Dealing with 'inevitabilities' in business strategy

Many of the threats and opportunities we identify on SWOT and PESTEL analyses are uncertain: things that might or might not happen or things that might happen one way or another. Others are simply trends which carry with them an air of inevitability.

For example, we don't know:

• if antibiotics will lose their effectiveness or if medicine will find an alternative approach,
• if the world will be able to reverse the effects of global warming and/or overpopulation, or
• whether the current political trend to the right will continue, etc.

However, we can be pretty sure that:

• products and services will continue to digitise,
• data will become increasingly important in the way the world functions,
• autonomous vehicles will eventually succeed human-driven vehicles, 
• people will be more inclined to rent assets which they had previously been required to own, and
• AI will take on ever more complex tasks previously thought to require a human to perform them.

Of course, the distinction between these categories of uncertain and inevitable changes can be blurred and depend on the lens through which you look at them. But in any strategic context and time-frame, it is usually possible to distinguish between the two.

How should a strategist handle these inevitables?

One swallow does not a summer make

Every inevitability will have its doubters: people who think it will not come to pass - as often as not, because they simply don't want it to happen.

But as strategists, we must deal with the world the way it is, not the way we wish it were.

Even the strongest trends seldom proceed in a straight line. There are invariably numerous setbacks and other surprises. The doubters will cease upon these as evidence that it will not happen. But, as Aristotle said:

One swallow does not a summer make.

There can be few recent examples as dramatic as the bursting of the dot.com bubble around the turn of the century. Many businesses went bust, and I am sure that small fortunes were lost. At the time many heralded this as proof that people wanted to continue to do business as they had before and that the threat of technology had been shown to be a hollow sham. However, as much of a setback as it was, the dot.com trend recovered and strengthened.

Part of the strategists' role is to see through these setbacks and other anomalies and remain focused on the underlying trend.

Not 'if' but 'when' and 'how'

Once we've established that something is more or less certain to happen, we can stop worrying about if it will happen, and start applying our minds to when and how it will happen.

The strategist should monitor such trends on an ongoing basis. Do recent events suggest that the trend is speeding up or slowing down, or likely to speed up or slow down? Are they evolving in a way which is slightly different to how they started out or where originally expected to play out?

For example, Amazon changed the business of book distribution (and many other businesses!) forever. But what fewer people had anticipated was how Amazon was able to use its new-found power in the publishing industry to launch the Kindle where so many other e-readers had failed before it.

Being alert to these subtle changes in a trend which seems otherwise inevitable could be a source of significant strategic advantage, especially where all of your competitors are also building their propositions around the same trend.

So, perhaps it is time to go back over your SWOT and PESTEL analyses, distinguish between those which are uncertainties and those which are inevitabilities, and ensure you have appropriate sense-and-respond strategies in place for each.

photo credit: Duncan Rawlinson - Duncan.co - @thelastminute Passage via photopin (license)

4 remedies for your innovation woes

Innovate or die!

These days everyone is either innovating or desperately wishing they were.

Don't get me wrong - innovation is a good thing. As customers' expectations evolve at an increasingly alarming pace, organisations that fail to innovate quickly get left behind.

However, most of the organisations I speak to don't feel they're innovating particularly well.

So, here are my 4 tips for better innovation:

1. Work from the outside in, not from the inside out

Start by describing an ideal world as experienced by the customer. Describe what they do, why they do it, and how they feel about it. If possible, talk a few customers through this and see if their eyes light up or if they start to drift off.

Avoid the temptation to start by describing how you could improve your existing products, services and processes, or how you could leverage some exciting new idea.

This helps to ensure that all of your innovations serve a genuine customer need. Only then should you consider whether you have, or could gain access to the technologies and capabilities required to deliver it. 

2. Don't try and boil the ocean

Once you have a clear picture of what you want to do, don't try and do it all in one go.

Instead, break it into the smallest chunks that you can that will still deliver some value to the customer.

This delivers customer value sooner, and with less risk and less cost. They also allow you to learn what is and isn't working, and to adjust your approach accordingly as you go.

Note that these small chunks are not the proverbial quick wins or low hanging fruit - because you've started from the outside in and broken your propositions down, they are integral components of your strategic transformation programme.

See also: Big business appears to favour big change

3. Measure twice, cut once

Determine measurable hypotheses and build your measurement systems into the solution right up front.

Test your hypotheses on a scientific basis - that is with control groups. If you don't have control groups, you will never have any way of knowing whether your innovation caused the observed improvement, or whether it was caused by some other factor.

Don't try to collect data after the fact. It is too tempting for people to try and collect data which proves the hypothesis.

And don't try to collect data manually. People get lazy and start to cut corners. People want to move on to the next idea, and you want them to be free to. Instead, build the measurement into the solution, so that the measurement data is generated as a by-product of the process.

See also: Management approaches to data, Getting the most out of KPIs, and How to measure success against strategic vision and objectives

4. Make sure you build a reverse gear

If your hypothesis fails - the innovation does not produce a measurable improvement, or worse still, produces a measurable decrease - then you need to be ready to reverse it out.

All too often organisations are unable (because it is technically too difficult) or unwilling (because they are too emotionally invested in it) to reverse failed innovations out. Generally, it takes a little more effort to build innovations which can easily be reversed out, but it is worth it, in the long run, to keep your innovation process honest.

Lots of pundits talk about the need to embrace failure. You can only do so, if you can reverse it out, and learn from it before trying again.

See also: Strategic Analysis: Old Mutual changes its replatforming partner

photo credit: wuestenigel Socket on the wall via photopin (license)

Of Operating Models, Business Models and Strategy

I was talking earlier this week about the operating model changes that would be required to deliver a particular strategy, and someone (quite rightly) challenged me to be more clear on what I meant by operating model, and how it relates to other ideas like business model.

Andrew Campbell is as good an authority on the subject as any. He teaches Business Models on the Ashridge Executive Education course and is also the author of the book Operating Model Canvas (which I would highly recommend).

Handily he has also written a short and accessible article on Business Models and Operating Models.

In it, he confirms the conventional summary of an Operating Model as People, Processes and Technology. (He goes on to define an alternative which may be technically better but somehow seems less accessible.)

He refers also to Alex Osterwalder's book Business Model Canvas, which I would also highly recommend.

I think you could probably summarise:

• Operating Model: How you operate.
• Business Model: What you offer to Who and how you make money from it.
• Strategy: Why this achieves your purpose.

See also:

• How to design a Target Operating Model (TOM)
• Introducing the Enhanced Business Model Canvas

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.