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Case Studies: Milk-Shake

Case Studies: Milk-Shake

| On 22, Feb 2018

Darrell Mann

I read the latest Clayton Christensen book, Competing Against Luck, last month. In the fractured, fractious world of innovation, Christensen is one of the few ‘must-read’s. Even though an awful lot of his output is a re-hash of old ideas, he at least tells the story well. Competing Against Luck is an attempt to reclaim the ‘jobs to be done’ space. Stop selling products, is Christensen’s sage advice, and start recognizing that your job as a provider is to help your customers to get their own jobs done. Somewhat depressingly, the book contains yet another re-telling of the milk-shake story. It’s a great story, I grant, but there are limits. And telling the same story for over a decade now probably means the limit has been hit for all but his biggest fans. My problem with the story, now I’m on my umpteenth re-reading is that beyond the initial insight, it doesn’t deliver anything that’s actually use-able. I can see that it offers up an initially counter-intuitive recognition and insight that the humble milk-shake can be hired by customers for a number of reasons. But as far as I can tell that insight has never been turned into an actual innovation by any of the fast-food restaurants on the planet. And more importantly gaining the insight cost an awful lot of Market Research budget that, from a TRIZ perspective, was utterly unnecessary. It felt like time to re-tell the story to see how a) the key insight could have been revealed with zero budget, and b) how the insight could and should have been translated into an actual innovation.

First up, for those that might not know the Christensen ‘jobs-to-be-done’ version of the story, here is a precis:

It starts with a fast-food restaurant chain that wanted to improve its milkshake sales. The company started by segmenting its market both by product (milkshakes) and by demographics (a marketer’s profile of a typical milkshake drinker). Next, the marketing department asked people who fit the demographic to list the characteristics of an ideal milkshake (thick, thin, chunky, smooth, fruity, chocolate-y, etc.). The would-be customers answered as honestly as they could, and the company responded to the feedback. But alas, milkshake sales did not improve.

The company then enlisted the help of one of Christensen’s fellow researchers, who approached the situation by trying to deduce the “job” that customers were “hiring” a milkshake to do. First, he spent a full day in one of the chain’s restaurants, carefully documenting who was buying milkshakes, when they bought them, and whether they drank them on the premises. He discovered that 40 percent of the milkshakes were purchased first thing in the morning, by commuters who ordered them to go.

The next morning, he returned to the restaurant and interviewed customers who left with milkshake in hand, asking them what job they had hired the milkshake to do. Christensen details the findings in a recent teaching note, “Integrating Around the Job to be Done” (see his website if you wish to read it.)

“Most of them, it turned out, bought [the milkshake] to do a similar job,” he writes. “They faced a long, boring commute and needed something to keep that extra hand busy and to make the commute more interesting. They weren’t yet hungry, but knew that they’d be hungry by 10 a.m.; they wanted to consume something now that would stave off hunger until noon. And they faced constraints: They were in a hurry, they were wearing work clothes, and they had (at most) one free hand.”

The milkshake was hired in lieu of a bagel or doughnut because it was relatively tidy and appetite-quenching, and because trying to suck a thick liquid through a thin straw gave customers something to do with their boring commute. Understanding the job to be done, the company could then respond by creating a morning milkshake that was even thicker (to last through a long commute) and more interesting (with chunks of fruit) than its predecessor. The chain could also respond to a separate job that customers needed milkshakes to do: serve as a special treat for young children—without making the parents wait a half hour as the children tried to work the milkshake through a straw. In that case, a different, thinner milkshake was in order.

End of story. No evidence that the fast food restaurant in question had any idea what to do with the different-jobs ‘insight’.

Here’s what TRIZ/SI would have had us do. In our version, it would quite likely start with an analysis of Ideal Final Results. Figure 1 illustrates the most comprehensive means of connecting different stakeholder ‘ideals’ for the main attributes of a milk-shake:

Figure 1: (Partial) IFR Attribute Analysis For A Milkshake

The key with these kinds of analysis is to put yourself in the place of extreme customers. No need to actually go and talk to them. No need, either, to think directly about the jobs they are trying to get done. By forcing yourself to think about each different attribute and to think about extremes of that attribute it automatically forces you to think about the different contexts each stakeholder might find themselves in. So, to take Christensen’s famous viscosity example, the moment you force yourself to think about extremes of viscosity that the customer might want you immediately realise there is a range. In other words there is no such thing as ‘the’ ideal viscosity: sometimes the customer wants high and sometimes they don’t. If we’re really then looking for extremes we might give ourselves permission to contemplate that, at one end of the spectrum – when we’re trying to finish the shake as quickly as possible for example – we don’t want any viscosity, and at the other, we want the viscosity to be high enough that we don’t lose any shake if we accidently spill it.

Notice here that by forcing ourselves to think about extremes of attributes the ‘jobs to be done’ of the various different stakeholders are automatically revealed: the customer is trying to get the job of ‘not spilling in the car’ done; the restaurant is trying to get the ‘don’t throw any away because it’s past its shelf-life’ job done. And so on. ‘Getting the job done’ is one of TRIZ’s pillars – ‘function’. It’s a vital consideration in any innovation story, and it comes automatically once you allow yourself to ask the right (‘what does ideal look like?’) questions.

Christensen et al get this. What they don’t get – tragically – is the contradiction part. There are only two ways to innovate – add a new job or attribute, or solve a contradiction – but by far and away the most reliable and common of the two options is the latter. 85+% of innovations come from contradiction solving.

While Christensen’s expensive researcher interviewing morning and afternoon customers might have successfully identified the fact that there were different jobs the customer was trying to get done, they failed to see the real innovation opportunity. And that was that the viscosity should be high AND low. There is a contradiction to be solved. Solve that, and you’ve just made all of those different jobs the customer might be hiring the shake to do become easier.

The reason there’s no ‘aha’ innovation moment at the end of the Christensen version of the story is that while they might have revealed an interesting insight, they weren’t able to offer any solution to the restaurant. Different customers have different jobs they’re trying to get done, so what?

TRIZ/SI on the other hand, having revealed an interesting contradiction, would have suggested the innovator to draw something like this:

Figure 2: Contradiction Map For Milkshake Viscosity Contradiction 

And then, having drawn this picture, the next swift step would involve exploring how others might have already solved the problem. Inventive Principles 3, 10 and 15 for example. Any of which ought to swiftly point us in the direction of several others – already within the food and beverage sector to boot – that have already found very simple ways to alter the viscosity of the thing they’re trying to dispense. Altering the ratio of frozen and fluid constituents, for example, or, perhaps the simplest to adopt in the fast-food restaurant, a simple variable on the nozzle of the milkshake dispenser that affects the viscosity at the point of dispensing – as can be seen on certain draft beer pumps (check out patent WO 2007084258 for example). Or a combination of the two.

Figure 3: Variable Throttle Beer Tap

Solve one contradiction and there’s always another one, of course. Maybe asking the customer ‘how thick’ they want their shake is too complicated? In which case, from the TRIZ/SI perspective, we simply go around the Contradiction solving loop one more time. Maybe by creating a simple low-high or 1-5 viscosity scale for customers? Or by letting customers dispense their own? Let them decide for themselves what job they’re trying to get done.

Solve one contradiction and make a good solution; solve the second contradiction and make it great. Maybe that’s the real ‘job to be done’?