access their personal data, for what purposes, and over what time periods. They offer organisations GDPR compliance, and they offer customers greater control.

Port.IM is an example of a PIM.

PIM's are a relatively new and not yet completely established class of application, which could play an increasingly important role as people grapple with how they want to manage their personal information.

Conclusions

Many of the biggest and fastest growing businesses of recent time - Facebook, Google and Amazon, for example - largely on the basis of their ability to collect personal data and extract commercial advantages from it. More traditional organisations have, up until now, been able to continue to rely on the strength of their products and services to survive.

However, as more and more traditional competitors start to adopt these practices, those that don't risk being left behind.

Organisations should:

1. Take stock of the information they do and don't (but should) collect about customers and distributors.
2. Look for opportunities to experiment with big data, machine learning and personalisation to extract commercial value from data.
3. Ensure that the security of personal data is of paramount importance.
4. Work to ensure that users understand exactly what data organisations collect about them and why it is in their interest to allow them to do so.
5. Explore relationships with PIM providers, rather than trying to solve all of the problems themselves.

photo credit: Christoph Scholz IT-Sicherheit Schloss mit binary Hintergrund digital via photopin (license)

Interview for the University of Southern Indiana

Last Summer, I participated in a video interview with Sonia Garcia-Webb from the University of Southern Indiana, in preparation for their use of StratNavApp.com in their MBA strategy course.

One thing I learned from the experience is that I probably don't have a bright future in broadcast TV! The very far below eye-level camera angle certainly does not help! However, I've posted the video below, and then included a summary of the discussion points below that.



How did StratNavApp.com come about?

• I realise that I spent most of my working life helping clients to be more effective and/or efficient and that this often involved deploying technology to improve things. However, I realised I was not seeing the same improvements in the way strategy itself is done. We still rely on large Powerpoint decks and Word documents, which we email to each other. We waste a lot of time managing versions and coordinating updates.  I realised it was not a solution I could ever recommend to a client.
• I start thinking about what a better solution might be. Over time I started building components of that solution and using them in my own consulting work.
• Eventually, I realised I had a usable solution which I could package up and share with other people. That led to the launch of StratNavApp.
• The result is:
• A freemium SaaS solution which doesn't require a download and so is easy to use and manage.
• Based on a superset of StratML, the ISO standard for strategic and performance plans, giving it a robust foundation.

How does StratNavApp.com assist people to develop and execute strategies?

• I think most people start using StratNavApp thinking it is just a collection of useful templates encompassing strategy development and execution best practice.
• Whilst most people engaged in strategy are familiar with the underlying models, my experience is that many don't really understand how the models interact with each other to produce a coherent flow of logic leading to a well-formulated strategy.
• Because StratNavApp is built on top of an integrated project repository, it is able to make those links explicit and actionable.
• In fact, as much as StratNavApp can highlight the flow of logic, it can also highlight any gaps or misalignments in the logic. In my own consulting work it often helps me to identify where I have attempted to shortcut the process and then got it wrong, and then it helps me to fix it again.
• Lastly, one of the big criticisms of strategy is that it is usually an annual process, which results in a thick document which is immediately out date and sits gathering dust on a shelf until the next annual process. Because StratNavApp is an ongoing collaboration all the way from analysis to monitoring the results of delivering the strategy, it never becomes a 'dead document' and the built-in feedback mechanism makes strategy more of an ongoing process.

What do you think challenges clients most in developing and executing strategy?

• I think that the biggest challenge is not the strategy discipline itself, assuming the people you are working with are properly trained and educated.
• The first challenge is 'articulation' - a lot of what gets passed off as strategy is really high-level and vague. It reads well, but it lacks enough substance to be executable. Execution becomes like 'trying to nail jelly to the wall'. This is usually because a lack of confidence in the insights, or because people avoid conflict by falling back on 'artfully vague' wording which covers over the underlying disagreements. The way that the tools are strung together in StraNavApp makes it harder to do this.
• The second big challenge is 'stakeholder management' - strategy tends to be decided amongst senior executives who are usually very intelligent and headstrong but who have different worldviews having had different career experiences. Strategy involves change, and change inevitably creates winners and losers (or even just bigger winners and smaller winners). Winning and losing has real consequences for individuals in terms of prestige and pay. So whilst we think strategy is a rational process, it is clouded by power-political self-interest.

What advice do you give students learning about strategy?

• For me, a key driver is curiosity coupled with a 'disrespect' for boundaries.
• When you start your career you typically have a job with prescribed boundaries.
• Why I think I ended up in strategy is because I was always curious about how my role related to things outside of that boundary.
• I think as a strategist, you need to be a specialist in the discipline of strategy, but also have the curiosity of a generalist wanting to understand how all the other disciplines work together to achieve success. 

Many thanks to Sonia for recording the interview and allowing me to publish it.

The 4 books every strategist should read

I was recently asked if I offered training for a new strategist. I don't unfortunately (perhaps I should?). There is this blog, of course, and I also offer a free ebook.

I did get me thinking, however, about what 4 books I would recommend every new strategist should read. So here they are:

1. On Competition, by Michael Porter: Porter's work is often criticised, and many other authors claim to have improved on it, but let us not forget that Porter remains the benchmark they are trying to beat. His style is, unfortunately, not the most gripping, but the insights he offers are essential grounding for any strategist.
2. The Balanced Scorecard, by Robert Kaplan and David Norton: While Porter lays out the grounding for strategy analysis, Kaplan and Norton set the benchmark for articulating strategy in a way that eliminates ambiguity and ensures it will get delivered. Once you've read The Balanced Scorecard, consider also its sequel, The Strategy-Focused Organisation, by the same authors.
3. Business Model Generation, by Alex Osterwalder: Oswerwalder tackles the tricky problem of how to ensure your strategy gets delivered in a functioning and fit for purpose organisation. Business Model Generation is a practical text which is elegant in its simplicity. If you're looking for more after reading this, consider also Operating Model Canvas from Van Haren Publishing.
4. Good Strategy, Bad Strategy, by Richard Rumelt: For me, the most interesting aspect of Rumelt's book is how clearly he spells out the mistakes organisations typically make in developing and executing strategy, and the consequences of doing so. A strategist needs to know not only what they should do, but also how to avoid these common mistakes.

There, are, of course, many other good books on strategy. I have reviewed many of these elsewhere in this blog. A strategist should remain a permanent student, and read as widely as possible. However, if you just getting started, these four books should provide you with the grounding you need.

Please let me know, in the comments below, your thoughts on any of these books, or which other other books you think I should have included on this list.

An anatomy of Strategy

What are the key elements of a business strategy and how do they relate to each other?

The above chart maps these out, including the key elements of operations which strategy must direct.

The components of the strategy are:

• Mission: defines why the organisation exists; its purpose.
• Vision: defines what the world will look like when the organisation succeeds in its mission. See also: Strategic Vision: 3 tests
• Values: are what the organisation holds dear; they are important in choosing what the organisation will and won't do, and how it will or won't do it, in order to achieve its mission.
• Goals: describe in more detail what the business must achieve in order to achieve its vision. Goals are often described as defining the financial, customer, operational and learning and innovation perspectives of the vision. See also: Strategic goals versus operational objectives.
• Objectives: break the goals down into specific, measurable, achievable, realistic and time-bound (SMART) achievements.
• KPIs: define the specific measurements with which the objectives will be measured. See also: Six tips on how to pick the best KPIs for your strategy and Getting the most out of KPIs.
• Targets: define what must be achieved, expressed in terms of a KPI, within a defined time period.
• Actuals: define what is actually achieved with a period for a measure; if this is inconsistent with a target, then review and remediation may be required.
• Initiatives: define specific changes to be made to the business, and should have a clearly defined end state. See also 6 techniques and 5 tips for developing strategic options and How to tune and prune your portfolio of strategic initiatives.
• Actions: are the specific steps that need to be taken to achieve the end state defined by for initiative. They define exactly who needs to do what and by when and may also define what they need in order to do it, and who they are doing it for.

The components of the operations are:

• Processes: what the business does on an ongoing basis to transform the input it receives from its suppliers into the output required by its customers.
• People: who perform the processes.
• Structure: how the people are organised to do so.
• Skills: knowledge, experience and capabilities that the people require in order to do so.
• Capacity: the number of people required to do it.
• Technology: the systems and equipment required to perform the processes.

For more insight into strategic operations, see How to design a Target Operating Model (TOM).

All of these components and the relationships between them can be defined in StratNavApp.com, the online collaborative platform for business strategy development and execution.

Everybody Lies: The evolution of market research

I've only just started reading "Everybody Lies" by Seth Stephens-Davidowitz (see right), but already I am hooked.

Stephens-Davidowitz documents and evidences in page-turning style a view I have held for some years now:

1. we can now observe how people actually behave, especially when they don't think anyone is looking, in ways which were previously not possible, and
2. what we observe it is often very different from 
1. what they say they do or will do, and
2. how they behave when they think someone is looking.

I would guess that Stephens-Davidowitz borrowed the title of his book, whether knowingly or not, from the byline of the TV series "House M.D.", whose lead character says "It's a basic truth of the human condition that everybody lies. The only variable is about what."

Reading the book has given me pause to reflect on the evolution of market research. Without having actually done a comprehensive historical study of the subject, my personal experience suggests at least 3 waves of development.

Market Research 1.0

The first wave of market research consists of asking people for the views, preferences, intentions, wants and needs, etc. This can be quantitative, for example, in the form of a survey, or qualitative, for example, in the form of a focus group, etc.

The obvious problem with this is, of course, that people have many reasons to lie, and few reasons not to. Reasons to lie can be very simple, for example, wanting to appear intelligent, or virtuous, wanting to be liked or admired by the questioner. Often, people won't even be aware that they are lying. As humans, we are excellent post-rationalisers. Cognitive Dissonance Theory suggests that when faced with a question we can't or don't want to answer, in a situation where we'd like to, our brains simply fill in the blanks, making up a story, possibly without even being consciously aware of it. (Note, we're using the word 'lie' here throughout, even when subjects are doing it unintentionally and unknowingly.)

Another problem is that people find it difficult to answer questions about subjects outside of their existing frames of reference. For this reason, market research 1.0 is even less helpful when developing novel ideas. As Henry Ford apocryphally said: "If I had asked people what they wanted, they would have said a faster horse."  

Market Research 2.0

Market Research 2.0 attempts to build on Market Research 1.0 by showing customers examples of what future products or services might look like. Often, more than one version is shown, with subjects asked to interact with them, compare them and indicate preferences.

This can go a long way to alleviate subjects inability to imagine a different future, and if all options are attractive and presented positively, this will also reduce some of their incentive to lie.

Market Research 2.0 requires more work than Market Research 1.0 as it usually means that you first need to develop some ideas to test. If you're innovative in the development of those ideas, that helps. But the innovation is likely coming from the development of the ideas, rather than from the market research.

However, a number of examples illustrate the difficulties still inherent in the approach:

1. Subjects reportedly overwhelmingly rejected the idea of ever withdrawing cash from a hole in the wall, as opposed to from a bank teller, but today, 94% of UK adults use cash machines.
2. In market research, 68% of US customers said they liked the taste of New Coke, but 6 months after launch it was removed from the shelves, and the old formula relaunched. (Reference)
3. Research conducted between the announcement and launch of the iPhone found high demand in emerging economies like Mexico and India, but not in developed countries, stating: “There is no real need for a convergent product in the US, Germany and Japan”. (Reference)

As I write, I can think of at least two factors which might contribute to this problem, and I am sure there are countless more:

1. The Hawthorne Effect (also known as the Observer Effect) is named after experiments conducted from 1924-32 in which it was shown that subjects behaviour is altered by virtue of the fact that they know they are being observed.
2. Research subjects typically have no 'skin in the game'. For example, it is a lot easier to say you'd be happy to pay, say, £100 for an item than it is to forego the other enjoyments you'd have to give up in order to do so. This is probably exacerbated where they are positively incentivised to take part in the study.

Market Research 3.0

Market Research 3.0 observes how real prospects and customers behave with and use products and services in the normal course of their lives and when they don't think they are being watched.

As technology evolves it is increasingly possible to track how customers move through a store, what items they buy and how they engage with and dispose of those products.

This involves developing and launching a product before market testing it, but with increasing software content in products and services (think the Internet of Things) and advances in technologies such as 3D printing, it is become ever cheaper to develop and pilot smaller batches of products or to mass-customise products and services.

Sample Application

I use these techniques to great effect in the development of StratNavApp.com in three ways. It is important to stress, however, that all three techniques are based on machine statistical analysis and don't involve anyone ever looking at users' data or strategies, or attributing the results to any specific individuals or companies.

Here are some common ways we measure and

1. Web-site analytics: using even a simple (and free) tool like Google Analytics, it is possible to understand how users find the service, which parts of it they visit most frequently and in what order, how long they engage, and from where they leave. Using this insight, we can prioritise our development efforts to those areas and features users find most valuable. So, for example, we know that our SWOT analysis tool has been 24% more popular than our Strategy Canvas tool and 33% more popular than our Business Model Canvas tool (confirming our views on the continuing popularity of the SWOT).  
2. AB Testing: almost all new features are first introduced to a randomly selected subset of users (the "A" group") while the remaining users (the "B" group) continue to see the site unaltered. We can then measure whether the A group engages more positively (against our own defined Critical Success Factors) than the B group or not. If they do, then the feature is released to the remaining users, and if they do not, then the new feature is rolled back or adjusted and retested. Either way, the results are analysed to enhance our picture of how users use the service, and how we can further improve it. By way of a very simple example, StratNavApp.com's byline "Collaborative strategy development and execution" was the winner from among a number of AB Tested alternatives considered.
3. Content Analysis: StratNavApp.com provides a unique insight into how users develop and execute strategies. By way of a very simple example, we know that the word 'Market' is used almost twice as frequently as the word 'Customer' when describing strategic insights. We may not know why that is, and we may not know if it is a good thing or not, but we can certainly use it to enhance our product. We can analyse word counts, numbers and lengths, etc. of all elements used in StratNavApp.com with a view to optimising users' experiences of the tool.

Privacy and Ethics

This is not intended to be a post on privacy and ethics. However, it goes without saying that privacy and ethics have always been a key consideration in market research and it is right that there is ongoing debate and development of this subject as it evolves.

Conclusion

We've always known that market research is both invaluable and limited. As new technologies evolve we are able to increase the value it adds whilst simultaneously reducing its limitations. Those organisations that explore and utilise these new approaches will be at a distinct advantage over those that do not.