Cracking the Nut (Part 2) – Tackling a Messy Problem

In the last post, we used the model to analyze a constrained problem.  Now, we’ll take on something a lot more open ended.

We work for Slimy Inc, an upstart company in the hotly competitive market for slime.  There is a dominant player in the market – BigSludge – and we are trying to figure out how to compete with them.  The traditional slime market is focused on commercial uses, and we’ve been having a tough time competing with BigSludge because they are the market leader and far larger than we are.  But, we’ve created some innovative new slime for kids to play with – a radical development in the slime market.  We’re definitely going to be first to market, though we have heard that BigSludge has a team investigating toy slime.

There is a raging debate within Slimy, Inc about how to compete with BigSludge.   Some people want to continue going after them head-on in the large commercial market, relying on our superior products.  Others want to put all the company’s efforts into the new kid slime product line (but is there really a big enough market, and will it develop soon enough?).  Another faction is pushing for an acquisition – BigSludge knows about the cool new products we’ve come up with, and they’ve had a couple of discussions with our leadership team about it.  As usual, there are dozens of variations of these basic ideas, and they’ve gotten tangled up with each other.  Emotions are running high and discussions have been going around in a circle.

We’ve been asked to take on this problem, analyze it, and get the decision landed.  The stakes are high – the decision will probably determine the future of the company and perhaps its very survival.  So, it’s time to get methodical.

Defining the Problem

We start here, as always.  The problem may seem obvious, but often the various stakeholders in the conversation have quite different notions.  So we’ll start with a statement of the problem, and it’s incredibly important that we write it down.  There is a great quote by Leslie Lamport, a well known computer scientist: “we write things down in order to realize how poorly we understand them.”  In our case, we land on this: “what strategy should Slimy use to compete with BigSludge?

Looking at the other tools in the quadrant, there are a couple that will be helpful.

Scope – what is in and out of scope for our problem solving effort?  In this case, let’s put the decision to pursue acquisition out of scope – that’s an interesting analysis, but it’s pretty separate from figuring out how to compete.  Our project may provide good insight, though: if we can’t come up with any compelling way to compete, then we may have a lot more enthusiasm about acquisition.  But we’ll confine our scope to competing with BigSludge.  We also have to make sure we get the key people to agree on our proposed scope!

Assumptions, Axioms, and Principles – I think of these as the foundation of the analysis, and they are incredibly useful to work through and write down.  They are related notions, but I try thinking about all of them to see which are most relevant to the problem at hand.

  • Assumptions – something you believe to be true, but you are aware that you might be wrong.  “There is a large potential market for toy slime that BigSludge will not be able to address for at least three years”.  Once you’ve identified your assumptions, you can decide how risky they are, how much your potential wrongness could hurt you, and hence how much you need to validate them.
  • Axiom – “a self-evident truth that requires no proof” – you simply accept an axiom without debate or validation.  In our case, an axiom might be “commercial slime is an essential need for current customers and nothing will replace it in the market during the next ten years.”  Your axioms can save you a lot of time, since you don’t need to bother analyzing them.  But, they are obviously dangerous, because if you are wrong about them, you can choose a really bad path.  DEC had an axiom that PCs weren’t a threat because they were too small to do “real computing” – and DEC paid dearly for that axiom by going out of business and being acquired in a fire sale by one of those scorned PC makers.  So one of my main reasons to poke on axioms is that groups generally have a set of them that are accepted unconsciously and unquestioningly.  It is worth teasing them out and stating them explicitly to make sure that they really should be axioms, and are not just questionable assumptions masquerading as revealed truth.
  • Principles – a core belief that you are going to follow.  Ex: “we will produce no toxic by-products in our manufacturing processes” or “we will tell every customer the turnaround for any order within 2 days of accuracy.”  Principles help you be clear about what you believe in, and they guide you in terms of what solutions you are willing and able to consider.

Goals and Defining Success – often when people are disagreeing about a course of action, it is because they don’t agree on what “success” means.  Are we trying to achieve a good ROI on our product development investments? Establish ourselves as the share leader in a new market?  Win share from BigSludge in the current market?  Grow revenue or profit by a certain amount?  Survive as a company?

I have found it best for the goal of a project like this to be succinct and measurable.  I also prefer if the goal

  • Doesn’t pre-suppose a particular strategy.  In our case, if we define the goal to be “create a large new market for slime”, then it heavily influences the approaches we can use to get there.
  • Is defined positively, not negatively.  I much prefer “radically increase our revenue and profitability” vs. “take share from our competitor”.  The negative approach can lock you into a zero-sum mentality, rather than creatively looking for any way to achieve your true goal.  Presumably what you really want is to make massive amounts of revenue and profit, regardless of what your competition does.

For our purposes, we will use this: “a successful strategy will yield a set of quickly growing product lines in the market that have higher profitability than anything we currently sell.”

Constraints – we are always under many constraints and we need to understand how they limit our options.  They might involve resources (money or people), legal requirements, etc.

Solution criteria – just like we did in the last post, we will need to come up with a ranked set of criteria for evaluating our options.  This is often the hardest part of defining the problem; fierce disagreements are often disguised arguments about the criteria.  For example, we have one group that wants to tackle BigSludge head on in their core market, which is large and well established.  Another wants to dedicate the company’s resources to expanding into the new kid slime market.  What’s going on?

The real challenge is that there are competing (and valid) criteria to judge potential strategies.  In this case, they are:

  • Size of the target market – one group questions whether the kid market is real and large.  There is no question that the current market is large.
  • Growth of the target market – the current market is mature and is growing with GDP.  We don’t know of anything on the horizon that will change its trajectory (per our axiom above).  The future of the kid market is much harder to predict; if the company can really nail an offering, it could be a huge and high growth market.  Or, it could stay tiny because only the early kid adopters will ever bother playing with slime.
  • Riskiness of our market prediction – obviously one is not risky in terms of its existing, the other is high risk.  Also, BigSludge has that research group that might get a clue and develop a competing offering in the kid market – it’s unclear how long we can have it to ourselves.
  • Ability to capture target market – to get the traditional market, you have to find a way to compete with BigSludge and take share or expand the market somehow.  The company probably has a pretty good sense of how hard this will be and has some ability to execute on it, but it may be very difficult to succeed.  To create a whole new market, even if the demand is there, calls for specialized skills and new partner relationships and the like.  Does Slimy have what it takes to pull this off?

The disagreement between the two groups is a disagreement about the value to assign to each criteria and the relative priority of the criteria.  This is really key to understand.  Until you start digging at the real basis of the disagreement, you will often go around in circles and everybody will just get locked more deeply into their point of view.

The faction that wants to go after the traditional market believes that (a) the opportunity in the kid market is small and will stay small, (b) betting on that market is much too risky and will take a lot of energy to pursue, and (c) Slimy doesn’t have what it takes to create new markets.  The other group believes that (a) the kid market has huge potential, that (b) it will grow quickly, (c) Slimy is in a good position to take a risk for huge upside, and (d) the company can learn how to sell to a new audience.  They also (e) doubt whether Slimy can compete effectively with BigSludge in their core market.  So the two groups differ on:

  • The value to assign to a criteria: is the kid market going to be large and high growth or small?   Can Slimy really make headway in the traditional market?  These questions can’t be answered perfectly, of course, since you are predicting a future outcome.  But you can certainly get data to support the analysis.
  • The relative priority of the criteria.  One group is risk-averse, the other isn’t.  This is a useful debate – how much risk is the company willing to take on?  What kinds of risk?

The key thing you can do with an analysis like this is move the debate from an unproductive place – “We should go after the kid market!” “That’s crazy, we should go after the money, and that’s in the commercial market!” – to a much more useful discussion around the real issues – can we get facts to assign values to the criteria more confidently, and how should we prioritize competing values?  These are still very hard questions, and they still require us to make decisions in the face of uncertainty.  But they let us focus our energies on reducing the key points of uncertainty and on having debates that we can actually settle.

We’ve now got the foundation in place for our problem solving effort – we know the problem to solve and its scope, we know what our goal is, we have articulated our principles and assumptions, and we know how we’re going to evaluate possible courses of action.  Next, we’ll move on to other quadrants of the model.

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  1. [...] forward from part 1 and part 2, we’ve gotten the foundations laid down, so let’s keep powering [...]

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