Me See – MECE – Do you?

What is MECE (pronounced, me see)? And how does it help consultants solve problems? And how could it help agencies win more pitches?

MECE stands for Mutually Exclusive and Collectively Exhaustive.

It is a method that is a key teaching in our new ‘Structured Thinking’ training course.  It is very popular (probably compulsory) in large Strategic Consultants –  that ad agencies and marketing agencies will come up against in pitches more and more in the years to come.

Structured Thinking helps a team within an agency look at the bigger issues a brand faces, not simply its advertising and marketing. And importantly look for evidence for the changes they recommend to a brand’s advertising and marketing  by providing the evidence backed insights that have used in solving a brief .

The advantage of this is that it ties the advertising solution in with the bigger issues that a brand faces, which is exactly what management consultants do. And why they compete so well with agencies.

In short MECE checks that once a problem is broken down into its component parts there is a) no over lap – mutually exclusive (ME) and b)  nothing has been missed – collectively exhaustive (CE).

A good example of MECE in the world of advertising is the way Byron Sharp (How Brands Grow) breaks down what a brand needs to do to grow. Here is only the first level of a logic tree, asking the question –  What does a brand need to do to grow?

 

 

 

There is no overlap here – they’re mutually exclusive and they are collectively exhaustive, what else is there?

Now you’d take it to the next level and start breaking down each component. The logic tree can get very deep, however this way you’ll only need to boil the sea once.  At each level we make sure they obey MECE.

Then we ask in what ways can we increase physical availability? Still obeying MECE – be available in more outlets that are bricks and mortar and or more availability on-line?

Then the next level, where on-line? What retail outlets?

Increasing mental availability (traditionally advertising’s, marketing’s and branding’s bread and butter)  needs to break down into its components obeying MECE – develop distinctive brand assets, develop below the line promotions, develop promotions above the line.

Note each level of MECE can be and / or statements. E.g. Increase just one component, two components or all of them. This is where the research comes in for each component, when we form a hypothesis. E,g We might want to test how distinctive our brand assets are – so asking the question do they needs to be changed at all? If we are to change them, how and provide the evidence for those changes. No guess-work – evidence.

An agency brief would be asking what does what does their specific brand need to do to grow.  However, you might even start with a different question altogether, like, what changes could we make to (insert brand name) advertising? Or it could be a relative question –  Why is the market leader’s advertising better than ours? Or what are the key reason’s people choose one brand over another? Whatever the question you decide upon,  MECE is one tool that will help you stay on track.

Eventually, as we break down a problem logically further and further say 5-6 levels deep (where it gets harder and harder to maintain MECE, especially CE) we’ll hit upon a thought, possible solution, that we can share with the agency team, which will form a hypothesis, that will need to be tested and so we will need to look for evidence. This evidence will either prove or disprove our hypothesis.

It is the CE part of MECE which leads to better creative solutions because as we move down the tree it becomes harder and harder to be collective exhaustive and so we become creative in our answers and it becomes more of a fun brain storm around a very tight component of the question we initially asked. This is where your insights / thoughts could be unique to how other agencies competing on a brief will see the problem and yours will be evidence based.

The advantage of working this way is that your biases can be torture tested, we all have a best guess at an answer to a question but there will often be biases present. So instead we can test our thinking and  provide some evidence to our prospects / clients. They can then follow our logic in our pitch and see that our thinking is not just thorough and crystal clear but hard to contest.

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THE GIANT RETURNS ON GETTING A BIT BETTER

 A guest blog from Steve Hacking CEO at Kardelen

THE GIANT RETURNS ON GETTING A BIT BETTER

Bad Ideas Designed To Stop People Thriving: #1, The Learning Curve

Think about developing a skill, and you think of a learning curve. Early on you improve quickly, then progress gets slower; and it’s soon hard to tell if you’re improving at all. At some stage you plateau and stop improving, just like your handwriting, driving and party dancing did a few decades ago. If you’re super conscientious you practice for 10,000 hours, making tiny improvements to get really good, but this seems to have a high price.  Here’s how that learning curve looks

Chart 1 - Learning Curve w Title

If we think like this, it’s no wonder that we eventually stop making an effort to get better and unconsciously start coasting, then maybe even start looking around for a new skill.

The good news is that this limiting picture of learning curves is, in the vernacular, “a crock of shit”:

  1. Learning curves for individuals don’t need to look like this – there’s nothing inevitable about plateauing
  2. The vertical axis measures the wrong thing, and fools us into thinking the wrong way

I’ll just look at this second issue here, and propose a better measure that will make you think differently and cheer you up.

What if we looked At reward instead of ability?

Let’s analyse the data lover’s paradise of baseball, and the abilities of folk who are all the way along baseball’s learning curve – starting pitchers for major league teams.

Here’s the pitching ability of the top 55 ranked pitchers using one common measure: walks and hits per innings pitched, or WHIP.  Lower is better.

Chart 2 - Pitching Ability w Title

I’ve only shown one season, so variability is higher than for career stats.  But if you look at the trend line, you can see the difference in ability between pitcher number 55, Wei-Yen Chen, and pitcher number 1, Clayton Kershaw.  It’s tiny: less than 1 hit or walk conceded in every 10 innings pitched.  To go up 1 place in these rankings, you need to improve your ability by about 0.2%.

Let’s look again at our pitchers, but instead of comparing ability, we’ll compare how much they get paid.

Chart 3 - MLB Salary w Title

Don’t forget that these guys are on the far right diminishing returns part of the learning curve.  The difference in their abilities is tiny, but the reward for tiny improvement is huge. In fact the further along the learning curve you go, the more difference a tiny improvement makes.  Here’s a comparison of salaries that includes people from the full professional spectrum of pitching ability.

Chart 4 - All Baseball Salary w Title

Choose any field that rewards ability – from sales and project management to writing novels and betting on football matches – and you’ll find the same pattern.  Change your reward from money to whatever is important to you: glory, gold medals, cats rescued or souls saved from damnation, and again the pattern is the same.  The better you get, the bigger your reward for getting even better.  Looking only at increase in ability leads us to draw the wrong conclusions because it’s the rewards that really matter.  When we look at rewards, we end up seeing our learning curve in a new way:

Chart 5 - Reward Curve

What to Practise?

Moving from left to right on this chart is about smart, conscious practice. That leads us to 2 questions: what to practise and how to practise?

My takeaway from this article is about what to practise: take the few fundamental skills needed to be good at your job and practise those a lot, no matter how good you already are and no matter how little you think you can improve. That seems to be the best way to reap some giant returns on getting a bit better.

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YOU THINK A CAUSED B, BUT HOW CAN YOU TELL?

(This is a guest blog from Steve Hacking CEO at Kardelen Training)

Here’s an obvious improvement, and a clear cause of that improvement.

The stimulus of the espresso really caused an improvement in the javelin throw. More evidence for the performance enhancing benefits of caffeine? Not so fast.

What Caused What?

If we see one thing happen (A) and another thing happen (B) we can conclude at least 5 different things:

1. A caused B. My singing (A) caused my singing teacher to wince (B).

2. Something else caused B. Here’s an analysis of UK fertility rates (B) before and after Games of Thrones (A) was released on HBO in 2011. Anyone arguing that GoT caused a decrease in birth rates, Khaleesi?

3. Something else caused both A and B. If shark attacks (B) go up at the same time as ice cream sales (A) on US and Australian beaches do we conclude that those ice cream sellers are tempting in the sharks, or maybe that hot days cause more people to go the beach and in the sea?

4. The change in B was random. Here’s one time we can feel sorry for football managers. Take a look at this excellent German research on football teams’ results (B) when they had a bad run of form and changed manager (A). Looks like changing the manager was a good idea.

Now have a look at the teams that had similar dips in form but didn’t change manager. Do you still think changing the manager made a difference? Or that teams just revert to their typical performances following a bit of bad luck?

5. B caused A. Did our change in manager (A) cause a change in performance (B), or did a run of bad performances (B) cause a change in manager (B)? Does veganism (A) cause better health (B) or are health conscious people (B) more likely to become vegan (A)?

How to Tell if You Have Your a Cause

In most, complicated, real life circumstances the way to know you have your cause is to find yourself 2 almost identical situations: in 1 the cause being present; in the other absent.
We can see if these 2 situations already happened in the real world, just like our German football teams that did change manager after a bad run compared to those that didn’t.
We can also create these 2 situations by doing our own trial, to see what happens in when our speculated cause is present versus when it’s absent. Here again is my experiment of throwing the javelin, this time showing before and after a break that didn’t involve drinking espresso.

Looks like the espresso isn’t the cause of the improvement after all. Maybe it’s just that a good warm up and practice causes better performance.

Even if you think you have a cause, here’s a few other tests that’ll raise your confidence that you really do:

  • Can the effect be obviously explained by anything else? Are ulcers obviously caused by stress, or could it be something else, say, H Pylori?
  • Does the effect happen every time the cause is applied? Does hiring Pep Guardiola cause your football team to tiki-taka its way to the Championship? Yes, well except when it doesn’t.
  • Is there a low chance of the cause effect being explained by randomness? Run lots of
    experiments on small enough samples and you’ll find lots of fascinating counter-intuitive cause effect relationships that no one will ever replicate.
  • Are our sample groups similar? If we only give training to super stars on the promotion fast track,we can’t compare its effect against a sample of regular folk who aren’t on the fast track.
  • Is there a chance that the participant is wittingly or unwittingly fixing the outcome? Would you trust the findings of a sports drink company about the big race time improvements that come from drinking its product?

We will all sometimes jump to conclude that our management intervention caused our temporarily underperforming branch to improve, that people are successful because they say “no” more often and not the other way around, or that our team lost because we weren’t wearing our lucky jumper.  If it’s important, that’s the time it’s worth reflecting on whether we really do know what’s a cause and what, well, isn’t. If we don’t, we’ll be drinking too much espresso and not doing enough javelin practice.

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